In 2021, Barcelona-based delivery platform Glovo was faced with a law that threatened its business model. Spain had passed the Rider Law, which introduced a presumption of employment when a digital platform organizes, directs, or controls couriers’ work, including through algorithmic management. Glovo came up with what seemed like a clever fix to avoid having to classify its couriers as employees: emphasizing courier autonomy by redesigning its app so they could log in freely, reject orders without explicit penalties, and choose a daily rate multiplier.

Three years later, however, it was clear that the fix had failed. Glovo and its parent, Delivery Hero, announced that they would hire roughly 15,000 couriers as employees in Spain, incurring a significant financial hit. The workaround had simply delayed the reclassification the company had sought to prevent. During this period, courier protests over lower earnings, along with mounting employment-related penalties and a criminal case against Glovo, increased its exposure.

Glovo reached for a common leadership response to unwanted regulation: minimal compliance. This involves making the narrowest changes a company believes will satisfy a rule while preserving how the business operates. But as algorithms become embedded in how companies deliver services and manage workers, that response is becoming a trap.

Leaders often default to workarounds or temporary fixes because they preserve the existing business model while avoiding a costly redesign. But this leads companies into what we call the algorithmic compliance trap. As Glovo’s case demonstrates, in algorithmic businesses, such fixes expand the audit surface, increase internal complexity, and invite scrutiny that can push the company toward the very redesign it was trying to avoid.

That said, not every regulatory change or instance of enforcement calls for a complete redesign. The hard question for senior leaders is when simple fixes are sufficient or a more significant rethinking of the business model is warranted.

We have developed a practical diagnostic for making that call. Drawing on five cases of algorithmic businesses facing major regulatory challenges in Europe — Glovo, Deliveroo, Airbnb, Uber, and Meta — we identified four signals that can help leaders judge whether workarounds are likely to stabilize a business model or accelerate a costly redesign.

Algorithmic Minimal Compliance: What It Is and Why Leaders Are Drawn to It

When regulation arrives, every business faces the same basic choice: absorb the cost of full compliance or find a way to meet the formal requirements while preserving the existing model. Historically, compliance could be layered onto the organization through policies, training programs, and designated controls while keeping core operations largely intact.

For algorithmic businesses, that separation is harder to maintain. Compliance often has to be built into the product itself: into decision logic, ranking systems, access gates, pricing rules, and automated enforcement. Changes in one part of the system can affect outcomes elsewhere and must continue to work at scale as the product evolves. That can make even genuine, good-faith compliance expensive and disruptive, and it creates a strong pull toward minimal compliance: modifying code, contracts, and workflows just enough to satisfy the formal requirements of a rule while preserving the underlying business model and what the system optimizes.

Algorithmic minimal compliance tends to appear in a few recurring forms:

• Contract terms designed to pass a specific legal test.
• Interface redesigns that signal autonomy while the platform continues to coordinate outcomes through incentives and information.
• Data collection and reporting tools built for recurring obligations.
• “Human review” steps that satisfy oversight requirements without shifting real decision authority.
• Compliance programs that produce reports and process artifacts while the system continues to evolve.

None of these moves is necessarily made in bad faith. When rules are new and enforcement standards are still taking shape, a workaround can keep a business operating while leaders learn what regulators will actually test for.

Algorithms make these workarounds harder to contain for two reasons. First, code changes, contract amendments, and workflow redesigns leave auditable traces that a revised policy memo does not. A workaround intended to satisfy a formal requirement becomes a record and, in adversarial settings, may serve as evidence. Second, workarounds in complex, interdependent systems rarely stay contained. A change in one part of the logic can have ripple effects elsewhere, creating new inconsistencies and new exposures. What begins as a targeted fix can expand the audit surface faster than it closes it.

Four Red Flags That Suggest Minimal Compliance May No Longer Hold

Across the five cases, four signals help explain when minimal compliance remained viable and when it began to compound the problem.

1. Tightening legal clarity. Minimal compliance thrives in gray zones. As laws, rulings, and guidance become more specific, loopholes close and the design space for a workaround shrinks.

2. Increasingly visible harm. When social costs become visible to workers, users, communities, journalists, or public officials, regulators become less willing to accept fixes that change the optics without changing outcomes.

3. Intensifying regulatory scrutiny. Regulators often allow room for experimentation while new rules take shape. As they shift from guidance to information requests, investigations, coordinated enforcement, formal proceedings, and demands for proof, the room to rely on a favorable interpretation narrows quickly.

4. Increasing internal complexity. A workaround may begin as a narrow change. But when compliance touches multiple models, markets, and workflows, fixes begin to cascade. Each adjustment creates new inconsistencies, requiring further fixes and expanding the company’s exposure.

These signals reinforce one another. Tightening legal clarity, visible harm, and intensifying scrutiny raise the bar for acceptable compliance, while growing internal complexity makes that bar harder to meet through additional fixes. As the gap widens, the workaround begins to shape the product road map instead of protecting it, and leaders lose control over the timing and scope of redesign.

Consider these rules of thumb when determining your organization’s next move:

• Zero or one red flag: A workaround may hold. Monitor and reassess as it meets operating reality.
• Two red flags: The window is closing. Start redesign planning.
• Three red flags: Escalation is likely. Fund a durable redesign path now.
• Four red flags: Forced redesign is likely. Treat further workarounds as a cost multiplier and move to redesign.

Now let’s consider how those red flags played out across five cases.

Deliveroo and Glovo: Similar Services, Different Tests

U.K.-based delivery platform Deliveroo provides the clearest contrast to Glovo because, despite being in a similar business, its workaround held whereas Glovo ultimately abandoned its contractor model in Spain.

Deliveroo: A Narrow Fix That Held

In 2016, the Independent Workers’ Union of Great Britain petitioned the U.K.’s Central Arbitration Committee (CAC) for collective bargaining recognition on behalf of Deliveroo couriers. Under the statutory definition applied by the CAC, the dispute turned on “personal service”: couriers could qualify for union recognition only if they were required to perform deliveries themselves.

Shortly before the CAC hearing, Deliveroo introduced new contracts granting couriers a broad right to send substitutes and did not tightly police its use. The CAC examined the terms and actual practice, treated the right as genuine, and rejected the union’s claim. The U.K. Supreme Court later held that the couriers were not in an “employment relationship” for Article 11 trade union rights in that specific context. The decision was limited to the claim before it; it did not settle every employment-status question.

Deliveroo’s minimal-compliance response remained stable because all four red flags remained relatively weak.

• Legal clarity: It was a narrow legal test focused on “personal service,” which Deliveroo could satisfy through a genuine substitution right.
• Harm visibility: The dispute was technical and did not trigger broad public salience.
• Regulatory scrutiny: The CAC accepted the contractual boundary as dispositive for this specific claim.
• Internal complexity: The fix remained contained and did not require extensive reconfiguration of the platform’s core algorithms.

Glovo: When the Red Flags Stack and the Workaround Touches Core Economics

Glovo faced a broader legal question: whether couriers coordinated through its software were genuinely independent. Spain’s Rider Law was applied to examine whether the platform organized, directed, or controlled couriers’ work, including indirectly or implicitly through algorithmic management.

Glovo redesigned several parts of its app to make courier independence more visible while retaining its contractor model. Couriers could log in freely, reject orders without explicit penalties, choose a daily rate multiplier, and exercise wider substitution rights. Performance rankings were removed, and monitoring was reduced. With those levers loosened, the platform relied more heavily on pay parameters, bonuses, and information flows to balance supply and demand.

Glovo’s minimal-compliance response eventually encountered all four red flags.

• Legal clarity: The Rider Law left little room for interpretive maneuvering because it reached mechanisms at the center of Glovo’s operating model. Unlike the CAC’s narrow personal-service test, which Deliveroo could address through one contractual change, the law required Glovo to demonstrate genuine independence across the entire operating relationship. Its product design therefore became evidence of organization, direction, and control.
• Harm visibility: Free log-ins shifted supply-balancing risk to couriers through longer waiting times and greater earnings volatility. The rate multiplier also exposed couriers to price competition. Reports of lower earnings and courier protests made those effects visible.
• Regulatory scrutiny: Authorities found that the platform still organized, directed, and controlled couriers despite the new autonomy features. They treated the redesigned model as continued false self-employment rather than a good-faith adaptation.
• Internal complexity: Glovo loosened access and performance controls but still had to match volatile supply and demand. Service problems prompted further adjustments to pay parameters, bonuses, and incentives. Each correction made Glovo’s continuing authority over courier pay and access easier to trace.

As these signals reinforced one another, Spain’s Labor Inspectorate issued its first sanction against Glovo’s post-Rider Law model, and prosecutors opened a criminal investigation into whether the company had continued to deny couriers employment rights. In December 2024 — one day before CEO Oscar Pierre was due to testify in the criminal case — Glovo announced that it would move to an employment model in Spain. The shift would cover roughly 15,000 couriers, and Delivery Hero projected a 100 million euro (about $113 million) impact on Glovo’s adjusted EBITDA business in Spain for 2025.

The diagnostic may have been able to separate the two cases’ paths before the outcomes were known. Deliveroo remained in the zero or one range: Its change was narrow, genuine, and unlikely to spread through the operating system. Glovo entered implementation with two red flags because the law reached core coordination mechanisms and the response required changes across several parts of the platform. Visible harm and intensifying scrutiny then raised the count to four, well before the employment model announcement.

Leaders do not need perfect foresight. They need to count the warning signs early and count them again as their workaround meets operating reality.

Three Design Choices That Make Compliance More Durable

The Deliveroo-Glovo comparison shows why a rule’s reach and a response’s containability matter most. Three shorter cases show what durable compliance requires under different forms of digital regulation: building recurring obligations into the product, giving human reviewers real authority, and making performance continuously verifiable.

Airbnb: Compliance That Becomes Product Architecture

Some obligations are easier to contain because they are specific, recurring, and separable from core marketplace decisions. The European Union’s Directive on Administrative Cooperation in Taxation (DAC7), in effect since Jan. 1, 2023, requires platforms to collect and verify taxpayer information and report host and transaction data annually. The EU’s short-term rental data regulation, in effect since May 20, 2026, adds recurring registration number and activity data obligations where national registration systems apply.

Airbnb’s DAC7 process turns the obligation into a workflow: Collect taxpayer information, and notify hosts when data is missing. If a host still does not provide the information after being notified, Airbnb freezes payouts until the host complies. The newer regulation extends the pattern: Display and check registration numbers, remove listings when ordered to by authorities, and transmit activity data monthly. Because these controls apply to modular functions — identity verification, listing eligibility, payouts, and reporting — rather than core marketplace logic such as ranking, pricing, and allocation, they limit internal complexity and the audit surface.

Specific, recurring duties can become reusable product capabilities when the requirements are stable enough to be standardized across markets. Airbnb applies the same design logic to the newer short-term rental data regulation, although it is too early to judge the outcome. When a rule reaches allocation, pricing, evaluation, or control, reusable infrastructure can support compliance but cannot resolve the underlying operating issue.

Uber: When ‘Human Oversight’ Is Treated as Symbolic

Uber’s GDPR case shows why a human-review step cannot serve as a procedural workaround when the law tests whether oversight is real. Under the General Data Protection Regulation, people generally have the right not to be subject to decisions based solely on automated processing when those decisions produce legal or similarly significant effects, such as permanently losing access to work through a platform. Four drivers from the U.K. and Portugal challenged permanent account deactivations for suspected fraud. Uber argued that members of its operational risk team had manually reviewed each case. The Amsterdam District Court initially accepted that account, but the drivers appealed.

In 2023, the Amsterdam Court of Appeal reached different conclusions for the four drivers. For three, Uber had not shown how reviewers influenced the decision, what information they considered, or whether they had the competence and authority to change the outcome. The court described their intervention as “not much more than a purely symbolic act.” For the fourth, a personal interview before deactivation was sufficient to establish meaningful human involvement.

Uber did not establish meaningful human involvement for three drivers, and the court ordered it to provide information about the logic, significance, and consequences of the decisions within one month, subject to a daily penalty of 4,000 euros ($4,675) for noncompliance.”

In August 2026, the Dutch data protection authority fined Uber nearly 825 million euros ($951 million), finding that it had made fully automated decisions to deactivate drivers and had not adequately informed them. Uber appealed, and the authority said that the violations had stopped.

Meta: When Compliance Must Keep Working

Meta’s experience under the EU’s Digital Services Act (DSA) illustrates why launching a compliance tool may be insufficient when regulation depends on ongoing external scrutiny. The DSA requires very large platforms to provide eligible researchers with access to public data and to address systemic risks to civic discourse and elections. Meta launched its Content Library and API in late 2023 and subsequently announced that CrowdTangle — which enabled real-time data monitoring by researchers, journalists, and civil society groups — would close in August 2024. Access to its replacement required an application and was initially limited to researchers from qualifying academic and nonprofit institutions. The question was not whether Meta had created a replacement but whether it provided adequate access and real-time functionality.

In April 2024, the European Commission opened formal proceedings against Meta after raising concerns that the company planned to close CrowdTangle without an adequate replacement for real-time civic discourse and election monitoring. Researcher access was one part of a broader investigation. Meta added functionality to the Content Library and API but closed CrowdTangle as planned. In October 2025, the commission preliminarily found that Meta’s access procedures were burdensome and that the data made available to researchers was often incomplete or unreliable. The finding remained preliminary, and the proceeding was still open as of August 2026.

The test was practical: Could the replacement provide adequate access and functionality as Meta’s products and policies changed? Launching a tool establishes a process; it does not prove that the process works. When regulation requires ongoing external scrutiny, durable compliance depends on maintaining timely access to usable, reliable data as the product evolves.

What to Do Next: Five Operating Moves That Preserve Management’s Options

The algorithmic compliance trap’s greatest cost is the loss of choice. The diagnostic tells leaders when to escalate; the five moves below can be taken to change how the company evaluates, governs, and implements compliance before the outcome is known.

1. Start with what the rule actually tests. Translate the legal requirement into a concrete operating question. Deliveroo faced a discrete personal-service test; for Glovo, Spain’s broader test implicated nearly every mechanism the platform used to coordinate delivery. That breadth was an early warning. Map the requirement across contracts, algorithms, incentives, interfaces, workflows, and decision rights. Then ask what would have to change under strict, consistent enforcement. If compliance would weaken a core source of control or economic advantage, scope and fund a redesign alongside any limited response.

2. Stress-test whether the change will stay contained. Before scaling, use pilots and scenario tests to assess the combined effects on allocation, pricing, earnings, service quality, fraud, appeals, and adjacent workflows. At Glovo, changes to courier log-ins, rate multipliers, and rejection rights affected several outcomes and prompted further interventions. Treat new bonuses, exceptions, or controls as evidence that the response is spreading. Escalate it as an operating model decision before local corrections become embedded.

3. Set a stopping rule before approving the workaround. Define in advance what will trigger a redesign: a formal proceeding, a limit on financial exposure, sustained deterioration in pay or service, rising complaints, or repeated compensating interventions. Glovo’s successive interventions show why monitoring is not enough unless the threshold is set in advance. Each correction can look cheaper than redesign while cumulative exposure grows. Set the trigger, decision owner, and transition funding when the workaround is approved.

4. Build the proof that the compliance claim requires. Match evidence to the claim. Deliveroo’s substitution right was accepted in the CAC proceeding because couriers could use it in practice. Uber did not establish meaningful human involvement in the deactivation decisions involving three drivers; reviewers need the information and authority to change outcomes. The proceeding against Meta tests whether qualified researchers can obtain timely, usable, and reliable data. Build that proof alongside the response: Record changes and overrides, track affected outcomes, and rehearse what an external reviewer would ask the company to demonstrate.

5. Turn recurring, separable duties into product capabilities. Airbnb’s DAC7 process shows what reusable compliance infrastructure can include: tax data fields, user notifications, payout controls, and reporting workflows. Build such components for reuse only where requirements are genuinely equivalent, with shared specifications, testing, and version control as rules evolve. Reusable components can reduce local inconsistency when duties are separable from core decision logic. They cannot resolve rules that directly constrain allocation, pricing, evaluation, or control; those require an operating model decision.

Alongside these five operating moves, companies can seek clearer rules or interpretations through consultations, coalitions, standards setting, litigation, and engagement with policymakers. Channels vary by jurisdiction. These efforts should complement operational preparation, not delay it. Once authorities request records, inspect operations, or open proceedings, influence cannot replace a verifiable response. A company can challenge an interpretation while preparing to comply should the challenge fail.

Keep the Choice in Management’s Hands

A limited workaround may be sensible when a requirement is narrow, separable, and verifiable. Although these cases involve platforms, the diagnostic applies wherever connected systems shape consequential decisions — when banks allocate credit, insurers price risk, employers screen applicants, or retailers set prices. As generative AI enters these workflows, leaders must identify the model’s role, trace how its output shaped the decision, document the controls applied, and show how the result can be reviewed or challenged.

The risk begins when a limited response becomes part of the operating model. Glovo changed course after its workaround had spread and exposure had grown. Earlier action preserves a genuine choice among a contained fix, reusable compliance infrastructure, and deeper redesign. Once compensating changes shape core operations, redesign is no longer a future possibility; it is already happening. Management can direct it early or let accumulated workarounds dictate its shape.

It’s a familiar scenario: An organization implements digital tools intended to improve a particular process but fails to obtain the desired result. Employees who are meant to use them complain that the tools don’t solve the problem or that they add inefficiencies. Managers who investigate problems with the tools chalk the failure up to poor adoption but don’t look more closely at what caused the outcome.

When those leaders do seek to analyze failed digital investments, they make a common error: They evaluate data quality and system usability as a single variable. However, those factors operate through entirely different mechanisms, and they fail in different ways; improving one does not improve the other.

The cost of that error is specific and recurring. When a digital investment underdelivers, organizations almost universally attribute the failure to implementation problems, insufficient training, or change management breakdowns. These explanations are structurally inevitable when leaders don’t distinguish between a data quality failure and a system design failure. But without that distinction, the postmortem cannot identify the real cause. The result is a corrective investment that addresses the wrong problem, followed eventually by another underperforming system, followed by another misdiagnosis.

Empirical research that I and others conducted across one of the most cognitively demanding decision environments available for study found that data usability and system usability affect cognitive load through entirely different pathways.1 They aren’t two dimensions of the same problem. They are two distinct problems that require distinct diagnosis and distinct investment. The findings are directly actionable for any organization managing knowledge workers whose performance depends on digital systems.

Along with Kalle Lyytinen and David Aron at Case Western Reserve University and Michael R. Cauley at Vanderbilt University Medical Center, we surveyed 564 practicing physicians across 32 medical specialties to examine how data quality, system design, and information overload jointly shape doctors’ cognitive load when using electronic health records (EHRs) in high-stakes decision-making. We chose clinical medicine because it’s the highest-stakes, most extensively documented context for knowledge work under cognitive pressure and time constraints. The cognitive mechanisms that the research reveals are not unique to medicine, though.

EHR data usability, which encompasses the quality, completeness, and clinical relevance of patient information, increases germane cognitive load. Germane load is the productive cognitive effort associated with deep reasoning and meaningful engagement with complex information. When knowledge workers encounter high-quality, well-organized, contextually relevant data, they engage more deeply with it. That deeper engagement is the mechanism through which good judgments are made. It is not a symptom of overload or fatigue. It is the condition that produces decision quality. Better data makes physicians think harder about what matters. That’s the investment paying off.

We hypothesized that EHR systems that are highly usable — that is, those whose interface design, navigation structure, and workflow alignment support user interaction — reduce extraneous cognitive load. Extraneous load is unproductive cognitive effort generated by poor design: excessive navigation steps, misaligned workflows, alert fatigue, visual clutter, and documentation requirements that consume mental capacity without contributing to the decision at hand. Better system design eliminates that waste and redirects cognitive capacity toward the reasoning that produces accurate judgments. The problem it solves isn’t worker well-being. It addresses decision quality degradation caused by avoidable structural friction.

The research confirmed both effects with statistical precision. Data usability demonstrated a strong direct positive effect on cognitive load, with a standardized path coefficient of 0.597. System usability partially — and negatively — mediated that relationship, with an indirect effect of negative 0.571. Information overload mediated both pathways.

Why Correctly Diagnosing System Failures Matters

Consider what happens when an organization invests heavily in data quality without a proportional investment in system design. Data usability improves. Germane cognitive engagement increases. Knowledge workers are able to reason more deeply about better information. But if the system requires more navigation, more clicks, more workflow friction to access that information, extraneous cognitive load increases simultaneously. The total cognitive burden on the user goes up even as the quality of the underlying data improves. Decision throughput decreases. Users report fatigue and frustration. Leadership concludes that the digital transformation underperformed, without understanding the structural design problem: The investment improved data quality but didn’t include commensurate investment in reducing the friction of accessing and acting on that data.

The reverse failure is equally common. Organizations focus on system usability improvements, simplifying interfaces, reducing clicks, and redesigning dashboards without addressing underlying data quality. Extraneous load decreases. Navigation is easier. But if the data is incomplete, inconsistent, or poorly organized, the germane cognitive engagement that produces good decisions is not triggered. The system is easier to use. The decisions aren’t better.

Both failure modes are predictable once the distinction between them is visible, but neither can be seen when leaders evaluate digital system performance as a single variable.

Consider a financial services firm that invests heavily in a new data platform, consolidating market intelligence, portfolio analytics, and risk signals into a single source of truth. Data quality improves measurably. But the interface through which analysts access that data, built by a different vendor on a different timeline, requires eight navigation steps to surface a complete company profile and generates alerts at a threshold calibrated for compliance, not decision-making. Analyst productivity stalls. Senior talent starts leaving. The postmortem team concludes that the platform was poorly adopted and launches a training program.

The data investment was sound. The system design was not. The postmortem examined neither independently, so it fixed nothing. Had the two levers been assessed separately from the outset, the failure would have been locatable, correctable, and cheap to fix relative to what the misdiagnosis cost.

The Importance of Information Governance to System Design

The finding that higher data usability reduced perceived information overload was not about limiting data volume. It was about improving signal quality: making sure the information in front of a decision maker was relevant, reliable, and organized. Acting on this insight requires attending to the following four governance practices.

Eliminate redundancy. When the same data appears in multiple places within the same system, users stop reasoning from content and start verifying consistency. That verification work produces no decision value. Eliminating redundancy removes it and increases trust in what remains.

Show your data’s lineage. When a decision maker cannot quickly assess whether a data point is current, how it was collected, or how much confidence it warrants, they spend mental capacity on source verification rather than the decision itself. Embedding simple indicators of reliability — when a measurement was taken, by what process, and with what confidence level — eliminates that overhead and lets judgment begin sooner.

Mandate the fields that matter most. Missing information at a critical decision point doesn’t just slow things down; it breaks the workflow entirely and forces improvisation that shouldn’t be necessary. Structuring data capture requirements around the fields most critical to core decisions ensures that gaps appear by exception, not by default.

Mind your alert thresholds. Alert fatigue — the learned tendency to dismiss notifications because most don’t require action — isn’t a user behavior problem. It’s a governance failure. Every alert threshold is a decision about where to direct a decision maker’s attention. When those thresholds are set too low, attention is trained away from the system entirely. Resetting them so alerts fire only when action is genuinely required is among the highest-return cognitive design improvements available, and it requires no new technology.

Do Better at Diagnosing Why AI Systems Underdeliver

Organizations investing heavily in AI-enabled decision support systems across industries are generating the same postmortem pattern at scale. Leaders evaluate AI performance primarily through model accuracy, adoption rates, and efficiency gains. Those metrics are necessary. They are not sufficient.

AI systems that improve the quality and relevance of information surfaced to decision makers are engaging the germane load lever. They are making the data better. That is valuable. But if the interface through which users interact with AI outputs is poorly designed, if recommendations arrive without sufficient context, if the system requires significant navigation to understand the basis for a recommendation, or if it generates notifications users have learned to dismiss, the extraneous load the interface imposes will erode or eliminate the germane load benefit the AI delivers.

Making an AI investment that improves recommendation quality without making an equivalent investment in the interface design required to act on those recommendations will produce the same misdiagnosis as every prior generation of digital underperformance: The tools will be blamed, the training questioned, the workforce examined, and the structural design problem left in place.

Leaders should look for three qualities in any AI system in their portfolio. First, the data it surfaces must be worth the depth of reasoning it demands from the people using it. Second, the interface design should reduce the structural friction of accessing and acting on that data. And third, the information that is surfaced, when it is surfaced, and any accompanying signals as to its urgency, should be designed to direct judgment toward what matters, not scatter attention.

How Leaders Should Evaluate the Success of Digital Systems

The questions that belong in leadership reviews of digital system performance extend beyond standard metrics of uptime, accuracy, and user adoption. They are questions about cognitive design.

Does the system reduce the effort required to find relevant information, or does it increase it? Are the alerts the system generates ones our people act on or ones they have learned to dismiss? Is the cognitive engagement this system demands producing better decisions, or is it creating structural friction that the design should be eliminating? What signals are being surfaced at each decision moment, and are they the right ones for that moment? Are there signs of decision quality degradation — rising error rates on complex decisions, increased escalation volumes, or shortened tenure among experienced decision makers — that point to a poorly designed cognitive environment rather than a talent or motivation problem?

Alert configurations, interface logic, workflow design, and data governance thresholds are cognitive design decisions with direct performance consequences; they are not IT configuration choices. Leaders who want to improve the returns on their digital investments must gain visibility into these decisions, not cede them entirely to implementation teams, vendors, and IT administrators.

In July 2024, the cybersecurity software company CrowdStrike pushed a routine but buggy software update to its platform. In the ensuing 78 minutes, before a patch was deployed, IT systems worldwide crashed. Microsoft estimated that 8.5 million Windows devices were affected. As blue screens of death cascaded across the globe, airlines grounded fleets, hospitals lost access to patient records, and banking platforms went dark. One analysis estimated that the collapse cost Fortune 500 companies $5.4 billion.

Six months earlier, a ransomware group had accessed Change Healthcare, the largest medical claims clearinghouse in the United States, through a single portal that lacked multifactor authentication. Within days, nearly every pharmacy, hospital, and physician practice in the U.S. was unable to process insurance claims. Surgeries were postponed, and the personal health data of as many as 1 in 3 Americans was exposed. Combined direct response costs and business disruption impacts incurred by parent company UnitedHealth Group exceeded $2 billion in the first half of 2024 alone, according to the company’s filings with the Securities and Exchange Commission.

Two incidents. Two different failure modes. Combined documented losses exceeding $7 billion. In both cases, crisis experts wondered, had anyone rehearsed this?

They could have. As far back as 2022, McKinsey was reporting that 70% of C-suite technology executives at large enterprises were exploring and investing in digital twins as a way to optimize operations, model supply chains, and accelerate decision-making. A digital twin is a dynamic virtual replica of an organization’s operations, supply chain, manufacturing lines, IT infrastructure, or distribution network, connected in real time to the data flows that govern its physical counterpart. Unlike a static model, a digital twin updates continuously as conditions change, and it can be queried, stressed, or reconfigured without touching the physical system.

Few companies are using this modeling capacity to rehearse crisis scenarios. They’re not using it to test what happens when a software provider’s routine update crashes their operating environment, or when their largest payment processor goes dark. But they could be. The gap between what digital twins are used for and what they could be used for is not a technology gap. It is a strategic gap that has cost organizations billions of dollars and is risking the loss of much more.

Warning Signals From Practitioners

To test whether this gap was visible at the practitioner level, I conducted a structured poll during a session titled “Rehearsal Intelligence: AI Digital Twins for Crisis-Ready Organizations” at ASIS Europe 2026, a Tier 1 international conference drawing risk professionals from across sectors and geographies. Roughly 40 practitioners attended the session; between 19 and 23 responded to each poll question. While I cannot claim that this convenience sample is statistically representative, its composition — exclusively senior security and resilience practitioners with direct organizational visibility — offers a meaningful practitioner-level signal.

Of the 21 respondents who identified their role, 47% were corporate security directors or heads of security. The remainder were in risk management, security consulting, and C-suite functions.

Asked how often their organization conducts crisis simulations, 68% reported once a year, through a tabletop exercise — typically, a facilitated discussion in which a team walks through hypothetical scenarios and planned responses. A further 15% reported never testing the crisis plan at all. Only 5% reported conducting simulations continuously, using live data or digital tools.

The second question produced the starkest finding. Of 22 respondents, 20 (91%) reported that their organization was not using digital twins in any capacity. Two said that their organization used digital twins for operational monitoring. Not a single participant reported using digital twins for crisis simulation.

The third question identified future impediments. Asked what the primary obstacle was to adopting digital twins for crisis management — and, by extension, where challenges might lie in organizations yet to adopt general digital twin technology — 43% said “insufficient leadership awareness and buy-in.” This was the top answer, above budget constraints, technical complexity, and data integration challenges.

Why Traditional Simulations Are No Longer Enough

An executive might reasonably say, “We have crisis teams, we run tabletop exercises, and we have AI. Is the absence of digital-twin-based rehearsals truly a strategic risk?”

To be clear, tabletop exercises run by crisis teams have genuine value. They align work groups, expose assumptions, and create shared mental models. No serious resilience professional would argue against them. But they carry structural limitations that become more consequential as crisis complexity increases.

First, tabletop exercises operate on pre-constructed scenarios. In other words, the exercise is designed around a crisis someone has imagined. CrowdStrike and Change Healthcare were not included in anyone’s tabletop scenarios. The crisis that will actually test your organization is, by definition, the one that was never in the playbook. Second, tabletop exercises are episodic. The muscle memory that organizations build inevitably decays between quarterly or annual sessions. And third, tabletop exercises test the team, not the system. The exercise reveals how particular crisis management team members think under pressure, but it does not reveal how the actual organizational systems, supply chain, IT infrastructure, customer-facing operations, or financial flows might behave under real crisis conditions. Stress-testing examines financial or technical resilience within defined parameters but is not designed for cross-system cascade failure. The gap between what a team decides and what the organization can execute is precisely where most crisis responses break down.

Artificial intelligence adds some value to planning, but less than leaders might expect. In a peer-reviewed study published in March 2026 in International Studies of Management & Organization, researchers Raphaël De Vittoris and Carole Bousquet analyzed 24 crisis simulations conducted between 2017 and 2024 that tested the predictive capacities of human and artificial intelligence across a range of scenarios.

Each simulation was built around nine critical developments that a crisis group should be able to anticipate. Those points were embedded in the scenarios in advance and validated by internal and external crisis professionals, and performance was measured as the share of critical developments that each group involved in the study actually surfaced.

On that measure, AI operating alone identified 41% of the critical points. (The AI models tested were the three most frequently cited by the crisis management team members themselves — the tools they would plausibly reach for under pressure.) Human teams without any support identified 48%. But the use of sophisticated AI platforms barely added value to crisis-anticipation performance: Human teams that used AI identified 49% of the critical points, a gain of a single percentage point. Human teams equipped with structured information access (that is, Google’s search engine) found 81%.

Those results carry a direct management implication. AI investments on their own won’t yield the best solutions. Instead, preparing teams before pressure arrives requires different options.

In their 2025 article “How to Supercharge Your Crisis Training,” MIT’s Sandra Galletti and Steven B. Goldman argue compellingly that organizations need to move beyond passive crisis planning toward active, experiential simulation, and that most organizations fall significantly short of what modern crisis complexity demands. They’re right.

Digital twins offer a way to inject systems with active simulation. Digital twins can stress-test systems against scenarios no one has imagined, run continuously rather than episodically, and be used to test not just team thinking but an organization’s actual capabilities to respond to disruption. Rehearsal intelligence is not a theoretical construct but the organizational capability that emerges when the digital twin is deliberately repurposed from an operational optimization tool into a permanent crisis-anticipation environment. The technology already exists in increasing numbers of large organizations. The gap between using digital twins for operations and digital twins for crisis rehearsal is an opportunity that requires a governance decision that data suggests is long overdue. What’s missing is the framework to deploy it for this purpose — and, as the poll data from the practitioners makes clear, the leadership attention that could make it systematic.

A Five-Step Framework to Deploy Digital Twins for Rehearsal Intelligence

Repurposing an organization’s existing digital twinning tool for a new class of questions rests on a five-step framework of organizational design decisions.

1. Treat the digital twin as a permanent rehearsal environment, not an episodic exercise tool. Crisis simulation should not be an event but rather an ongoing organizational practice. The digital twin should be accessible for scenario stress-testing as routinely as it’s used for operational planning.

Some organizations are already moving in this direction, though without a shared language for what they are doing. BMW planned and validated the construction of a new plant in Debrecen, Hungary, in an entirely virtual environment more than two years before physical vehicle production began. Its digital twin simulates every production change before any physical modification is made, in part to avoid danger and damage. For instance, what BMW calls its “virtual factory” allowed faster insight into collision checks — simulating the movement of a new vehicle design through the production line to make sure it wouldn’t scrape up against anything. Using the digital twin reduced what would typically take almost four weeks to just three days. In June 2025, the company announced that it would be scaling the use of digital twins across 30 of its production facilities worldwide and projected that production planning costs will fall by up to 30%.

Similarly, Walmart has deployed digital twins across many of its stores and distribution centers. Brandon Ballard, group director for real estate at Walmart US, said last year that digital twins have helped the company detect and remediate potential equipment failures up to two weeks before they occur, reducing emergency alerts by 30% and cutting refrigeration maintenance costs by 19%, according to CNBC. In one implementation across 20 stores, a digital twin project proactively identified and addressed 842 potential failures in Walmart’s systems, such as refrigeration, in a six-month period, enabling the company to avoid an estimated $1.4 million in downtime costs.

2. Position AI as a structured sparring partner, not a decision maker. The De Vittoris and Bousquet study offers a warning here. Deploying AI casually added a single percentage point to team performance. The best results came from teams that used tools they had mastered, in a structured and deliberate way. Among the top-scoring crisis cells, those that used AI engaged with it through multiple contextualized prompts, in real conversations, explicitly asking for the anticipations that a team emotionally affected by the event might miss. The worst-scoring team to use AI submitted one short, uncontextualized question. The researchers’ conclusion is that organizations need a genuine prompt culture that includes a library of adaptable prompt templates built before a crisis arrives. That literacy comes from deliberate practice, not from buying an AI platform.

Here’s a suggestion: Build one structured anticipation session into your next crisis team meeting. Give the team a specific disruption scenario and ask them to regard your AI environment as a structured challenger to generate three alternative scenarios, identify second-order consequences of each, and test the assumptions your current crisis plan rests on. This is precisely the structured engagement that separated the best-performing teams in the study from the rest. It’s available to your team today.

3. Embed structured anticipation into organizational culture rather than delegating it to a crisis unit. The performance differential in the De Vittoris and Bousquet study came from anticipation mechanisms that were formalized and actually used when the crisis hit, not from titles or org charts. It’s a matter of organizational design, not staffing.

At Walmart, the digital twin infrastructure that monitors refrigeration temperatures could be queried against disruption scenarios: What happens across 200 stores if the monitoring systems fail simultaneously? What is the cascade if three distribution centers lose connectivity during peak demand? These are questions that general managers, not just risk managers, can ask.

The first step toward building this culture is to run one crisis rehearsal scenario in your digital twin environment. You don’t need a comprehensive program or a multiyear transformation. You just need one scenario, representing a realistic disruption event relevant to your sector, with a structured debrief within 72 hours. The goal is not a perfect simulation. It’s to harness the organizational learning that comes from running one.

4. Build scenario templates and structured question sets that enable crisis teams to engage the digital twin environment rapidly when a scenario emerges. In financial services, digital twins that model trading infrastructure and liquidity flows can be queried against settlement failures, counterparty cascades, or regulatory intervention scenarios. In pharmaceutical manufacturing, production-line twins can be stress-tested against supply disruptions or batch-failure events before they reach patients or regulators.

In energy and utilities, grid topology and distribution twins can simulate cascade failures before they reach physical infrastructure. FedEx has built a digital twin of its global logistics network, which it uses to forecast, identify, and minimize disruption.

The point is, the technology footprint and focus will vary by sector. Repurposing digital twins will call for a new class of questions.

5. Adopt a three-stage maturity model — reactive to structured to anticipatory — that gives leadership a clear diagnostic and a direction of travel. The majority of organizations in my ASIS Europe poll were in the first stage, reactive, with crisis plans and occasional tabletop exercises, while digital twins were either absent or siloed in operational functions. The second stage is structured, where the organization has conducted at least one deliberate crisis-rehearsal scenario using a digital twin environment, with assigned ownership of the rehearsal function, a defined debrief process, and documented findings. The third stage is anticipatory, where crisis rehearsal is continuous and embedded.

Stage 3 has an underlying prerequisite that no governance decision alone can shortcut: the need for quality data feeding the digital twin. Organizations that reach Stage 3 have made the accuracy, accessibility, and maintenance of their live data environment a leadership priority, not just a technology department responsibility.

For most organizations, moving from the first to the second stage will mean finding out whether currently deployed digital twins within specific functional domains can be accessed, combined, and stress-tested by a crisis anticipation team. This does not require a technology project. It requires that you assign one person to audit which digital twin environments your organization currently operates and then have a two-hour conversation with your technology leadership.

The organizational capability that CrowdStrike and Change Healthcare revealed to be absent in 2024 has a name: rehearsal intelligence. Although it is urgently needed and the technology to build it is being adopted by more and more large enterprises, most organizations don’t have it in any systematic form.

Organizations can realistically aim to get to Stage 3, where they are in a continual state of anticipation, and crisis rehearsal is ongoing and entrenched. The return extends beyond crisis avoidance. Organizations operating at Stage 3 report faster decision-making (because the decision architecture has been tested against disruption before a real crisis arrives) and more precise capital allocation (because leadership understands which system vulnerabilities carry the greatest operational risk). They have stronger AI utilization because teams engage with AI as a structured reasoning partner rather than a retrieval tool, and because they have a measurable recovery-speed advantage over competitors encountering the same disruption for the first time.

As Jürgen Wittmann, head of innovation, virtual factory, and virtual commissioning in BMW’s production department, told CIO magazine last year, “Thanks to the digital twin, we know exactly the current situation and can immediately see the impact of changes.” Why shouldn’t all companies use that same tool for a wider look at the horizon?

The distance between where most organizations are today and where rehearsal intelligence can take them is not measured in technology investment or budget cycles. It’s measured in the decisions made, or deferred, right now. The CrowdStrike and Change Healthcare outages didn’t announce themselves in advance. The next disruption won’t either.

Businesses working to manage their spending on energy were dealt a blow in late February following military action by the U.S. and Israel against Iran. A predictable consequence of the conflict was a dramatic increase in the price of crude oil, the primary benchmark for global energy costs. The price of a barrel of crude oil shot up from $67 in early March to $112 in early April; by early June, it had come down to $80, still a roughly 25% year-over-year price increase.

Geopolitical conflict can bring exceptional volatility to energy costs, but steadily rising expenditures have been the underlying trend for companies since 2022, putting pressure on their profit margins. Energy is a central input in energy-intensive industries such as cement, chemicals, and metals, which account for roughly three-quarters of industrial energy demand. Companies that rely on global supply chains, and even companies that consume relatively less energy in their operations, like food processors, are feeling the effects of rising fuel and power costs.

The pressure to reduce costs is likely to drive more corporate efforts to improve energy efficiency. While that’s good for sustainability, our research suggests that energy efficiency can also deliver measurable cost savings, enhance resilience, and fuel growth. Attention to energy efficiency can lower per-unit manufacturing costs, reduce exposure to energy-price volatility, and sustain margins during energy shocks. The companies we studied have cut their energy use at some facilities by up to 50% and reported efficiency gains of 10% to 20% in their core industrial processes.

Upgrades to digital systems, manufacturing equipment, and facilities all played a role in those outcomes. The key to success, however, came not from any upgrade made in isolation but from having a comprehensive strategy that linked improvements together. Company executives made energy efficiency a clear priority, with defined accountability and performance targets.

From our surveys of Swiss manufacturing companies and interviews with executives across the globe, we identified three ways in which the most energy efficient among them manage their energy costs and take advantage of digital capabilities in that work.

First, they invest in collecting and analyzing data about energy use from everywhere in the company and its ecosystem and making that data visible to managers. Using sensors, dashboards, and centralized data platforms, they enable real-time energy management and cross-site benchmarking. Second, they redesign their operations and systems to optimize energy use, eliminate waste, and reduce dependence on energy-intensive inputs. By embedding advanced analytics and AI into daily workflows, they can improve continuously. And third, they are more likely to create products and services that support their customers’ energy efficiency goals.

Here, we’ll explore energy efficiency practices among the companies we studied and provide insight into how managers can adopt similar approaches.

Make Energy Data Visible Everywhere

Enterprise-level energy efficiency becomes feasible when the energy that business operations consume is visible in near real time. In our survey of Swiss companies, those in the top quartile on energy efficiency rated themselves more highly, on average, than those in the bottom quartile on integrating data collection, analytics, and reporting technologies across their operations. Executives we interviewed at those and other companies reported that they use those capabilities to systematically and continually track and analyze information on energy usage and share it with internal and external stakeholders. Specific practices included translating data into actionable insights for operations teams, frequently sharing energy metrics with senior leadership, and communicating successes to customers, investors, and regulators.

At Swiss specialty chemicals manufacturer Clariant, digital technologies are central to measuring and sharing energy usage data. One of its early initiatives was to install sensors to capture data on energy used by plant machinery. It also invested in operational dashboards for its sites to consolidate their data and uncover opportunities for efficiency improvements. The sustainable operations team now tracks high-frequency data from 80 manufacturing sites and stores it in a central data repository.

Increased visibility has helped to improve energy efficiency on several levels. Plant managers and site operations teams use dashboards to monitor energy consumption, emissions, and operational efficiency across Clariant’s production facilities so that they can identify potential improvements. Business and regional operations leaders use dashboards for cross-site benchmarking, revealing where they may be lagging behind comparable operations. The sustainable operations team monitors energy usage across plants and works with managers at each site to determine how to reduce their energy costs.

Clariant’s savings have been significant. At one plant in China, the energy used to produce finished goods was cut by about half from 2019 to 2021, and even further in subsequent years.

The company’s generative AI-based Clarita platform, introduced in 2023, is used across production sites to monitor energy consumption, identify inefficiencies, and recommend actions to optimize processes while continuously learning from operational data. At a production plant in Germany, local operations and energy efficiency teams used Clarita to identify abnormal steam consumption in a heating system by comparing real-time usage with historical baselines. The results led them to adjust operating parameters, which significantly reduced steam demand. Additionally, a mining site in Indonesia used the system to save energy by improving routines for scheduling and maintaining its equipment. (See “Can AI Save Energy?”)

Cut Energy Waste

Despite having improved visibility into energy use, many companies we studied still struggled to reduce their energy costs because they did not use the insights from their data to change their production processes or to manage raw materials and fuels differently.

Those reporting the most action on energy efficiency in our sample closed this gap by embedding energy and resource management into routine operations and decision processes. For these companies, energy efficiency became part of daily performance tracking, with energy use measured and managed alongside productivity and cost metrics rather than treated as a separate sustainability concern. Then they deployed new technologies, such as equipment that manages its own energy use, along with heating and cooling systems that employed waste-heat recovery technology. They also redesigned manufacturing processes to use fewer materials and less energy.

Some companies are also moving to replace fossil fuel inputs (and their associated price volatility) with electricity and renewable sources, which are often more efficient.

Take, for example, Swiss industrial group Georg Fischer (GF), which makes equipment for transporting liquids and gases. The company has focused on designing operations to cut energy waste as part of a broader energy efficiency strategy, said Oliver Hilbrand, a plant manager. “Our goal was to significantly increase efficiency in all areas — from production to logistics to energy use,” he said.

At the Seewis, Switzerland, site where GF manufactures valves and actuators, it has deployed a combination of energy-saving technologies, including higher-efficiency machinery, smart lighting, waste-heat recovery, and better insulation. Additionally, the site gets 100% of its electricity from renewable sources. As of 2023, the plant had reduced its emissions from fossil fuels by about 63% compared with a 2019 baseline. While not a direct measure of fossil fuel use, a reduction in emissions indicates that a company is less dependent on it — and less exposed to fluctuating fossil fuel prices.

Meanwhile, GF is shifting to renewable electricity sources companywide. It is installing solar panels on factory roofs and buying certified renewable electricity from regional hydro and wind sources. These steps have not only reduced GF’s dependence on grid power generated by fossil fuels; they have also made it easier to predict future energy costs. Although renewable energy generation fluctuates across days and seasons, the cost per kilowatt hour of solar power is less volatile than that of oil and can be managed through local measures, such as storing solar power in batteries for later use during peak-rate hours. By 2025, GF was sourcing 62% of its total electricity from renewable sources.

Further, the company has applied circular-economy principles to reduce its consumption of energy-intensive raw materials, such as plastic and steel. If it can maintain quality and comply with regulations, the company mixes its scrap with raw materials to reduce raw material inputs. In 2025, it reported that it was recycling 68% of its waste, including scrap from production.

In addition, the company conducts life-cycle assessments to evaluate its energy footprint across the entire value chain, including raw material processing, product manufacturing, transportation, product use, and disposal. It discloses that data through Environmental Product Declarations verified by third parties, enabling it to benchmark against global standards and identify targeted opportunities for further improvements.

Create Energy-Smart Offerings

Among the companies we studied, we saw a third area of focus in energy efficiency emerging: developing products that use fewer resources and enable customers to reduce their own energy consumption. Those reporting more activity in this area were significantly more likely to be partnering with other companies to reduce energy use and environmental impact.

By offering more energy efficient products, companies can create more value for customers. For example, when GF customers upgrade their equipment, the company is able to quantify energy savings and other sustainability benefits they can expect to achieve. Meanwhile, ABB, a global provider of electrification and automation systems, has partnered with E.ON, an infrastructure services company, to offer energy efficiency services, such as appraisals, system design, and financing, with guaranteed savings. Those offerings reduce upfront barriers to investing in new equipment and shift performance risk away from the customers.

Companies can also capture value indirectly by embedding energy efficiency into their products, providing customers with self-service data, and using their strong internal energy efficiency performance to attract and retain customers.

ABB has developed a portfolio of digital tools that help customers manage their energy use more effectively. Analysis of the company’s installed base showed recurring inefficiencies in motors, pumps, and process equipment, along with limited visibility into the energy that equipment consumed. In response, ABB introduced a service that combines monitoring, analytics, and AI to identify inefficiencies and recommend actions. These systems connect equipment, track energy flows in real time, and coordinate operations across assets, enabling continuous adjustment. The company reported that its cement industry customers have achieved efficiency gains of 15%-18%. Data center customers are up to 25% more efficient. In addition, these tools enable customers to troubleshoot anomalies in their energy consumption 60%-80% faster.

Similarly, DMG Mori, a global manufacturer of computer-controlled machine tools such as milling and turning machines, developed its Greenmode offering after analyzing how energy was being consumed during machine operation at customer sites. The company observed that customers’ energy consumption was driven not only by machining but also by auxiliary systems, such as machine cooling, feed drives, and compressed air, and by prolonged idle times. Further, customers had limited real-time transparency into how much energy their machine tools used.

To address these issues, the company built features into its machines that help customers see their electricity consumption in real time and operate machine components according to when they are needed during production instead of running them continuously. DMG Mori data that has been certified by TÜV Süd, an independent testing and certification organization, shows that these measures have reduced machine energy consumption at customer sites by more than 30%, on average, and up to 40% compared with earlier-generation machines.

Our research points to a reinforcing cycle: Companies that make energy consumption visible across the enterprise gain the insight to redesign operations, and the savings from redesigned operations can be used to fund products and services that help customers do the same. Those that have made an organizational commitment to treat energy efficiency as a business priority set performance goals and manage for them. As long as fossil fuel costs remain volatile, companies that can continuously improve their energy efficiency will have the advantage over those that are forced to react to — and absorb — every shock.

The in-house lawyers at real estate investment firm Alturas Capital Partners used to rely on outside counsel for much of its lease work. Thanks to generative AI, it can now do that work internally, saving the firm hundreds of thousands of dollars in spending and compressing lease work that once stalled for weeks. But for the outside counsel that lost the work, the bigger problem is existential: AI has eliminated the need for their services. This is redefining the boundaries of firms, since some firms are expanding the scope of their work, while others are losing it to their former customers. This effect is also affecting the prices that the remaining providers can justify. Just recently, Wall Street banks pushed big law firms to cut fees because of AI.

This scenario is widespread: Generative AI has lowered the cost of producing legal documents, market analyses, creative assets, and software in situations where a capable in-house team equipped with AI can credibly replicate what an outside provider had been supplying. Service providers must find ways to ensure that there is still a need for their work.

Services firms have traditionally built their value propositions around the specialized expertise and experience of their staff, as well as their proprietary methods. Generative AI has changed that by providing access to a fair degree of knowledge that was once the sole province of human experts.

This is not the first time technology has changed the economics of what companies handle in-house and what work they outsource. In the 1990s, technological advancements in communications and software enabled the expansion of services as it became feasible — and cheaper — to outsource back-office work.

Calculating Customer Costs

Service providers must rethink existing sales strategies that are based on expertise alone, now that generative AI is changing the economics of the decision to outsource higher-level knowledge work and making it cheaper to bring it in-house. They need to focus on how to win based on cost as well. Here are three ways they can do so.

Improve unit economics. Services firms’ first move should target production cost. The instinct for providers under threat is to defend their expertise — to argue that their people produce better work than a client’s in-house team armed with AI tools. That defense is collapsing. The durable advantage lies not in the skill behind each output but in the economics of producing thousands of them. Providers that build the infrastructure to produce work at scale — automated pipelines, open models, costs spread across many clients — can price each finished output below what any single client could match by doing the work in-house. Cheap production pulled the work inside; cheaper production can pull it back out.

One of the world’s largest marketing services groups, WPP, offers an example of how a traditional services firm can win this way. It built WPP Open, an agentic marketing platform that draws on decades of proprietary intelligence, including 30 years of data from the world’s longest-running brand equity study, and behavioral science frameworks from its agency Ogilvy. Coca-Cola is among the brands using it. WPP has also built a self-serve offering through which marketers build strategies, generate assets, and activate media campaigns. While a competitor could acquire the same technology, it would still lack the accumulated expertise that powers the agents — expertise that takes decades to build.

This requires a fundamental shift in how professional services firms think about their business, to service productization rather than bespoke delivery. Generative AI has made that shift both more urgent and more achievable. Providers that have already begun the journey — by standardizing workflows, training models on proprietary data, and packaging expertise into repeatable systems — are best placed to win on unit economics. As one analysis of professional services firms argued, the service providers that thrive will be those that shift from delivering their expertise through people alone to delivering it through people and systems together.

Make buying as easy as asking. The second move targets a different cost: the effort of buying itself. Hiring a provider has never been free of effort. The customer has to find the right expert, negotiate terms, explain its needs, review drafts, and integrate the result into its own systems. Every hour spent on that strengthens the argument for doing the work in-house. Agentic AI lets providers eliminate that friction, with agents that take the request, do the work, and deliver the answer directly into the tools the customer is already using.

Moody’s shows this advantage at work. The credit intelligence firm watched its own customers pick up generative AI and saw that they would soon be able to answer their own credit questions. Its response was to build AI agents that run analyses automatically and deliver the results within the Microsoft applications customers already use, such as Excel. A portfolio manager checking a counterparty’s credit risk now gets Moody’s ratings, data, and research right in the spreadsheet via Microsoft Copilot, the AI tool they would have used to do Moody’s work themselves. The answer arrives in moments.

Law firms are taking the same path. A&O Shearman, for example, has built AI agents — developed with legal AI firm Harvey — that distill the reasoning of its senior lawyers for tasks like antitrust filing analysis and reviewing loan documentation, and makes them available to clients and other firms by subscription.

When buying from a provider becomes as easy as asking, building an alternative in-house stops looking like a worthwhile investment.

Own the operational burden. The third move targets the cost that clients encounter last: quality assurance and maintenance. Bringing work in-house with AI looks easy at first, but someone has to check every output before it can be trusted, fix mistakes, rewrite prompts and workflows as models change, and clear outputs through compliance. The costs accumulate quietly — in staff hours, in rework, in constant maintenance — until they rival the fee the client was paying the provider in the first place. The service provider’s move is to make that burden visible to the customer and be able to carry it for them.

Thomson Reuters illustrates how a traditional knowledge provider can win on this ground. The company’s CoCounsel Legal is an AI research product built on its Westlaw legal platform, which contains decades of curated case law, 35 million legal classifications, and the ongoing work of more than 650 attorney-editors. A firm building its own AI legal research tool would have neither the content infrastructure nor the verifiable citations and audit trails that courts and compliance functions require.

What Service Providers Under Threat Can Do Now

Service providers survive when they win the client’s full make-or-buy comparison: cheaper to run, easier to buy, and less painful to manage. Here’s how to get started on each of the three moves.

1. List which of your deliverables generative AI can already produce just as well as a skilled person, because those are the ones clients will pull in-house first. For each, compare what one output costs you, all in, against what the client would pay in tools, tokens, and staff time to produce it internally. Wherever your numbers win, package the system and sell it.
2. Count the steps and the days between a customer asking you for something and getting a usable answer. Every step is a reason to build instead. Cut those steps to deliver your expertise as close to the customer’s use case as possible. Agentic AI can help here.
3. For every customer considering building in-house, write out what running the work would actually cost their business: the hours checking outputs, the rework, the model updates, the compliance reviews, and the salaries behind it all. Put that total next to your fee. If the customer insources anyway, stay close and reopen the conversation two quarters later, once those costs have appeared in its books.

Generative AI has not made expertise worthless. It has raised the bar for what providers must offer alongside it. The firms that thrive will be those that make buying cheaper, easier, and less burdensome than building in-house.

For years, social media companies have come under fire for promoting divisive, false, and dangerous content in order to monetize user engagement. But in March, when a jury in Los Angeles Superior Court found Meta and Google liable for causing harm to users due to the addictive nature of their products, the verdict put the focus not on content but on the companies’ deliberate design choices.

What stands out to us in considering the implications of the court decision is that even absent a willfully exploitative business ethos, other companies may unwittingly create the potential for harm simply by applying user-centered design methods such as design thinking. These methods prioritize giving users what it seems that they want. They involve engaging with target users to understand their wants and needs and having them test and offer feedback on prototype products. Our research found that such methodologies, used in good faith, can nonetheless lead practitioners to ignore potential harms or give inadequate attention to ethical considerations.

To better understand how such situations arise, we interviewed 27 senior managers and leaders at well-known technology, pharmaceutical, and design consultancy firms based in the U.S. and Europe. We asked them about the ethical challenges they have encountered when applying design thinking and other user-centered approaches, as well as the governance mechanisms they have used — or wished they had used — to mitigate unintended harm.1

We heard about many moments of reckoning. Features that increased user engagement were described as leading to addictive user behaviors, as in the Meta case. Over time, personalization contributed to the amplification of extreme and polarizing content. In each case, the teams had followed user-centered best practices, but unintended consequences repeatedly emerged. Such experiences raise a fundamental issue: How can we better understand and manage the blind spots and ethical challenges that user-centered design may create?

Methods like design thinking have gained popularity as a creative way to identify and address user needs.2 Applied by companies such as IBM, Meta, PepsiCo, and SAP, design thinking is a problem-solving and innovation approach founded on several principles, including collaboration across functions, iteration and experimentation, and the adoption of an empathetic, user-centered orientation.

While there are plenty of examples of design thinking as an effective methodology for developing products that appeal to users, we’ve also seen cases where its use simultaneously resulted in harm to users and other stakeholders. For example, Juul’s electronic cigarettes emerged from the work of two Stanford University product design graduate students, who used design thinking to create more appealing products than those already on the market. While the product was certainly appealing, achieving 70% of e-cigarette market share in the U.S. in the mid-2010s, it had to pay over $450 million to settle legal claims over product harm and false marketing in 2023. A big part of the problem was that efforts to improve the user experience by introducing nicotine delivery pods in fruity flavors appealed to teenage users, making Juul the dominant brand for vaping among high school students. The failure to consider their choices’ potential harms as the business scaled belongs to Juul’s leaders, but the ease with which design thinking practice can overlook such concerns must be noted.

Overall, a clear ethical framework underpinning the application of design thinking and other user-centered approaches is lacking. Our interviews suggest that this gap is systemic. It stems from recurring blind spots in how user-centered innovation is practiced.

When User-Centered Becomes User-Blind

While our interviewees acknowledged that understanding users’ needs is important, they also highlighted four issues that a strong focus on users may create.

1. Leaving the environment out of frame. Even though the natural environment was clearly affected by the design thinking implementations we considered, none of the firms we engaged with explicitly considered the environment as a stakeholder. And when someone raises such considerations, design teams aren’t necessarily responsive. The director of user experience design at a global smartphone manufacturer recalled the team’s negative reaction when, during a design session, the head of environmental policy asked whether the company really needed to release a new device every year, given how many old phones went into landfills despite the company’s recycling program.

Such issues are problematic because the creation, production, and delivery of various companies’ products and services have evident environmental impacts, including the use of rare mined materials in consumer electronics, the disposal of hazardous materials in pharmaceuticals, and the energy consumed to create digital products. In all these instances, design decisions may degrade a healthy natural environment that humans depend on. Most companies appeared to treat these issues as externalities rather than risks to be proactively managed.

2. Overlooking stakeholders who aren’t the primary target users. Concentrating on the target users often restricts product developers’ attention to specific personas, leaving out other relevant stakeholders. These may include vulnerable users, nonusers who could be indirectly affected by the product, and potential malicious users. For example, the CEO of a design services firm described a location-sharing feature in a dating app that was intended to improve matching and enhance user safety. While the design team saw it as a value-adding innovation, many women interviewed during testing compared the feature to stalking. “For features that track users, you need to design with creepers in mind,” the CEO pointed out. In this case, the team had designed for well-intentioned users, not for malicious ones or those more exposed to risk.

3. Sacrificing scrutiny for speed. Like other innovation approaches, such as agile methods and Lean Startup, design thinking entails rapid experimentation with users to develop new offerings. While quick prototyping and pressure to deliver results can be useful in certain settings, speed can reduce consideration of potential harm, especially for more vulnerable users, such as children. A senior director at a design services firm reflected on whether more time could be spent in developing and iterating prototypes with users: “In a perfect world, definitely yes. But we live and work in an imperfect world with tight project deadlines and resources.”

4. Exposing the company to risks through scaling. We found many examples of iterations conducted with a small set of users that produced dysfunctional outcomes when the products scaled and reached more diverse populations of customers, particularly vulnerable users, such as children. There are two reasons for this. First, small-scale tests are not always the best approach for unearthing a wide variety of use cases, especially malicious use cases, which often emerge as user diversity increases. Second, several executives we interviewed described how so-called edge cases were treated as statistically insignificant during development. As one expert noted, even a small percentage can become consequential at scale: “In Facebook’s case, the 1% is 1% of 2 billion users. That’s approximately 20 million people. People. Not widgets.” Features developed around a narrow user profile can behave very differently when used by millions of people with varied motivations, vulnerabilities, and social contexts.

Delivering Innovation While Minimizing Ethical Risks

Effective leadership and deliberate governance are required to address the limitations we’ve identified in the design thinking process. Senior managers need to understand that user-centered approaches can have ethical blind spots, and that they must take an active role in supervising their use. There are three elements in doing this.

Start with purpose, not just user needs and desires. By purpose, we mean both financial objectives and the company’s mission and values, especially as they relate to society and the environment. Potential consequences for multiple stakeholders should be discussed from the beginning, including secondary and indirectly affected stakeholders. However, there are trade-offs to be managed here. In principle, broad inclusion improves foresight, surfaces edge cases, and reduces downstream harm. In practice, it is neither feasible nor strategically prudent to convene a “committee of everyone,” as this can slow development cycles, create decision paralysis, expose confidential information, and dilute competitive advantage. The challenge, therefore, is not maximal inclusion but structured inclusion.

Three key questions should be asked at the outset:

• What broader value are we trying to create beyond addressing direct users’ needs?
• What trade-offs are unacceptable, even if engagement or revenue increases?
• Who benefits, and who might bear unintended costs?

Attend to the larger system within which design takes place. In our study, we found that processes and structures can greatly enable or limit the agency of design thinking teams. For example, in several companies, KPIs, performance targets, and rewards that prioritized time to market constrained design teams’ ability to consider diverse stakeholders and potential harms. Similarly, business models optimized for maximizing online engagement hindered design thinking teams’ ability to consider downstream harms, particularly among young people. To counter such issues, ethical considerations should be built into strategic discussions — about the business model, competitive strategy, and key objectives, for example. Decisions about user interfaces and product features must not be seen as purely operational. Leaders must promote psychological safety for designers and developers so that they feel free to speak up about potential product harms.

When considering the broad context, ask these three questions:

• What behaviors do our KPIs and incentives reward?
• What harms are invisible in our dashboards?
• Can teams raise ethical concerns without penalty?

Pause before scaling. Before a product launch, assess the full spectrum of benefits and harms across multiple stakeholders and then decide whether to proceed. In addition to reviewing the product against its initial purpose, leaders should evaluate it against ethical criteria: Does it respect relevant rights and duties? What are its most consequential downstream effects? Does it nurture context-sensitive relationships and responsibilities?

In our study, several experts noted how difficult it is to anticipate unintended consequences before a product reaches scale. AI tools can assist by simulating edge cases, modeling malicious-use scenarios, and stress-testing assumptions. For example, a team developing a content recommendation feed could, before product launch, generate synthetic user profiles across age groups and other risk factors and stress-test whether the system disproportionately steers some users toward content that is extremist or is disinformation, for example. If it does, the team could require mitigation (such as friction, downranking, or guardrails) as a condition of launch.

Discussions of scaling risk might consider the following questions:

• What happens if a relatively small percentage of users misapplies this feature at scale?
• Have we stress-tested edge cases and categories of vulnerable users?
• Who signs off on scaling risk?

As products, services, and algorithms become more sophisticated and more complex at scale — reaching more people faster — the cost of getting things wrong shifts from inconvenience to measurable harm and legal liability. A more exacting approach may add time and cost upfront but could reduce downstream consequences such as reputational damage, product recalls, in-market fixes, and litigation. The question is not whether leaders can afford diligence but whether they can afford to postpone it. In some industries, those risks are no longer hypothetical — they’re already being tested in court.

In an unpredictable economy and fractured media landscape, marketing leaders are navigating a period of profound transformation and disruption — and as AI technologies evolve, the pace of change will only increase. In response, the highest priorities of chief marketing officers today are shifting, and understanding their concerns is essential to addressing the most pressing growth challenges companies face.

Marketing is at an inflection point. The structures, talent models, measurement frameworks, and influence mechanisms that will define the function for the next decade are being established right now. CMOs who move with intention — building the function, proving its value, leading effectively through disruption — will define what marketing becomes. Those who wait may find that it has already been defined for them.

Recently, I conducted 15 in-depth interviews with global CMOs from a range of industries to identify their most urgent priorities and challenges. I heard five main concerns:

• Embedding marketing as an executive-level growth function
• Transforming with and for artificial intelligence
• Hiring to defuse the talent time bomb
• Reaching consumers across a fragmented media system
• Developing new metrics for zero-click AI platforms

Compared with other business functions, marketing as a whole, along with even the most granular of measurement tasks, is perhaps being transformed most deeply by the rise of AI. While some sectors are establishing new AI workflows to speed up whatever they already do, marketing is working to harness AI not just as an efficiency tool but as a fundamental reinvention of the function.

In some ways, its very task has changed. Zero-click platforms have upended the meaning of what was, until recently, the web’s most basic metric: clicks. Meanwhile, the media landscape is growing ever more fractionalized, rewriting the rules of attention as consumers encounter individualized feeds algorithmically tailored to them. Function leaders face the challenge of developing talent for a future where the skills that mattered yesterday are being rapidly displaced. An additional challenge is to elevate the role and importance of marketing as a company’s growth engine, which will require tomorrow’s CMOs to develop new skills, lead an effective organization, transform the work, and create more pronounced value. This isn’t about learning new tools; it’s about designing more effective work in a more effective organization.

Together, these five priorities describe a function at a critical point — where marketing leaders are clear-eyed about the challenges ahead and increasingly convinced that marketing’s moment to shape enterprise strategy has arrived.

Evolving From Marketing to Growth

CMOs who operate as marketing specialists rather than general managers are often excluded from upstream product and business decisions — a marginalization that keeps them from delivering the impact that marketing should have. “If marketing isn’t involved when a company is identifying new products and services and isn’t helping bring insights to the table about what customers and future customers want or don’t want, then you end up getting a product that you can’t sell,” Amy Martin Ziegenfuss, CMO of Six Flags Entertainment Corporation, said.

CMOs must be part of higher-level conversations to drive their companies’ success and increase their own influence, and to shift the perception of marketing from a cost center to a growth driver. Julie Nestor, executive vice president of marketing and communications for Mastercard Asia Pacific, described a previous employer’s “investment optimization tool that calculated the ROI for every marketing request before it was approved. It removed subjectivity entirely and created a shared language between marketing and finance. Marketing was no longer asking for [a] budget; it was presenting a business case with a projected return.”

As John Costello, a former board member of Ring Inc. and president of global marketing and innovation at Dunkin’ Brands said, “Changing the title from chief marketing officer to chief growth officer would help all the constituents understand what the role is actually for.”

Implementing AI or Altering the Function?

Nearly every CMO holds a dual view on AI: enthusiasm about its potential, and genuine anxiety about their organization’s ability to capitalize on it. Experimentation is ongoing, but effective scaling of AI has so far remained elusive. CMOs pinpoint two primary internal challenges of AI implementation: identifying the tools that can drive efficiency, speed content production, and improve personalization; and getting teams to adopt them.

Sara Mendez, formerly CMO of SC Johnson Lifestyle Brands, said that marketing is the function to be “most transformed by AI, because it is the most complex, the most connected to every other part of the organization, and AI is simultaneously transforming every individual discipline within it.”

Another CMO mapped over 700 marketing tasks and found that more than 80% could be AI-led. But the limits of what AI can do are unknown and constantly changing as AI tools evolve. A financial services CMO put it bluntly: “Execution, machines can do. Creative thinking — yet to be proven.”

The implication is not just greater efficiency but a full reinvention of how marketing functions are organized, staffed, and run. One CMO reduced a 50-step campaign process to nine steps by mapping AI across her organization’s workflows and rewriting every job description accordingly, yet she still pondered what the “right” organizational design would be in 2030.

Transforming Talent Priorities

The skills AI demands don’t yet match the pipeline — a talent time bomb spanning industries and professions. One CMO asked, “Who am I going to hire on my marketing team — HTML coders, content writers, creative designers, copywriters, media planners? Or do I need people who know how to think?”

Teresa Barreira, global CMO of Publicis Sapient, noted, “When we mapped what only humans could do, we landed on soft skills: plasticity, storytelling, being a great spotter of things. Then the question is, how do you teach young people that?”

CMOs in particular are caught in a three-way bind as they confront skill obsolescence, as the channel-specialist model is displaced by AI and demand shifts toward judgment and critical thinking; pipeline anxiety, as execution-level work is commoditized and marketing risks losing its appeal to the best young talent; and the build-for-today paradox, where CMOs are making staffing decisions now without knowing what their organization will need in two or three years.

“CMOs will have to figure out how to manage hybrid teams — human and AI working together. And, critically, how do you manage AI when your people haven’t done it themselves?” asked Michelle Froah, former global chief marketing and innovation officer at ETS. One CMO noted that young marketers can now gain years of experience faster through AI agents but lack the judgment to lead them, because they haven’t learned what “good” looks like.

Reaching Consumers in a Fragmented Media World

The old model of reaching a broad audience efficiently is gone. CMOs are navigating a splintered media landscape — one that includes connected TV, retail media networks, podcasts, influencer ecosystems, and AI-powered search — and face the ongoing challenge of keeping up with new platforms as they emerge. Each platform grabs further attention with individualized feeds algorithmically tailored for each audience member. And integrating the disparate media to provide a superior consumer experience is becoming increasingly challenging.

“Creating a seamless and frictionless and integrated experience for a consumer is one of the biggest challenges. Companies stitch the pieces of communications, commerce, and marketing together and try to portray one single, seamless experience, but it does not look like Michelangelo’s David. It looks much more like Frankenstein,” suggested Luis Di Como, former executive vice president of global media at Unilever.

The emergence of AI agents making purchasing decisions on behalf of consumers compounds the problem and raises a fundamental question: Who, exactly, is the marketer marketing to? As Noha Abdalla, CMO of Choice Hotels, said, “[AI] agents don’t watch TV, so maybe it doesn’t matter anymore, if the agent is picking for you. Are the end consumers part of the loyalty program, or are the agents part of the loyalty program?”

The dissolution of a clear audience, in conjunction with a shifting set of platforms with obscure priorities, has created a pressure chamber for marketers. Abdalla suggested that an unclear media system greatly broadens marketing’s remit. “How, as a marketer, can I influence what is shown to consumers in their agent and LLM interactions? It used to be, I had to create a breakthrough ad campaign. But now maybe my job is to make sure that the product experience, the customer experience, and the reviews are the best they possibly can be, because that’s what’s going to influence the [AI’s] decision-making.”

Measurement Challenges and As-Yet-Undetermined Metrics

Measurement challenges are nothing new to marketers. Even before AI, the ROI on social media was difficult to calculate. Marketing mix modeling (MMM) is a statistical technique for estimating how much each marketing input — TV, digital, social, promotions, pricing, and so on — contributes to a business outcome, such as sales or revenue, while also accounting for external factors, such as seasonality, competitor activity, or macroeconomic conditions.

Andrea Zaretsky, CMO of Morgan Stanley Wealth Management, suggested, “If I could wave a magic wand, I would take away the walled gardens so we can understand the return of our dollars on social media as well as receive comparable measurement and methodology from across the key platforms. Further, MMM takes three months to assess performance — three months in today’s real-time world. There really hasn’t been enough innovation in this area in many years.”

A major lag in the availability of this data dramatically slows decision-making around how a marketing function should adapt its approach. And beyond the perennial marketing measurement question, identifying the ROI of AI is a much tougher challenge. In a zero-click AI landscape, what, exactly, is to be measured, and how? As the field works to establish a meaningful mechanism to measure the reach of AI, marketers must synthesize inconsistent data and standardize measurements — a seemingly impossible task, given AI models’ dynamic black-box algorithms.

Today’s CMOs are building organizations in a dramatically changing business environment and developing talent for a future moving faster than the hiring process. As Greg Stuart, CEO of the Marketing + Media Alliance, said, “They are navigating a world being reshaped by AI, political polarization, and media fragmentation at a speed that makes last year’s playbook obsolete.”

But these CMOs are not waiting for clarity before acting. They can’t afford to. They are experimenting, iterating, and embracing change as an operating condition rather than an obstacle. The ones moving fastest are those who have accepted that speed itself is a competitive advantage — and moving forward imperfectly is better than planning perfectly.

Corporate sustainability is facing headwinds. Net-zero pledges are being quietly walked back. Regulatory pressure is loosening in some parts of the world. Shareholders are demanding stronger business cases. Inside companies, sustainability leaders sometimes spend more time defending their function than expanding it. The familiar question “Where is the value?” has returned with a sharper edge, often accompanied by an implicit either/or framing: profit or purpose.

In most organizations, sustainability transformations don’t dramatically fail. Rather, they lose their edge. Goals get softened; ambition narrows. The original aspiration is often still in the strategy deck, yet the day-to-day decisions begin to look like the ones the company would have made regardless of its sustainability plans. Understanding why this happens in corporations is difficult because the process often unfolds gradually and becomes visible only much later. The same tension between commercial viability and sustainability ambition plays out more quickly and visibly in early-stage sustainable or social impact ventures, where teams face similar pressures to reconcile profit and purpose but have less room to postpone difficult choices.

Over more than two years, in real time, we tracked six startups targeting sustainability or social impact, from the first idea through to either reaching proof of concept or collapsing. The research was published in Strategic Entrepreneurship Journal.1 We expected to find that the difference between success and failure came down to strategy, market timing, or capital. What we found instead points to something less obvious, and potentially useful for managers leading sustainability transformations in established companies.

The Pattern Behind the Drift

The conventional explanation for sustainability transformations losing momentum is that conviction wavers under pressure, such as the impetus to make quarterly numbers. There is some truth in this, but it overlooks how often the retreat is structurally encoded long before the pressure arrives.

In our study, three of the six ventures we followed ended in collapse. The founders rarely identified a specific moment of compromise that led to failure. The CEO of a failed mental health venture traced it to the team’s early idealism: “We have a very new product, which was launched with very high standards and high idealism, without saying ‘we grow on the go’ and [starting] with a simple, small product to make money first.” The realization that there was no viable business model came late. By the time it arrived, the venture was past the point where it could simplify its approach.

Strikingly, the same fate awaited a second team that, on the surface, took the opposite approach. Where the idealists committed too completely to achieving an ambitious sustainability impact, the pragmatists committed too completely to developing a business case before anything else. One of the pragmatic-minded founders told us, “Frankly speaking, we want to become rich with it. I also want to have my island in the Maldives.” The team agreed early to scale first and add social impact later, but they never got to the impact piece. The sequential plan (first survive, then pursue purpose) turned out to be one stop on a multileg trip. The second leg never happened.

Both companies failed, and in both, the team was internally aligned. The problem was that everyone shared a single, one-sided way of framing profit and purpose, with no one to push the alternate view.

The Cognitive Variable

The pattern that distinguished the ventures that built viable, mission-aligned business models from those that didn’t was not primarily strategy, market, or money. It was how the team thought.

The teams that struggled, whether they were idealistic or pragmatic, treated profit and purpose as a trade-off. They tended to think in either/or terms, which led them to tackle first one goal, then the other. In contrast, the teams that built durable models held the two objectives in tension, maintaining both/and thinking (a paradox mindset).2 They didn’t resolve the tension; they worked through it, decision by decision. They were slower to commit to a fixed business model, developed simpler early prototypes, and were more willing to engage stakeholders whose feedback complicated their plans. From the outside, the second group of companies sometimes looked indecisive. But taking time to reason through their decisions turned out to be the source of their later flexibility.

Why does this matter for established companies pursuing sustainability transformation? Research on corporate sustainability has long argued that managers’ cognitive frames shape how they perceive and respond to sustainability tensions.3 Our findings suggest that this is not just specific to individual managers but also a property of their teams. The cognitive composition of the team running a sustainability transformation, typically a small group reporting to the C-suite or with a C-level member at its core, is the corporate equivalent of a founding team. They make the early structural decisions that determine the transformation process. They define what the sustainability function does and doesn’t do. They set the metrics and ambition level. They choose the language. And whatever logic dominates their thinking gets imprinted into the organizational architecture they build.

This early founding logic is what most companies underestimate. In our cases, the early choices the teams made based on how they thought (what to commit to, how complex to make the offering, which trade-offs to accept) became increasingly hard to reverse as their venture moved forward. Each implementation step locked in the assumptions of the previous one. The way these early conditions get baked into the structure and persist is what management researchers call structural imprinting.4 By the time financial pressure or stakeholder feedback signaled that the model wasn’t working, the team had less room to maneuver than they had realized. The structural lock-in was already in place.

The same dynamic often plays out in corporate sustainability functions. The KPIs chosen and the targets set in the beginning often frame what counts as progress for the coming years. The reporting structure that’s set up can influence which conversations happen and which don’t. The team that’s assembled affects what gets noticed in the first place. Most of these early decisions feel small at the time. They are not. And all of these small, early choices are, to some extent, a function of how a leader thinks: either/or or both/and.

Why Today’s Risk Is Asymmetric

In principle, both ends of the spectrum are dangerous. A purely idealistic transformation team can over-engineer the program, refuse useful compromises, and produce something the rest of the business cannot absorb. A purely pragmatic team can quietly hollow out the more aspirational ambition until what remains is business as usual, just rebranded.

The gravity in most established companies pulls in one predictable direction. The ambient pressure is to soften, defer, and de-prioritize the purpose part. With sustainability under sharper economic and political scrutiny, and with the regulatory and reputational tailwinds of the late 2010s replaced by harder questions about return on investment, the dominant internal voice is the one asking for the business case. That voice is not wrong to ask; the questions are legitimate. But if it is the only voice within the transformation team, the early imprinting tilts toward pragmatism. And pragmatism, once imprinted, doesn’t tend to course-correct toward purpose. Our data on the sequential “first survive, then pursue purpose” path suggests that the second part doesn’t reliably arrive.

The implication is that stronger conviction at the top alone is not enough to protect against drift. Divergent thinking, conflict, and productive friction within the team driving the work are also needed.

Three Things Managers Can Do

Drawing on the patterns we saw in the ventures that managed to get off of failing paths, we identified three practical implications for sustainability transformation leaders and the teams driving them.

1. Select team members for cognitive friction, not consensus. The most viable teams in our study held real tension internally. They were not comfortable, but they were productive. One health-tech CEO described a recurring debate between a psychologist on the team who insisted that the assessment instrument they were offering needed to be more rigorous, and a colleague who insisted that it didn’t matter as long as it sold. Neither of them was entirely right, but together, they kept the model honest.

That means a critical question to ask when assembling a sustainability transformation team is “Does this person disagree to some extent, productively, with the rest of the team about what we are here to do?”

2. Build in reality checks before pressure forces them. In our research, the teams that managed to find alternatives to failing paths almost always did so because of input they had not asked for. A new colleague joined and reframed an assumption. An accelerator coach pointed out that the commercial model didn’t work. A stakeholder responded to a prototype in a way the team had not expected. These interventions could be considered because they came before structural lock-in had narrowed the team’s choices.

Corporate transformation teams likewise should create opportunities for reality checks that don’t depend on a crisis to surface. They should consult standing advisory groups and customer or stakeholder panels early, while they are still developing plans, rather than going to them only later to seek approval. These interventions might feel inefficient when things are going well, but they could be the inefficiencies that buy flexibility later.

While it can seem like more governance, these reality checks add the kind of friction that an external-pressure event would otherwise force on the sustainability team under worse conditions (often when it is too late).

3. Keep early architecture simple enough to flex. The teams in our study that survived shared a pattern: While they embraced complex both/and thinking, they were strikingly cautious about complexity in the beginning. They prototyped simply. They committed late. They added scope only when stakeholder feedback warranted it. In contrast, the teams that failed (both idealists and pragmatists) went big early. They built elaborate offerings before they understood what worked. In other words, the teams that could hold the most complexity in their thinking were often the most disciplined about keeping their early moves simple.

For corporate sustainability functions, this translates into a heuristic: Build the simplest version of the program in the first place that still credibly addresses the core mission. Resist the urge to ship a flagship initiative that bundles five priorities into one. Each priority that’s added early constrains the next decision that can be made.

The pressures on corporate sustainability won’t ease in the near term. But the three levers we learned from observing early sustainability and social impact ventures (selecting team members for cognitive friction, building in early reality checks, and keeping initial architecture simple) can facilitate both/and thinking in a transformation team when pressure pulls toward either/or — either profit or purpose. Taking team composition and its internal dynamics seriously from the start helps a transformation keep its edge.

Businesses are striving to adapt ever faster to keep pace with rapid change, and yet B2B marketing practices have remained surprisingly static. Sure, the tactics have shifted to digital executions, and the use of data has made targeting B2B buyers more precise, but marketers remain rooted in fundamentally flawed assumptions about the corporate buying process.

For far too long, marketing leaders have operated on the belief that B2B purchasing decisions are almost entirely rational, driven primarily by product-feature superiority and competitive pricing. However, this conventional wisdom supposes that buyers live a Dr. Jekyll and — let’s say — Mr. Spock existence. In their private lives, they are fully formed human beings subject to all the cognitive and affective influence that B2C marketing wields to shape personal buying decisions. But when they step into the office, they become emotionless, like the famous half-Vulcan first officer of the starship Enterprise, and all of their decisions are informed solely by logic. This assumption undergirds nearly all B2B marketing efforts.

Nevertheless, the truth is that B2B buying is far more emotional and socially influenced than C-suite leaders have accepted. The same emotional triggers, social forces, and cognitive biases that influence us in our personal lives remain in play in our professional lives as levers that marketers can use to shape B2B purchasing decisions.

This shift in understanding isn’t just relevant for marketers in exclusively B2B settings; it’s a crucial insight for CMOs across industries, who often have at least one segment of business customers. Netflix must simultaneously attract individual subscribers and court advertisers; Google markets consumer-facing search along with enterprise cloud solutions. Even educational institutions like the University of Michigan’s Ross School of Business, where I teach, must appeal to both individual students for its MBA program and corporate partners for its executive education offerings. In each case, the underlying principles of how to influence decisions are similar, but marketers are unlikely to use similar tactics.

Of course, marketing and go-to-market strategies are going to be different for B2B than for B2Cs, even if they work on similar leverage points, because the buying processes are so different — and, as we’ll discuss, more relationally complex for B2B. To uncover the human drivers that influence purchasing decisions, I partnered with Mimi Turner, head of marketplace innovations at LinkedIn, and Jann Schwarz, senior director of marketplace innovation and strategies at LinkedIn and founder of its B2B Institute, on a research study. We surveyed 750 senior B2B buyers responsible for large-scale, enterprise purchases about their purchasing decisions. Forty-three percent were at the vice president level or above, and 41% were in organizations that had over 10,000 employees and were engaged in billion-dollar deals. Our exploration focused on what gets purchased and why once a short list of brands has been narrowed down to a final consideration set.

Our research revealed that buying decisions are heavily influenced by individuals who are not the nominal purchaser. They may be executives, such as the COO or CFO, or staff members from procurement. While B2B marketers typically target the known prospect or lead who has technical experience and domain expertise relevant to the purchase, these other individuals typically lack nuanced understanding of the product value propositions under consideration. These hidden buyers don’t download white papers or attend webinars and are more or less invisible in terms of general B2B marketing signals. They don’t turn up on lead sheets or pipeline trackers. Yet their perception of brand contributes to up to 50% of the buying decision, according to our research. Understanding and swaying these hidden influencers is critical to driving B2B purchasing.

We also found that a contributing factor to B2B purchasing decisions was how defensible the decision was. For instance, if a product bought from an established, reputable company performs poorly, the buyer is unlikely to be blamed, whereas if a purchase is made from a young startup, the buyer’s judgment may be questioned. As the old business adage went, “Nobody ever got fired for buying IBM.” That means the more defensible option often wins, even if a competing vendor offers a better value proposition. According to additional interviews we had with buyers, it’s also easier to get the more defensible option through procurement. This means that brand reputation matters, perhaps even more so than performance claims, once the short list has been narrowed to vendors that meet the buyer’s technical requirements. In fact, 81% of respondents said that in purchasing situations, almost everyone with a voice in the decision knew the brand that was ultimately selected. That makes brand marketing, which typically focuses on emotions and values, essential to B2B sales.

Lastly, we found that the confidence to make a purchase in a B2B context — what we call buyability — is significantly influenced by the extent to which the vendor is culturally aligned with the purchasing company. B2B buyers want to make sure that the company they’re buying from is not just good but also aligned with the working styles and priorities of their company. For instance, Patagonia, known for its strong environmental stance, discontinued its business of emblazoning its merchandise with the logos of hedge funds and tech companies, because it regarded them as contributors to planetary degradation. That means B2B marketers have to communicate not only product specs and value but also how the organization sees the world and how it acts within it.

The upshot? C-suite leaders must rethink transactional B2B marketing approaches that focus on product features and take steps to understand purchasing culture at buyer organizations. They must speak not only to nominal buyers but to unseen stakeholders that ultimately drive buying in a B2B context, and they must understand what those stakeholders value and how to influence them. Doing this well is a more significant determinant of who wins a deal than whether a product is cheaper, faster, or more efficient. Investing in brand marketing that communicates a company’s culture and values matters as much in B2B as it does in B2C.

Dry rot. A Potemkin village. The Wizard of Oz.

What do those things have in common? In each case, they may look good on the surface, but it’s only an illusion. Wood afflicted with dry rot looks just fine until the tree it’s in topples down. Grigory Potemkin is said to have tried to impress Catherine the Great by creating a prosperous Crimean village constructed only of building facades, masking the real town’s crippling poverty. The Wizard of Oz looked great and powerful, until the cowering man behind the curtain was revealed.

Could artificial intelligence do the same to organizations — create an impressive, seamless exterior even as capability completely falls apart inside?

This alarming possibility was raised by the AI experts we interviewed for a joint Anthrome Insight-Axialent study on AI’s impact on behavior and culture inside organizations. Even as those experts uniformly cited AI’s potential to transform work for the better, they cautioned that AI adoption risks unintentionally creating an organizational mirage: workplaces that appear highly capable while people’s real skills quietly erode beneath polished AI-generated output.

Perhaps even more worrisome: When we can no longer reliably tell who truly knows what, the interpersonal trust that teams depend on to function also crumbles.

What are the early signs that the mirage effect is already forming in your organization — and what does it look like when it takes hold? How can leaders avoid the mirage effect and make sure that in the AI age, their people and organizations are as capable as they appear?

Capability Mirages: The Early Signs and Troubling Possibilities

An AI-driven illusion of competence, hiding the absence of genuine understanding, is already appearing in some organizations, the AI experts told us. Stephanie Antonian, founder and CEO of AI product development company Aestora, explained how this can play out in dangerous ways for leaders and organizations: “The upside [of AI tools] is that everyone can produce a level of work that’s pretty good for basic tasks. … It looks pretty good. But then you don’t know what’s underneath it, how resilient that piece of work is, or whether it’s going to give you an additional liability.”

After all, AI is not necessarily an improver of work but, rather, an amplifier. As AI Business Impact CEO Gábor Szórád observed, “At the end of the day, if you are a fantastic software engineer or a great manager, AI allows you to do more with the same energy. If you’re a bad one, you’re just going to create more crappy instructions, longer ones, more bad ideas. It just magnifies whatever you put in.”

The tech vendor marketing narrative stating that AI makes individuals more capable just compounds the mirage issue. In some situations, AI does enable the production of better short-term outputs — but it also breaks the historical link between strong output and strong capability, causing teams to lose sight of who actually possesses strong skills.

Moreover, AI itself doesn’t know when it’s out of its depth, said Amir Michael, professor of accounting and deputy executive dean for executive and professional education at Durham University. Likewise, Antonian observed, people who aren’t capable in a particular subject area can’t spot where AI-generated work has gone wrong — unlike people with subject-matter expertise. They may pass on bad output to others who also can’t tell the difference.

The mechanism through which professional mastery has always been built — productive struggle, error-based learning, the slow accumulation of genuine judgment — may be quietly bypassed before many people realize what is being lost.

But there is a second, less visible problem that, in the long run, may be the more dangerous one: Not everyone is self-aware enough to notice their own skills eroding. There is no single individual — among managers, colleagues, or clients — who can do an accurate, real-time read on how capable an organization is, overall. Individual skills and collective capability could weaken long before anyone notices, and there is absolutely no guarantee that even the best-functioning AI could begin to fill the gap. Leaders should be especially concerned about losses in the sophisticated, critically necessary “muscle of critical thinking,” said Albert Durig, cofounder and partner at Triviam Consulting.

This situation results in a direct and damaging consequence for organizational trust. Teams have traditionally functioned based on knowing, or at least being able to calibrate, who knows what. That calibration determines whose judgment should be relied upon, how managers identify who is ready for greater responsibility, and how organizations know what they are truly good at. When AI makes that calibration unreliable, trust erodes with it. When AI-assisted work is later discovered to have been misrepresented, the trust collapse tends to be swift: “The impact on trust is 0 [doubt] to 100,” said Elisa Farri, vice president at Capgemini Invent Management Lab.

Concerned yet? We all should be.

But if leaders act now, they can avoid organizational dry rot — and maintain true capability fueled by humans and machines alike.

How to Preserve Human Capability and Team Trust

Let’s explore what the experts we spoke with had to say.

1. Choose purpose and business goals over an AI-first mentality.

It’s trendy to announce that your company is thinking “AI first.” But it’s not what these experts would recommend. Antonian put it directly: “When you go AI-first, you have already told your organization it’s not human-first.” That signal, once received, is hard to unsend — and at a moment when people are already anxious about their relevance and job security, it can quietly erode the trust that makes teams function.

One potential outcome of an AI-first future is an unpleasant inversion of the roles of human and machine, said one senior AI executive at a Fortune 500 company. They see a concrete risk that people might let AI do the reasoning, interpreting, and responding only to become “transactional tools” themselves.

“AI can be the lead,” Michael noted. “That’s the problem. As long as AI is your follower — it follows your requests, your orders — we’re fine. The time that AI jumps to be your lead, that’s the downturn.”

AI-first thinking, in the view of our experts, causes people to lose perspective on the utility of AI as a tool — and to deploy AI in comically inappropriate settings. Remember the old saw “If you’re a hammer, everything looks like a nail”? That applies to AI-first: “You don’t walk around the house, holding the biggest drill that you have, asking people if they need their coffee stirred,” said Andrea Jones-Rooy, a data scientist, organizational researcher, and visiting associate professor at New York University’s Center for Data Science.

Even when AI is used for more seemingly appropriate ends, such as measurement and KPIs, Durig noted, it can cause a dominance of measurement over meaning. That leads to “a performance culture without purpose,” he said.

On the flip side, when purpose comes first, enabled by AI tools, our experts see the potential for true progress and even stark disruption. With AI enablement, “Small groups of people that get together for a specific purpose may outperform corporations because they are more nimble, flexible, fast-moving,” said AI entrepreneur Thierry Kahane. The key, in Kahane’s vision, is cohesion around a goal versus a technology.

What concrete leadership steps fuel this mission? First, you should check your own rhetoric. If discussion of AI is eclipsing dialogue around business outcomes, the conversation is framed incorrectly; people are more likely to become passive, let their skills slip, and quietly lose faith in their own relevance. Similarly, if the only voices you’re hearing in the AI conversation are those of people who are passionate about the technology, the balance of business-purpose versus tool is likely off.

Finally, it’s critical to routinely audit how people are operating AI tools on the ground. Is AI making decisions that humans should be making? To ensure that an organization is operating “business first,” leaders and teams must exercise constant vigilance around day-to-day AI use. Otherwise, human capability is destined to slide … and we won’t know until a black swan event happens that AI, trained on typical data, is ill-equipped to handle it.

2. Leaders should model AI usage specifics.

If you don’t want people to switch off their judgment, critical thinking, and intrinsic motivation, you must teach them how to engage with AI as a sparring partner rather than as a delegation tool, Farri said. People need to engage in active, back-and-forth interaction with AI —interrogating its suggestions, pushing back, and cocreating outputs.

And this behavior needs to start at the top. When leaders model active, curious, judgment-led AI use and do so visibly, this signals what the organization actually values far more powerfully than any guidance document can, Szórád said. “The project sponsor needs to be the CEO. The CEO needs to use AI daily,” he said.

That’s important advice at a time when many organizations have given employees only the barest clarity on how to utilize AI day to day. This may be well intentioned on those organizations’ parts; perhaps their leaders don’t want to stifle employee creativity. But thoughtful guidance on AI usage can walk the tightrope of specifying behavior without shutting down exploration. For example, organizations can distribute highly generic but intelligently framed prompts — “When I say _____, what am I not thinking of that I should consider?” — that keep the human in the driver’s seat.

The AI realm is still new enough that people genuinely need to be steered away from the wrong behaviors: Telling people what not to do is just as important as telling them what to do, Farri said. Caveats must be clearly communicated, she added. For example, you might say “The more flawless an AI output looks, the more ruthlessly you should stress-test it.”

3. Build human capabilities first, AI augmentation skills second.

MIT Sloan School of Management postdoctoral researcher Isabella Loaiza offered a deceptively simple principle that organizations are widely ignoring: “You need to learn first and then use a tool to supercharge your abilities.” One executive asked us to imagine what could happen when the sequence is reversed: “When someone with 20 years of professional experience uses AI to amplify their impact, that works. But what happens when a 20-year-old’s first interaction with work involves AI from day one? How do we develop that person at the same speed and depth? We will create a talent gap that will be hard to close.”

This has direct implications for how organizations design onboarding, early-career development, and role progression. One question you should ask now is not “How can we use AI to accelerate this person’s output?” but “What does this person need to genuinely understand before AI can help them go further?”

Performance measurement systems need to take this phenomenon into account too — not just asking “Is the work good?” but “Does this person understand it, and are they growing through producing it?” Measuring human-centered outcomes requires human-centered metrics, which most organizations have not yet built.

4. Restore accountability, and use it to rebuild trust.

Accountability is challenging in the best of times. Knowledge work, particularly, has long been slippery to ascribe: That’s due to both messy over-collaboration and, frankly, some bad, illegitimate-credit-taking behavior within teams. When you don’t know who does what, it’s hard to hold anyone accountable. Add in AI, and the accountability muddle gets even worse, with direct consequences for both skills and trust.

When a team member takes credit for work that an AI clearly performed, intra-team trust is naturally eroded, several people commented. But worse — and central to the issue of AI mirages — if a team is not sure how work is getting performed (that is, what mix of AI and human capability, exactly, is being deployed), it quickly becomes impossible for colleagues to understand each other’s skills. Without that visibility, the natural ways that teams maintain their collective skills will break down. More seasoned team members and managers won’t know who to coach.

The trust consequences extend further than most organizations realize. Kahane described a dynamic that he said is almost universal: employees deliberately concealing their use of AI to ascribe productivity gains to their own efforts — thus protecting manager and peer perceptions of their human performance. (Researchers are beginning to see this dynamic in academic settings as well. Students are concealing their AI use due to perceived taboos.

Daniel Strode, a professor at the IE School of Human Sciences and Technology, pointed out the vicious cycle this could create: Employees hide AI use; leadership expects efficiency gains that don’t materialize; neither side communicates effectively; and AI initiatives collapse under the weight of accumulated mistrust. The irony is sharp: The very tool meant to help organizations perform better becomes the source of the opacity that prevents them from understanding how they are actually performing.

Now you might say, “Shouldn’t that be OK? If the AI can do the work capably, who cares?” The issue, though, is that the AI can do the work capably until it cannot. And as previously noted, neither the humans involved nor the AI know those specific capability limits, which are masked by the polish of AI outputs. Losing early-warning signs of capability gaps only makes the eventual emergencies more dire. The solution here can be classified as “simple but not easy.” Cedric Wells, head of IT innovation and new technologies at Gorilla Glue, framed it practically: “Set clear ownership and verification norms.” Who produced the work? What role did AI play? Who is accountable if it is wrong?

Today’s AI technology has raised important questions about the basics of how we get work done — especially around who (human or technology) has the capability to do what. Individual-skills erosion, and collective capability erosion, could damage organizations irreparably. If AI tools are both contributing to human deskilling and concealing that fact, they could speed the collapse of many organizations. The experts we spoke to were alarmed, but they were also hopeful that leaders will step up to the challenges.

If leaders coach teams to use AI thoughtfully, accountably, and with real purpose, the mirage could become the reality: Organizational capability could meaningfully increase and human skills could grow too. We can be our best selves if we lead technology and are not led by it.

A vice president of product opens her laptop on a Monday morning to find that the AI model her team had worked with for the past six weeks to build a customer workflow has been leapfrogged by a cheaper, faster alternative. Again. Her Slack feed is blowing up with links to the announcement. The CEO has already forwarded an article about what a competitor is doing with the new tool, with the subject line “FYI.” She hasn’t finished rolling out the last integration, and now she’s wondering whether to scrap it. She is not resistant to AI. She is worn out by it.

Most leaders look at this picture and see an execution problem: The organization wasn’t moving fast enough. The cautionary tale that reinforces that instinct is Chegg, the education company whose market capitalization collapsed when the launch of AI-powered alternatives rendered its core tutoring model obsolete. The lesson everyone has drawn is obvious: Move fast or die. So leaders push harder, with more pilots, more mandates, and a constant drumbeat of urgency.

But that lesson, taken too literally, backfires. Bracing only against the danger of moving too slowly, leaders managing AI adoption underestimate a quieter risk: that they will wear out their organizations by racing toward a finish line that does not exist. The old playbook was built for disruptions that end, and its instincts (move faster, push harder, wait for things to settle) become liabilities when there is no end state. Leaders who optimize for speed alone will lose to those who build for endurance as well.

What follows is a reframing and a set of emerging practices for leading through an AI disruption that will not settle.

From Process to Permanent Condition

Research on disruption has been circling this problem for years. One influential strand that one of us (Rory) developed with Clayton Christensen and Michael Raynor pressed on the point that disruption is a process, not an event. The recurring incumbent error is to judge the threat by where it stands rather than where it is heading. Yet, even correcting for this carries a quiet assumption that the threat’s trajectory has an ultimate destination. After Netflix disrupted Blockbuster, streaming became the new normal. Each wave of new technology ran turbulently for a while and then hardened into arrangements a company could see and plan around.

AI changes this dynamic. With previous technologies, the entrant’s advantage grew because something outside it improved: Components got cheaper, networks got faster, supply chains got better. AI is increasingly self-improving. Each generation helps train and build the next, so the distance between waves keeps shrinking. There is no settled position to plan toward, because the core keeps extending and the old barriers to disruption keep falling. We have come to call this condition steady-state disruption: a context in which capability shifts arrive continuously and accelerate one another, with no equilibrium in sight.

Managers and scholars already have language to describe turbulent environments. They talk about VUCA (volatility, uncertainty, complexity, and ambiguity) and about the dynamic capabilities a company needs to sense change and adapt. But that vocabulary assumes that the turbulence eventually breaks: A period of upheaval is followed by a return to relative calm. Steady-state disruption is the condition in which the calm never comes.

If disruption is a process rather than a sequence of separate shocks, then the AI capabilities landing inside an organization are not a series of discrete events to be handled one at a time but an ongoing process. Companies that treat a continuous process as a string of episodes fatigue their employees and ultimately struggle to adapt.

The pace itself shows no sign of letting up. Stanford’s 2026 AI Index shows AI systems posting steadily higher scores on standard industry benchmarks. New frontier models appear every few months, and an industry investor reported that the leading model often holds its position for only a few weeks before a newer one or an open-source rival takes share.

This cadence is especially hard to absorb because the work never reaches a stopping point. As Airtable CEO Howie Liu has observed, AI adoption is unlike the move from desktop to mobile or from on-premises to cloud computing. Each of those shifts was a single, fairly foreseeable change in form, but with AI, every model release brings new capabilities and new patterns that have to be learned more or less from scratch. Even if this progress were to hit a sudden plateau, organizations would still need to spend years folding existing capabilities into their products, workflows, and decision-making.

Addressing the Human Cost

For a lot of people, the early excitement has curdled into something that’s harder to sustain. Recent research makes the cost concrete. One analysis found that AI tends to intensify rather than lighten individual workloads, piling on cognitive demand faster than it strips away drudgery. And Deloitte’s Global Human Capital Trends survey saw the same thing at the organization level. As collaboration with AI deepens, so, too, do burnout, loneliness, and overload.

These findings document strain on individuals, but the cause sits above the individual level. When a playbook written for episodic disruption no longer works, the organizational machinery that once absorbed shocks now transmits them directly to workers instead.

So, what should a change management toolkit for the age of continual AI disruption look like? A handful of practices that are provisional but useful are taking shape at leading companies. Instead of placing the burden of absorbing change onto individual employees, these practices move some portion of that burden onto the structure of the organization.

Practice 1: Build a Permanent AI Unit

Since 2022, a common response to the rapid rise of AI has been to form an AI committee — a group of people asked to advise, set direction, and evangelize on AI, usually on top of their existing jobs. Committees of this kind tend to add work rather than soak it up. Members are stretched thin, the rest of the organization gets only intermittent guidance, and the committee’s own queue keeps growing. Steady-state disruption calls for a sturdier, more permanent approach. Whether it is a full-time team at a large company or a carved-out slice of a few people’s time at a smaller one, the work of tracking, translating, and triaging AI’s churn should be somebody’s actual job rather than a standing favor.

Microsoft offers an illustration. The AI Center of Excellence inside Microsoft Digital began as an ordinary advisory group in 2023. But the group’s leader, Qingsu Wu, recalled that as adoption spread, so did duplicated effort, uneven governance, and gaps between strategy and implementation. The question, Wu said, shifted from “How do we help teams try AI?” to “How do we turn AI into consistent, measurable outcomes at scale?” The center became the place where AI work is coordinated, with a single idea intake pipeline, a hub for upstream architecture and security decisions, and the ability to see patterns where individuals and siloed teams cannot.

Once such a group has enough depth, the frontier-scanning activities and scrap-or-scale calls that used to land on scattered individuals become the standing remit of people equipped to handle them.

Practice 2: Run Two Clocks, Not One

Most organizations keep time on a single clock. Plans are quarterly, budgets are annual, and the all-hands meeting lands on its dependable schedule. Onto that steady rhythm, employees are now also being asked to ship AI experiments by the week, keep last quarter’s integrations running, follow a frontier that shifts constantly, and explain to leadership what any of it means for the business. Companies have always lived with some gap between fast work and slow work. AI has widened it past the point where one person can comfortably hold both ends.

The strain shows up first in product organizations, where the distance between weekly model releases and quarterly road maps is hardest to ignore. Airtable CEO Liu watched AI-native competitors shipping major capabilities every week while his own teams followed quarterly road maps. So he split the product organization into two groups, borrowing a distinction from psychologist Daniel Kahneman: A fast-thinking group ships AI capabilities on a near-weekly basis, while a slow-thinking group takes on the deliberate infrastructure bets — the kind of work that, as Liu put it, you cannot ship in a week via a “hacky prototype.” The two are meant to feed each other. The fast group surfaces new possibilities, and the slow group turns the promising ones into things the company can rely on. Without such a deliberate split to protect the slow clock, the faster clock becomes the standard against which everyone is measured.

Practice 3: Teach in the Flow of Work

Under constant change, most corporate training approaches, such as annual certifications or one-off workshops, fall behind almost as soon as they are delivered. Employees are left to keep pace with new developments on their own and end up concluding that the frontier is simply outrunning them. The feeling only deepens when the learning is stacked on top of a job that is already full.

Learning should instead be made continuous and specific to the role, delivered inside the work itself. There is good evidence that knowledge sticks better through short, repeated exposures spread over time than through one-off intensive sessions. Salesforce adopts this approach with its AI-powered internal platform, Career Connect, which reviews an employee’s existing skills, identifies the gaps between those skills and their aspirations, and serves up tailored learning opportunities (such as courses, stretch assignments, or mentorship) through Slack, where they already work. The organization’s Agentforce Learning Days add a recurring, companywide push on AI skill development specifically. What emerges is an architecture for AI fluency and ongoing development that is steadier and closer to the work, better aligning with the realities of steady-state disruption.

What complicates this approach when it comes to AI is that employees who fear being replaced by the technology have little incentive to engage with it seriously. CEO Greg Case at insurance and risk advisory firm Aon has addressed this fear directly. His bet is that AI will widen what the firm’s roughly 60,000 employees can do rather than substitute for them. He has built Aon’s investment in continuous AI fluency around that framing and has credibility with his employees for leading Aon through the pandemic without layoffs. Continuous learning requires continuous buy-in, and buy-in requires workers to believe that getting better at AI benefits them, not just the organization.

When training stops being a place employees go and becomes part of how they work — small and constant rather than disruptive and periodic — it keeps a workforce current without asking people to absorb the frontier on their own time.

The vice president of product we mentioned at the beginning was faced with a new-model announcement, a forwarded article in her inbox, and a newly built workflow that was already obsolete. This steady-state disruption is exhausting because the weight of all this change rests on her and her coworkers, with nothing in the organization’s design built to help them carry it.

A generation of managers learned that disruption was a phenomenon that eventually settled. AI shows no sign of settling. Leaders who keep treating it as a series of episodes will keep piling that weight onto their people. Those who build organizations designed to carry it, through permanent AI infrastructure, split cadences, and work that has learning embedded into it, will be the ones who endure.

How does a fossil fuel company become the world’s largest developer of offshore wind? How does a software company written off for missing the mobile revolution become one of the world’s most valuable companies in the age of AI? Ørsted and Microsoft have faced a puzzle familiar to many leaders: When technological, regulatory, and societal shifts redraw the basis of competition, reinvention is no longer optional, but the path forward is far from obvious.

Reinvention has been a key concern in business strategy. Stories of startups disrupting markets are compelling and often romanticized. But established companies do not have the benefit of a clean slate. Existing frameworks illuminate different parts of the problem: Disruptive innovation explains why incumbents get trapped, value shows how companies create new markets, and work on renewal and reinvention highlights the organizational barriers that make change so difficult.1

Leaders are faced with a dilemma in strategizing their way forward. Some companies double down on their known strengths: They keep innovating around the core and extend existing products, technologies, or business models into adjacent spaces. Kodak’s unwavering attachment to film demonstrates the risks of sticking to the legacy formula. Starting from a clean slate with a pivot to an entirely new business may seem the better option. But in doing so, a company risks discarding capabilities, relationships, and resources that could offer comparable advantage in new markets while also alienating internal and external stakeholders.

Consider Ørsted. In 2009, the oil and gas company, then known as DONG Energy, was also Denmark’s largest utility and biggest CO₂ emitter, with 85% of its power coming from fossil fuels. But its business was under pressure from multiple directions, including exposure to volatile swings in fossil fuel prices, and public opposition to building new coal-fired power plants.2 At the same time, the global energy transition created new opportunities. To DONG’s leadership, it was clear that the existing business would not be viable long term, environmentally or financially, and that renewables were the path forward.

The question was no longer whether to transform but how. DONG’s leadership looked inward. The company’s portfolio included the world’s first offshore wind farm. More importantly, decades of extracting petroleum in the North Sea had enabled it to build something harder to replicate: deep capabilities in large-scale infrastructure engineering, complex project development, and the logistics of operating in harsh marine environments. This became the foundation for renamed Ørsted’s 85/15 Black-to-Green strategy for inverting its portfolio mix to 85% renewables and 15% fossil fuels, scaling its offshore wind business, and achieving a 30-year target in only a decade.

Ørsted’s remarkable transformation exemplifies a recurring pattern that we observed in our research on corporate reinvention stories. Microsoft’s trajectory tells a similar one. After failing to respond to the mobile shift and then making an ill-fated $7.2 billion acquisition of Nokia’s devices and services business, Microsoft emerged from what many have called a lost decade to become a leader in enterprise cloud computing. Like Ørsted’s, this reinvention was anchored around something deep, durable, and transferable: Microsoft had the trust of enterprise CTOs, and its products were in almost every Fortune 500 company. Its enterprise business competencies, combined with its nascent cloud computing business, became the foundation for a growth story that resulted in a tenfold increase in the company’s valuation under CEO Satya Nadella.

This is the pattern we have observed among successful corporate reinventions. Companies that navigate structural disruption do not typically build from scratch, nor do they simply protect the core. They uncover a deep, embedded capability that already exists inside the organization, often built for one context but carrying latent value that can be directed toward a fundamentally new strategic purpose. These capabilities, which we call kernels of reinvention, are powerful anchors around which new businesses can be built.

Finding a Kernel of Reinvention

Evolutionary biology has a name for the process Ørsted, Microsoft, and similar companies have undergone: neofunctionalization, where a gene that evolved for one function acquires a novel function. In evolution, new functions do not always arise from entirely new structures. Organisms facing shifts in their environment can benefit from repurposing existing genetic material for new advantage under environmental pressure. For example, millions of years ago, as the Southern Ocean cooled, Antarctic zoarcid fish evolved an antifreeze protein gene through the neofunctionalization of another gene, allowing the fish to survive in waters where other creatures would have frozen.3

Sometimes the kernel of reinvention is technical or scientific: Fujifilm’s future did not lie in film itself but in the chemistry, materials science, and precision capabilities involved. BYD’s kernel was a deep competence in battery electrochemistry and power electronics that became central to electric vehicles and broader energy applications. Kernels can also be commercial and relational, such as Microsoft’s enterprise business capabilities and trusted relationships with enterprise customers.

Four characteristics distinguish a genuine kernel from wishful thinking about legacy assets:

Deep. The kernel is not what the company sells, builds, or is known for but something that sits beneath it — underlying capabilities or resources that make today’s business possible. It can be genuinely hard to see because companies need to look beyond their strategic position, through their products and architectures. In photography, the kernel was not film but the chemistry and precision-coating science beneath it. For example, the same science that kept film stable and protected from ultraviolet light could also be used to keep skin moisturized and protected from UV damage. Fujifilm harnessed it to move into new markets, such as cosmetics.

Generative. Kernels enable a company to pursue new value and competitive positions. In e-commerce, Walmart’s capabilities in grocery retailing, including perishables logistics, and a dense retail network that used its stores as last-mile fulfillment nodes, became a launchpad for online grocery sales — a segment that pure-play digital players had yet to crack at scale. Walmart integrated this kernel with new e-commerce capabilities to transform it into an omnichannel model that served as a wedge. Later, it borrowed from the Amazon playbook by layering on a third-party marketplace and advertising business.

Defensible. Kernels need to give a company something that rivals in the new market will struggle to build quickly. Microsoft’s enterprise advantage in cloud and AI rests on trust and operational dependency built over decades with Fortune 500 customers, the security integrations embedded in its procurement and compliance processes, and the developer ecosystem that compounds with every new product. New cloud entrants could match the underlying compute, but they could not come to market with a web of institutional relationships and embedded dependencies similar to what it had taken Microsoft years to build.

Coherent. Perhaps least obvious, a kernel should create a visible, unbroken narrative thread from the legacy business to the new direction. This helps make the reinvention story more credible to employees, investors, and partners because it builds from a position of advantage rather than competing from scratch in a completely new field. Fujifilm explicitly positioned its beauty product line as based on the science developed for film.4 Similarly, Ørsted faced resistance to moving away from coal among its own employees, so it framed offshore wind as building on transferable skills and capabilities.5

In some cases, the kernel may already be being expressed through a nascent side business, an overlooked capability, or an underleveraged growth platform. (See “How Companies Build on Kernels of Reinvention.”) Ørsted had fortuitously inherited a modest wind portfolio through a 2006 merger with six Danish energy companies. In 2003, BYD had acquired small Chinese automaker Qinchuan, which it ran quietly in parallel with its existing business for nearly a decade before electric vehicles became the dominant story. Microsoft similarly had a nascent but growing cloud business under Nadella that became the foundation for its growth.

The Reinvention Playbook

Recognizing kernels in retrospect is easier than identifying them in real time. Successful reinvention is not a single decision but a sequence of decisions and continuous adaptation over years. Ørsted’s transition took a decade, while BYD’s took two decades — from the time it acquired an automotive business to becoming the world’s largest electric vehicle maker. The playbook below maps five steps toward kernel-based reinventions.

Step 1: Diagnose the disruption. Start outside the company. What technological, regulatory, social, or economic shifts are changing the rules of the game? Which parts of the current business are being weakened, commoditized, or rendered less legitimate? If the basis of advantage is moving, leaders need to confront that early. DONG’s leadership saw that fossil fuel volatility, public opposition to coal, and a strengthening renewables agenda were not isolated signals but a moment when converging trends made action both necessary and credible. Taken together, they suggested a structural change in the energy market’s direction.

It is important to guard against misdiagnosing structural shifts as cyclical headwinds. Such misdiagnoses can lead companies to build on kernels that are too close to the legacy product or business model. Consider whether the change is threatening current performance or the company’s underlying relevance.

Step 2: Excavate and validate latent kernels iteratively. Leaders must be prepared to dig, looking beyond the core products or technologies it currently sells to uncover deep, latent capabilities and assess their value in new markets. These are deeply embedded capabilities, such as subsurface engineering in the oil and gas industry, which could readily transfer to wind energy initiatives. They could already be being expressed through peripheral businesses that are relatively small in scale and not considered core, such as DSM’s penicillin capabilities or Ørsted’s modest wind-power business.

Leaders should be wary of identifying false kernels at the level of current core products — for example, film versus chemicals and materials. Frameworks such as VRIO (value, rarity, inimitability, and organization), which invite leaders to consider whether a capability or resource provides sustainable competitive advantage based on if it is valuable, rare, difficult to imitate or substitute, and how well the business organized to exploit it, can help. But the key is to look beyond today’s products and assess value in new contexts through a deep, generative, defensible, and coherent lens. Kernel identification is not a single act of insight. It is an iterative loop of excavation, validation, market testing, and refinement.

Step 3: Lock in the advantage in a new growth engine. A kernel is a strong foundation for growth, but it is not a business on its own. Once leaders have identified a credible kernel, a new growth engine and competitive moat need to be built around it. That may require new partnerships, business models, capabilities, and talent. Ørsted built an offshore wind business around its core capabilities, including developing innovative new project development and financing models, supply chain partnerships, and an operating model designed for scale.

This step often requires a dramatic shift in competitive logic. For example, one of the biggest shifts in Microsoft’s reinvention was to embrace open-source software and ecosystem collaboration with competitors. That made its cloud offering more relevant and credible but was a dramatic break from its more closed, Windows-centric posture of the past. Given that a significant portion of cloud workloads were running open-source software like Linux, Microsoft needed to ensure that the Azure cloud computing offering would be competitive and to regain trust and relevance among developers.

Step 4: Manage the transition. In biology, neofunctionalization often begins with duplication: One gene copy continues the old function while the other is free to adapt under new environmental pressures. Companies need to follow a similar logic: Kernels must be given space to develop and adapt in a new context where they will encounter new competitive pressures and metrics, but they must also maintain ties to the existing business to take advantage of corporate assets. For example, Ørsted created a new wind power business unit but invited the engineers from the fossil fuel business to join the new endeavor.

Reinvention also does not mean abandoning the old business overnight. While startups have the advantage of clean slates, established companies have the benefit of channeling legacy cash flows to fund the pivot. Leaders must manage the overlap: sequencing exits and investments and preparing investors and employees for short-term dips in service of long-term growth. Reinvention fails when the new business is suffocated by the old, but it also fails when leaders cut away the old before the new is strong enough to stand on its own.

Step 5: Craft a narrative of continuity. While leaders may spot the need for change, other stakeholders, including investors, employees, customers, and partners, need to be brought along on the journey. A compelling reinvention story must signal a radical departure from the nonviable past while also maintaining continuity. The story must also be made tangible through visible proof, such as a new product, business unit, market entry, or operating commitment. This is where kernel-based reinvention shines, since there is a genuine connection to something deep that made the company great. Validation from outside also matters: Customers, partners, and analysts telling the new story makes the reinvention real in ways that internal communication alone cannot.

If the story sounds like “everything we were is now obsolete,” stakeholders may resist or disengage. On the other hand, if it sounds too similar to business as usual, the shift may not seem convincing enough. The best narratives make clear what must change and how the company is building from an existing advantage to gain a strong competitive position in the new arena. Done well, this gives stakeholders an opportunity to credibly reframe where the company is heading, on a path grounded on past strengths. Ørsted’s transition worked in part because it was framed as necessary for the company to stay financially and environmentally relevant yet still built on internal strengths honed over decades.6 This also gave employees a path toward transferrable skills in an exciting new sector that aligned with the aspiration of creating a cleaner, more sustainable future.

Finally, it is important to remember that reinvention is not a one-shot, linear path but rather an iterative journey of learning, discovery, and scaling what works.

It is tempting to view reinvention as a choice between defending the core and starting anew. But the strongest reinventions rarely fit either extreme. They begin by separating the legacy business from the deeper capabilities that made it possible. The former may need to shrink, be sold, or disappear. The latter may become the kernels of the next growth story.

Companies that thrive in the face of disruption will not be those that preserve the past intact nor those that discard it wholesale. They will be those that can identify their kernels of reinvention and give them new strategic functions in a changed world. For leaders, the hardest decision is not simply whether to reinvent. It is what to carry forward.

For the fifth year in a row, MIT Sloan Management Review and Boston Consulting Group (BCG) have assembled an international panel of AI experts that includes academics and practitioners to help us understand how responsible artificial intelligence is being implemented across organizations worldwide. In previous posts this year, we have explored artificial intelligence’s impact on the workforce, including why responsible AI requires more than training human experts to verify AI outputs.

This time, we asked our panel to react to the following provocation: Responsible governance that treats agents as autonomous decision makers will fail. On the surface, there is broad consensus, with a clear majority (72%) of our panelists agreeing or strongly agreeing with the statement. But digging deeper reveals a more nuanced conversation about what autonomy means, the relationship between autonomy and accountability, and what’s at risk when characterizing agents as “autonomous.” The picture that emerges: Calling agents autonomous decision makers risks allowing the humans and institutions behind them to evade responsibility for their actions. Effective governance, by contrast, ties every consequential decision back to a responsible party that can be held legally and morally accountable, in the context of a broader sociotechnical system.

Below, we share panelist insights and offer our practical recommendations for organizations thinking about agent autonomy through the lens of responsible AI governance.

Agents are increasingly “autonomous” — but only in an operational sense. Our experts acknowledge that agents are exhibiting a growing degree of technical or operational independence and ability to take action on their own. EnBW chief data officer Rainer Hoffmann says, “Agentic autonomy is real and growing,” while Renato Leite Monteiro, vice president of privacy, data protection, AI, and intellectual property at e&, observes that “self-improving agents are moving faster than we can map their failure modes.” Ben Dias, chief AI scientist at IAG, agrees that “agentic AI is rapidly moving beyond providing support or answers to taking autonomous action on our behalf.” For example, National University of Singapore vice provost Simon Chesterman points out that “agentic AI can plan, call tools, transact, and operate across workflows.” For these reasons, AI speaker and consultant Linda Leopold believes that “agents are autonomous decision makers, technically,” because “they act without constant human oversight and approval.”

But this kind of autonomy deserves closer scrutiny. Bruno Bioni, founder and director of Data Privacy Brasil, contends that “what looks like autonomy is [actually] delegated execution: selecting steps, using tools, acting within limits set by someone else.” Dias explains, “AI agents are given a goal and a set of guardrails, and then they independently determine and execute the sequence of actions required to achieve their given goal.” Amit Shah, CEO of Instalily.ai, similarly describes agents as “infrastructure that decides in the operational sense: It routes the order, moves the inventory, prices the risk, and so on.” As a result, Öykü Işik believes that “how we define autonomy in this context is critical.”

Operational autonomy does not translate into moral or legal accountability. For many of our experts, technical autonomy does not confer moral agency or responsibility. As Chesterman contends, “Autonomy in the engineering sense is not autonomy in the moral or legal sense.” Leopold cautions, “It gets problematic if we also start thinking of agents as autonomous in a moral sense — as entities with agency, or even coworkers, rather than the software systems they are.” Shah explains, “A machine can make the call, but it cannot own the outcome or consequence in the moral sense.” Jai Ganesh, Wipro’s former vice president of technology, agrees that “agents can make choices or execute actions, but they cannot be held accountable for the consequences.” For Carolina Aguerre, professor at Universidad Católica del Uruguay, “Responsibility is an inherently human faculty.”

Similarly, operational autonomy does not translate to legal responsibility. As Stanford CodeEx fellow Riyanka Roy Choudhury puts it, treating agents as autonomous decision makers “severs liability from capacity” since an agent “holds no assets to attach, no license to suspend, no deterrable interests.” Chow also points out that “agents have no legal standing and no assets,” adding, “They cannot be sued, pay damages, or be fully sanctioned.” Going further, Işik observes that “AI agents are stochastic and context-dependent,” not “coherent agents with stable intent” that meaningful accountability requires. For a recent example, Choudhury and others point to Moffatt v. Air Canada, in which a British Columbia tribunal rejected Air Canada’s argument that its chatbot was a separate legal entity accountable for its own misstatements. This case illustrates the challenge that companies face in governing agents that can act like human employees but cannot themselves be held morally or legally accountable.

Treating agents as autonomous decision makers undermines accountability. In fact, treating agents as autonomous creates an accountability vacuum and, as Bioni puts it, “imports a legal and moral status the technology has not earned.” As Chesterman cautions, “The more we speak as if agents ‘decide,’ the easier it becomes for firms and governments to launder responsibility through the machine: The model recommended, the agent acted, the human shrugged.” Or, as Bioni says, “it lets developers, deployers, and users hide behind ‘the AI decided’ whenever outcomes go wrong.” Even more bluntly, Shah calls the term autonomous decision maker “a governance fiction” that enables “blame laundering with better vocabulary.” For companies that remain accountable for the actions agents take, this accountability vacuum creates real risk if employees believe they can transfer blame and avoid responsibility.

The severity of this accountability vacuum depends on what’s on the line. For Monteiro, “Autonomy and accountability should not mix when the stakes are real” because regulators, boards of directors, and courts will require “a human they can hold responsible.” But while RAIght.ai co-CEO Richard Benjamins thinks that “impactful decisions should not be fully autonomously taken by AI agents,” he believes “trivial decisions can be.” Apollo Global Management’s AI lead Katia Walsh agrees that “for high-stakes decisions, responsible AI governance should not treat agents as autonomous decision makers, but for other contexts, it may be just fine” to treat them as if they were. And Aguerre contends, “Since not all AI agents perform activities with the same level of risk, the different levels of autonomy granted to an AI agent should be assessed against the tasks and objectives assigned.” Consultant Pierre-Yves Calloc’h sums it up: “Responsible AI means knowing exactly where autonomy must stop.”

The limits of autonomy depend on the sociotechnical context. Knowing where agent autonomy should end depends on the broader context of how humans interact with technology. Pointing to the example of autonomous driving, Australian National University’s Belona Sonna observes, “In many domains, AI agents are intentionally designed to make autonomous decisions because real-time operation demands it.” For Sonna, “The challenge is therefore not autonomy itself but ensuring that autonomous behavior remains aligned with ethical principles, human values, and its intended purpose.” Calloc’h says this distinction is particularly critical in high-stakes decisions, “where outcomes depend on trade-offs between conflicting objectives and implicit value judgments.” For him, these are “governance choices shaped by context, ethics, and strategy [that] cannot be reliably encoded or delegated.”

This is why several experts argue that governance should look past the agent to the system around it. Chesterman says, “The right unit of governance is not the agent as a little corporate citizen but the sociotechnical system in which it is embedded: the developer who built it, the enterprise that deployed it, the data and tools it can access, the permissions it has been given, and the humans or institutions that benefit from and remain accountable for its use.” Mark Surman, president of Mozilla, similarly explains that “agents don’t come from nowhere: People build them, companies deploy them, and someone profits from the decisions they make.” As a result, he urges organizations to “frame agents as extensions of human and institutional choices” since “the point isn’t to govern the robots [but] to keep humans accountable.” Finally, GovLab chief research and development officer Stefaan Verhulst argues that “governance must recognize [agents] as participants in broader sociotechnical systems shaped by institutions, data, incentives, legal frameworks, and community expectations.”

Recommendations

Considering the above, we offer the following recommendations for organizations seeking to responsibly integrate agents with varying degrees of operational autonomy:

1. Calibrate autonomy according to the stakes, not capabilities. Just because an agent can act autonomously doesn’t mean it should always be allowed to, especially where outcomes are hard to reverse or involve real trade-offs between competing values. Organizations should base delegation decisions on the reversibility and real-world impact of each action as well as the agents’ ability to accurately and reliably take action. Reassess those thresholds as the stakes of a task or goal change over time.

2. Enforce limits to autonomy by design, not by policy alone. After the lines between what an agent can execute independently and what requires human sign-off are drawn, build those limits into the system architecture itself (through properly scoped permissions, approval gates, hard stops, and technical controls), rather than relying on the agent or human operators to honor a written policy or prompt-based instruction in practice.

3. Name a human accountable for every decision. Regulators, courts, and boards need someone to hold responsible, and pointing to “the AI agent” is unlikely to cut it. Organizations should assign clear ownership for agent outcomes to specific roles or individuals, not to the technology, and they should do so before deployment rather than after something has gone wrong. In cases where agents operate across traditional business siloes, each department must understand their specific accountabilities and their responsibilities for oversight, escalation, and monitoring.

4. Govern the system, not the agent. Avoid accountability structures aimed at the model or agent. Governance should be directed at the full ecosystem in which the agent is developed, operates, and decides, including the developers who built it, the enterprise that deploys it, the humans who scoped and authorized its use, and the context in which it will operate. This ensures responsibility has somewhere real to land but remains shared across the entire workforce.

5. Create a culture of agent accountability. Interdependence keeps rising as autonomous agents increasingly coordinate with humans and other agents — and accountability becomes more muddled. Consider a group of humans, following guidance on how to work with autonomous agents, who produce a combined work product that leads to costly mistakes. Are the humans, human-machine teams, or governance system itself accountable? Specifying accountability as part of the design process and ensuring responsibilities are documented and understood avoids this lack of clarity. But holding employees accountable also means ensuring they can challenge agents and will be rewarded for raising concerns. This is critical to building an accountable culture, but an organization may be less able to demand those same responsibilities of end users when agents are supplied externally rather than deployed internally. For those cases, accountability by design is even more critical.

For this final issue of MIT Sloan Management Review, Benjamin Laker and Maria Papacosta offer advice on ending things well. In that spirit, we want to reflect on the impact our editorially independent publication has had in its 67 years. Over the past few months, we’ve been buoyed by many messages and online comments validating our core mission: to identify and share new ideas that advance management practice. We’ve also heard about the value SMR has created as a curator and connector at a time when so much information of variable quality is available from so many sources. SMR has given scholars and practitioners a platform to develop their thinking on innovative approaches to business challenges, and leaders the opportunity to deeply engage with new insights and put them to work to solve problems and improve results.

Many people have contributed to SMR’s success; here are some thoughts from a few of them.

Paul Michelman

Editor in chief, BCG; former editor in chief, MIT SMR

“What SMR delivered for decades was innovative, well-expressed management thinking that can light a spark. It can provoke somebody to cast their eyes left or right when they’ve only been looking straight. Lighting a spark that leads to a solution is a highly practical thing. It begins with identifying ideas that matter, that can move the needle, that are fully additive to what’s already been published — but then shaping it in a way that delivers actionable inspiration. Another thing that set SMR apart is its place at the collision of technology, innovation, and industry — its MIT-ness. SMR was never afraid to be wonky, and I mean that in the best ways — that it’s OK to get underneath and a little bit nerdy.”

Deb Gallagher

Publisher, MIT SMR

“The most meaningful and rewarding part of the work we did was that people acted on our content — we helped change how people work, how organizations function. There’s a lot of great work being done in management education to make companies run better, but university scholarship can be very esoteric. Our mission was to create a bridge between academic research and organizational need. That remained steady while we evolved how we delivered it, adapting to new media formats and channels. We extended our reach by licensing our content to learning organizations and to publishers overseas that saw our unique value in speaking to the needs of leaders and workers in organizations and wanted to bring rigorous thinking to leaders in their markets.”

Martin Reeves

Founder, Martin K. Reeves Advisory; former chairman, BCG Henderson Institute

“BCG collaborated with SMR for almost 20 years on Big Ideas research projects on sustainability and on AI, in addition to contributing articles. Without a doubt, SMR has made a significant impact on the field of strategy, being one of the few publications bridging the rigor of academic research with practical relevance and digestible insights for practitioners. In particular, one of SMR’s superpowers has been to use its large and loyal subscriber base to both gather fresh field data and build an audience for consuming and discussing new insights. Thank you to all my friends in the editorial and marketing teams for your excellent work and impact on the field.”

Jeff Schwartz

Senior adviser, Gloat; former principal, Deloitte Consulting LLP

“For several decades, my job has been to look toward what’s coming next — and no publication shaped that view more than MIT Sloan Management Review. What we lose with its closing is a rare devotion to praxis: the living intersection of theory and practice, where technology, strategy, and operations meet — the very thing a place like MIT exists to think about. Our collaborations with SMR on digital transformation, talent, and strategy yielded early insights that continue to be relevant today. Later, we pivoted to a multiyear exploration that reframed the workforce itself — opportunity markets, workforce ecosystems. SMR seldom treated an idea as once-and-done. Themes were introduced, explored, and followed through. It seated academics and practitioners side by side, built communities of practice, and quietly orchestrated ambitious ideas.”

Robert W. Holland Jr.

Former managing director and publisher, MIT SMR

“SMR was losing money when I joined in 2010. We recognized that the academic journal model was no longer viable and made a strategic pivot to reposition SMR to bridge academic content and practitioner needs. The Big Ideas program generated significant revenue and extended SMR’s reach. We became self-sustaining and added “MIT” to our name to become MIT Sloan Management Review, which started to bring the ethos of MIT into what we were doing, with a greater focus on innovation and new ideas. That focus was unique to SMR compared with other journals.”

David Kiron

Editorial director, MIT SMR Big Ideas

“Big Ideas was never just a research program. It was a bet: that rigorous, independent research, produced with academics and with thought leaders at the world’s top consulting firms, could influence management practice and pay for itself. The bet paid off. Over 16 years, the program produced more than 40 reports and articles, garnering more than 3 million page views and 7,000 citations. Our thought leadership earned gold medals from the American Society of Business Publication Editors and Axiom. And program director Allison Ryder created Me, Myself, and AI, a respected podcast with nearly 2 million downloads. I am grateful to the researchers, editors, collaborators, and readers who made my role feel more like a privilege than a job.”

Martha Mangelsdorf

President, Mangelsdorf Communications; former editorial director, MIT SMR

“The heart of MIT SMR’s work is communicating rigorous and highly relevant academic research in a way that makes the findings easily accessible to managers. I worked with an author team as they developed a 2017 MIT SMR article about the Oxford approach to scenario planning. That topic has become increasingly important as our world becomes more uncertain, and the article is currently one of the bestselling pieces in MIT SMR’s store. MIT SMR’s continuing impact on practice can also be found in a new report from the MIT Institute for Work and Employment Research about negotiations between Kaiser Permanente and its unions over AI; the labor and management participants read and discussed two MIT SMR articles on AI implementation. That’s SMR at its best: providing practical, research-based information that helps leaders and organizations address critical issues.”

Linda Hill

Wallace Brett Donham Professor of Business Administration, Harvard Business School; author and editorial advisory board member, MIT SMR

“SMR provided space for those of us who do research to share our ideas, even when they were evolving, and get feedback from readers to shape how we would do our research going forward. That link between theory and practice starts a dialogue between academics and practitioners that makes both the theory and the practice richer. SMR helped practitioners and academics to frame the questions we should be asking. There are very few people who know how to be those translators. SMR editors were the bridgers — people who can actually curate and translate and integrate across worlds. We need that so much today. Emerging technologies are transforming everything. SMR helped practitioners understand what the opportunities and challenges really are. It created a global ecosystem of thought leaders in both practice and the academy that helped push both forward.”

We’re proud of the work we did at MIT Sloan Management Review and honored to have convened and participated in a lively, productive, and exciting conversation on leadership, innovation, and how business can contribute to a better world.

Many companies today declare a corporate purpose and subsequently find that translating it into strategy, culture, and daily decisions is difficult. Underlying that challenge is a critical question: Who will ensure that a purpose commitment is operationalized?1

In response, companies seeking to institutionalize purpose are creating a new executive role. Cisco, Deloitte, Virgin Atlantic, and others have appointed chief purpose officers (CPOs), signaling that organizations are increasingly recognizing the need for dedicated leadership of these efforts. (See “What Every CPO Must Do.”) Our study of 56 of these emergent leaders reveals that individuals can take markedly different approaches to the role, depending on how purpose is conceived at their organization and the degree to which it has been operationalized.2

We identified four leadership archetypes that reveal distinct ways in which purpose becomes operational and examined the conditions that enable or constrain their impact. Different types of CPOs face distinct execution barriers that can cause purpose to stall. In this article, we’ll look at each leadership profile and explain how to determine which aligns best with current organizational needs.

Four Types of Purpose Leadership

Of the four distinct types of CPOs we encountered in our sample, the largest group (39%) was the visionaries, who aim to inspire shared meaning and emotional connection. About one-fifth were activists, who confront institutional contradictions and use purpose as a moral compass, while one-quarter were architects, who strive to institutionalize purpose through governance and systems. PR strategists were the smallest group (16%); they prioritize crafting coherent narratives that align brands and communication. We’ll look at each archetype in turn.

Visionary CPOs often emerge in smaller, family-owned, or founder-led organizations. In these settings, values are often deep in the company’s DNA but not explicitly articulated or translated into strategy.3 For these leaders, purpose is an ethical and social obligation that must be lived collectively but not necessarily communicated outward. They inspire others to embrace and embody the organization’s purpose, fostering collective commitment to executing a purpose-driven strategy. They translate the company’s core values into a shared sense of meaning for employees. Their leadership is deeply personal, and they identify closely with the organizational purpose.

Visionary CPOs prioritize helping employees connect to purpose on an emotional level. Their storytelling is unscripted and relational: It surfaces in conversation, in shared rituals, and in how employees describe their own work to one another. They may host reflection circles, where employees share how their work connects to the company’s purpose; run coaching sessions focused on aligning personal and organizational purpose; and hold offsite retreats that give teams space to explore why their work matters and how it contributes to fulfilling the company’s purpose. One Danish fashion company holds weekly meetings in a park to reflect on its social impact. Small rituals like these sustain collective reflection and strengthen what one leader called the “emotional glue” that binds people across the organization.

These types of CPOs tend to cultivate influence by building personal connections and informal networks rather than via formal authority. They participate in company sports, join lunch conversations, and use everyday interactions as opportunities to reinforce shared meaning. Many view such a relational approach as the optimal way to create soft power and trust. By weaving purpose into daily relationships, visionary CPOs allow it to diffuse organically to become part of how people experience belonging and identity at work.

These leaders also seek to empower others to lead, recognizing that making purpose a shared responsibility helps facilitate organizational change. Many create cross-functional teams that host purpose dialogues, pilot new practices, and surface dilemmas for senior leaders to address.

Visionary CPOs may find it challenging to be seen as a strategic peer in the C-suite, given that purpose work often does not yield immediately tangible outcomes. Several study participants noted that their work was initially dismissed as “nice to have” rather than essential to organizational performance. Over time, most visionary CPOs learn that their own credibility depends on demonstrating how purpose advances business results. To achieve that, they must balance the work of translating moral conviction into measurable impact with efforts to maintain the human connection that makes purpose stick among their organization’s internal audience.

Activist CPOs work on the front lines of corporate transformation. They are most often situated in large, complex organizations characterized by conflicting stakeholder demands and sensitive issues. When such organizations are confronted with commercial pressures, political tensions, or inertia, they often struggle to uphold their stated values. The goal of the activist CPO is to challenge the status quo and ignite new energy. They see their role as reminding people what the organization stands for, holding it accountable to its purpose, and confronting drift and ethical blind spots.

Their leadership style is bold and unapologetic as they ask uncomfortable questions to probe whether decisions are prioritizing profit over people and purpose. One CPO urged the board to withdraw from a lucrative but ethically questionable market; another paused a multimillion-dollar product launch because it conflicted with the company’s stated values.

Some activists push for change beyond organizational boundaries, seeing purpose as a vehicle for systemic change. As one said, “I want to help change entire systems. … If this is how capitalism works, then let’s rewire it.” These leaders may run awareness initiatives like climate science briefings and social justice workshops, aiming to focus attention on the organization’s role within broader society.

The greatest challenge for activist CPOs is maintaining legitimacy: They must balance moral conviction with strategic credibility in order to push boundaries but avoid being labeled as troublemakers. Their credibility may also be questioned if some stakeholders see them as showing inadequate commitment to particular issues. Too much advocacy risks alienating leadership, while too much caution may erode trust among employees.

Failures occur when conviction turns into confrontation. Activists who push too hard without building coalitions risk marginalization. Without organizational support, isolation and moral fatigue can set in, turning purpose advocacy into frustration rather than transformation.

Activist CPOs who achieve sustainable cultural transformation build alliances while gradually shifting organizational logic and norms. For example, one CPO described how they consistently asked in meetings how decisions were connected to the organization’s purpose. It took years, but their colleagues eventually began proactively applying purpose as a decision-making filter in investment and strategy discussions — without needing to be prompted. Another noted that consistent questioning, once dismissed as idealism, eventually became part of how strategy meetings were framed. Activist CPOs’ influence is slow but transformative. They move the discussion away from asking whether an action is necessary and toward considering whether it aligns with the organization’s identity and values, and ultimately, its purpose.

Architect CPOs typically operate at the upper levels of large multinationals. In such environments, a formal purpose statement usually exists but has yet to be fully understood and adopted across the organization. Architect CPOs primarily identify as institutional changemakers who build the structural foundations that enable purpose to become relevant, durable, and measurable. Rather than relying on bottom-up engagement, they strive to hardwire purpose into the systems that drive performance. In doing so, they demonstrate that a strategically embedded purpose can endure beyond the tenure of any individual leader.

Architect CPOs focus on alignment between purpose, strategy, structure, and performance systems. For instance, from 2017 to 2019, Lara Bezerra was the CPO at Roche Pharma India, while also leading the division. She linked incentives and performance metrics to patient outcomes and community impact, ensuring that purpose shaped how success was defined and rewarded.

Architect CPOs’ work often involves redesigning policies, restructuring the organization, and building decision frameworks that help maintain alignment between values and commercial priorities. Most such frameworks track both financial indicators and metrics that evaluate sustainability performance or social impact so that they can show evidence that values-based decisions are aligned with the health of the business.

Sustaining momentum can be a challenge for architect CPOs amid bureaucratic inertia, competing priorities, and limited resources. Progress can be invisible and results easily overshadowed by short-term pressures, so they must learn to strike a balance between conviction and pragmatism.

PR strategist CPOs excel at crafting a cohesive narrative around purpose. The role often takes this shape in large, dispersed, brand-driven organizations where the purpose must be communicated consistently across geographies and stakeholders. PR strategists construct a deliberate, repeatable story designed to align how the organization is understood by employees, customers, investors, and the public. They align internal commitments and external reputation around a shared pro-social story that’s intended to engage and influence key stakeholders, foster identification with the organization’s values, and motivate behavior consistent with its overarching purpose.

Most such CPOs come from backgrounds in communications, marketing, or branding and view themselves as the storytellers of purpose — responsible for how it is articulated, shared, and believed both within and outside the company. Their appointment often signals a formal public commitment to purpose.

PR strategist CPOs look for creative ways to make purpose visible and credible. Some amplify employee voice through storytelling campaigns that highlight personal purpose moments and are curated, produced, and shared at scale. Others integrate social commitments into product portfolios or employer branding. These CPOs succeed by making sure the purpose is known and valued in people’s daily work.

However, while PR strategist CPOs understand the power of purpose to inspire stakeholders, they often lack the authority to embed it structurally within their organizations. Their challenge is legitimacy and reach among internal stakeholders. They are expected to build belief but are not always empowered to reshape the systems that sustain it. These leaders risk becoming figureheads who are powerful in communication but peripheral to real change in settings where purpose work is not actually mature and purpose remains confined to messaging rather than embedded in decisions and visible in actions. In the worst cases, storytelling replaces structural action, leading to “purpose-washing.” This misalignment can give rise to moral and identity dilemmas as CPOs try to reconcile their professional role as guardians of corporate purpose with actions that contradict the values they are meant to uphold. Over time, such tensions may precipitate personal and organizational identity crises, eroding both ethical integrity and institutional coherence.

Which CPO Type Fits Your Organization?

In deciding what kind of executive is best suited to lead the purpose initiative within an organization, senior leaders should ask themselves, “Are we trying to renew culture, realign strategy, or rebuild trust?” Our Purpose Leadership Fit model is a simple and practical way for boards and CEOs to identify the type of purpose leader who can help them move forward. We’ve found that organizational types often correspond to a particular stage of maturity, certain purpose objectives, and a particular type of leader. The four scenarios below set out the fit; the challenges each leader will face are summarized in “CPO Archetypes Approaches and Challenges.”

1. At smaller, founder-led, or early-stage organizations beginning their purpose journeys, the job to be done is to build purpose awareness and drive cultural renewal. The focus here must be on forging an emotional connection and engaging employees.

Best fit: Visionary CPO

Success factors: To build credibility and influence beyond inspiration, the visionary CPO should prioritize developing a small set of credible metrics that demonstrate how purpose affects outcomes such as engagement, retention, or customer advocacy. They should invest in informal, trust-based mechanisms (such as rituals, narratives, and peer networks) that anchor purpose in daily behaviors and decisions.

2. Large or complex organizations undergoing change where leadership is open to confronting profit-principle tensions are developing maturity in operationalizing purpose. In these settings, a CPO must drive accountability and challenge established norms to align purpose with action.

Best fit: Activist CPO

Success factors: Activist CPOs must build coalitions that span functions and hierarchies. They need to establish structured yet psychologically safe spaces for employees to voice misalignments. Their most important role is to secure visible sponsorship from senior leaders and governance bodies to sustain their mandate.

3. Multinational corporations with more maturity in their practice are typically integrating purpose into strategy and governance. Here, the CPO needs to more deeply embed purpose into systems and measures of outcomes, such as performance metrics.

Best fit: Architect CPO

Success factors: To ensure that their frameworks take root, architect CPOs must emphasize simplicity, clarity, and relevance at every level of the organization. They should design dual-KPI systems and governance structures that institutionalize purpose without creating unnecessary bureaucracy and link incentives to stakeholder value, not just financial returns.

4. Large, brand-driven organizations that have a mature, authentic, and well operationalized purpose practice must still build credibility and stakeholder trust as part of their competitive positioning. CPOs have to skillfully communicate purpose to build trust and keep internal culture aligned with external reputation.

Best fit: PR strategist CPO

Success factors: Communication must align with substantive progress. PR strategist CPOs should collaborate with HR, operations, and strategy to ensure that the stories they share are grounded in action and include concrete outcomes to make abstract values tangible.

No single leadership type fits every situation. The choice depends on how mature the organization’s purpose already is and whether the immediate work is to shift culture, confront contradictions, build systems, or sustain credibility.

Most organizations define purpose statements but falter when it comes to embedding purpose in strategy and execution. Our research suggests that this is a leadership gap. The four archetypes — visionary, activist, architect, and PR strategist — offer different approaches to the challenge of how to make a purpose commitment durable inside an institution.

As organizations move to appoint a CPO, they must ask: Which kind of leader is needed now? What work must they do? When will the work change? What support will enable them to succeed? The leaders who get this right will understand that their job is not to embody purpose permanently but to institutionalize it so thoroughly that their own archetype, in time, becomes unnecessary.

What’s wrong with loyalty? Isn’t it a virtue to display steadfast allegiance to something or someone other than oneself? After all, loyalty has been celebrated as a virtue across many cultures for millennia.

As economic headwinds and a tough job market appear to be empowering more authoritarian styles of leadership, it’s a good time to look more closely at loyalty. We must ask whether good leaders can fairly demand it of their followers and whether it’s an idea that modern management should finally discard.

Two recent high-profile examples illustrate just how problematic an emphasis on loyalty can be.

Staffers loyal to former U.S. President Joe Biden allegedly concealed troubling signs of the aging leader’s cognitive decline before his poor performance in a June 2024 televised debate against then-candidate Donald Trump. The public reaction to the event led the incumbent to withdraw from the presidential race, long after it was feasible for his party to engage in an open process for nominating another candidate.1

That election was won by a man who has made loyalty a central organizing principle of his leadership, at times demanding personal allegiance from those in his administration and reportedly subjecting candidates for senior roles to “loyalty tests.”2 Any sign of disagreement with President Trump is recast as betrayal, and those perceived as insufficiently loyal are sidelined or removed, even when their job performance or policy alignment is not the primary issue. Conversely, notably problematic performance and actions themselves may go unchallenged, as long as unwavering allegiance is maintained.

Loyalty, enacted in this manner, is not about shared commitment to a broader purpose or principle rather than to an individual. In practice, loyalty is often a measure of personal fealty, with silence and compliance positive indicators of commitment. When organizational scandals are exposed, it’s not uncommon to find that coverups of abuse, financial irregularities, and other forms of corporate malfeasance were rationalized as acts of loyalty to the institution or allegiance to a leader.

The Problem With Loyalty

Many readers likely have personally experienced these less admirable kinds of loyalty. Whether it’s “looking the other way,” “omitting all the facts,” or a host of other forms of ignoring or distorting the (ethical) reality of a situation, people commonly use the noble language of loyalty rather than the condemning language of fear, greed, and other types of self-preservation or self-interest. Broader history, likewise, shows that the word loyalty is commonly used in descriptions of the most corrupt regimes and destructive ideologies — where people have committed themselves to, colluded with, or enabled some of the worst actors in history out of fear, nationalism, or blind allegiance.

Loyalty is inherently about partiality; that is, it prioritizes some purpose, person, or group’s interests above others.3 In the context of work, loyalty means promoting the well-being of or preventing harm to a particular group of people or an organizational objective, even if it involves disregard for or harm to other people or purposes. Consider the manager who proudly recruits and promotes people from their college alma mater without considering how this affects potentially stronger candidates from elsewhere, or a subordinate who is fiercely committed to their manager because it benefits them personally, not because they’re both putting the organization’s mission first.

Loyalty is thus a conditional virtue: It might serve the pursuit of universal principles like justice or compassion, or it might serve much less noble things. Actions deemed loyal can, in short, be moral, amoral, or downright immoral. Covering up an in-group member’s unethical behavior because it also benefits you to do so might be described as loyal, but it’s still unethical. Similarly, it’s hard to be impressed by the morality of salespeople’s loyalty to their company when they use sales tactics to maximize their own income at the expense of customers. So does the term really reflect something to be proud of, or is it a nice-sounding way to deflect attention away from the harm being done or the self-interest of the actor? It’s certainly easier, after all, to say we’re “being loyal to the company” than to say that we’re “screwing customers,” or to say we’re “loyal to the boss” rather than that we’re “complicit in covering up abuse.”

Our goal, thus, is not to try to redeem the inherently problematic concept of loyalty. In work contexts, its usage has become so skewed toward the unadmirable that it’s not worth trying to salvage. Furthermore, decades of rightsizing and reorgs have made it abundantly clear that workplace loyalty is too often a one-way street. Research discussed in Sloan Management Review nearly 30 years ago pointed to the breakdown of a social contract under which employees could expect that their loyalty to the company would be reciprocated.4 In a recent survey, fewer than one-quarter of U.S. employees reported strong trust in their leadership or the belief that their organization cares about their well-being.5 The cynicism (or realism) of the majority was validated quite explicitly in August 2025, when AT&T CEO John Stankey fired off a frank memo in response to an employment engagement survey.6 In the memo, he addressed those who may have expected an “ ‘employment deal’ rooted in loyalty,” writing that the company had “consciously shifted away from some of these elements.”

Postures such as Stankey’s are strengthened by a difficult job market in which employees have few alternatives and authoritarian leadership styles are on the rise. The shift toward transactional work exchanges grounded in fear is well underway. Executives may label employees who challenge them in any way, or who try to maintain work-life boundaries, as “disloyal,” diminishing their career prospects. When loyalty means “having my back no matter what,” “sticking to the party line,” “not contradicting me in public,” and “making me look good,” workers may comply, but with an inevitable cynicism that looks nothing like the healthy employee engagement that organizations need in order to thrive. Similarly, when workers recognize that they are in a culture where it’s not what you know or contribute but who you know and what you do for them that gets you ahead, they may default to fawning and sycophantic behavior. Under those circumstances, it’s hard to imagine the organization itself is optimizing performance.

You may be thinking that the problems we’ve described are merely distortions of loyalty, not a problem with the notion of loyalty itself. You can probably readily think of workplace situations where the label of loyalty is applied to what seems like admirable behavior; situations where people remain committed to a mission and set of core values amid external challenges that would make drift toward personal comfort, safety, or short-term profit maximization easy to rationalize. That’s why we often associate the word loyalty with extraordinary effort, sacrifice, and resilience and claim it as a force behind necessary revolutions, solidified alliances, and the holding together of movements and nations. But all of the true virtues we attach to the term loyalty can stand on their own: One can be a person who demonstrates commitment and integrity, and keeps their word, without necessarily being or needing to be called loyal.

The good news is that we don’t need to use the word loyalty at all in the context of organizational life. Decades of work in the social sciences have given us a better understanding of what qualifies as healthy social exchange at work: where an ongoing interdependence is rooted in some degree of socioemotional support, reliability, and commitment to both contractual and more diffuse goals and obligations. Management research, meanwhile, has told us a lot about what happens when workplace relationships between leaders and employees are healthy: better performance, more timely truth-telling, more adaptability, less burnout, more engagement, and a sense of belonging. Given all this, and our observations of the mostly unhealthy ways that loyalty manifests at work, we suggest that when leaders explicitly demand loyalty, healthy social exchange is not on the agenda and people should be on the lookout for dysfunction. Loyalty tests or demands aren’t necessary or healthy in friendships or family relationships, and they aren’t in work relationships, either.

Not All Social Exchanges Are Created Equal

Once we stop valorizing loyalty, we can take a more clear-eyed look at the social exchanges that shape our allegiances at work. Many work relationships are characterized by what we call imbalanced allegiances. They can be coercive, where one side has the power to demand adherence to their agenda. In cultures shaped by coerced allegiance, “do it or else” is the spoken or unspoken mandate. In a survey we conducted, people in business units dominated by coerced allegiance were statistically more likely to respond that “agreeing with powerful others is the best alternative” and that it’s “safer to agree with managers than to say what you really think.”

This has a chilling effect on employee voice: People stay quiet and comply due to the feared consequences of being labeled disloyal. Leaders relying on coerced allegiance often mistake compliance with agreement and authentic commitment, but without real trust, innovation and agility silently die. Unsurprisingly, adaptive performance — measured as the ability to deal effectively with unpredictable situations and to adjust quickly when changes occur — was lowest among people working under coerced allegiance.

One-sided allegiance needn’t be coerced, however: Some cultures breed sycophantic allegiance. People desperate to be accepted, to be “in the room,” or just to survive when their own objective performance wouldn’t be enough will often willingly do whatever they think their boss wants. When a conflict arises between the organization’s mission or other stakeholders’ needs and their sponsor’s agenda, the sycophant’s loyalty is clearly to the latter.

Sycophantic allegiance and coerced allegiance often go hand in hand. Once people see the effects of coerced allegiance — disagreement is quashed, and those who speak up get pushed to the fringe — some employees start to fawn. That’s why survey respondents’ reported confidence in their ability to speak up effectively in relationships marred by imbalanced allegiance was by far the lowest across the types of social exchange we identified. Sycophantic allegiance likewise causes drift from the organization’s core purpose and leads to the well-known ills of a culture in which it’s not what you know or contribute that gets you ahead but who you know and what you do for them. But since sycophantic loyalty is not reciprocated, the sycophants, too, will be thrown under the bus when it becomes necessary.

In social exchanges based in mutual personal advantage, both parties value trust, and they care for and protect each other. These relationships can create cohesion, psychological safety, and a sense of belonging. But when one respondent, a consultant in the public sector, said, “I am not alone in saying I am there for the people … not the client or the work,” it pointed to the problem with these kinds of relationships: The ultimate focus is the relationship itself, not a broader shared purpose. These types of exchanges do fine in steady-state operating environments, but when turbulence hits, self-interest usually replaces any sense of mutuality.

A relationship-first mentality might be appropriate in other spheres of life, but few formal organizations have “advancing the interests of our employees above all else” as their primary mission. The mission of UVA Health, part of the university where one of us (Jim) works, is “transforming health and inspiring hope for all Virginians and beyond,” not “enriching our doctors” or “making work as easy as possible for all employees.” And when the organization needs to evolve to adapt to unforeseen headwinds or an employee’s poor fit, this form of commitment becomes even more problematic. Taken too far, “we protect each other” and “we don’t rock the boat if someone might fall off” ultimately undermine the organization’s fundamental reason for being and can allow unethical behavior to flourish.

However, even a culture that encourages a laserlike focus on the mission, what we call idealized devotion, can lead to dysfunction when it becomes an expectation enforced by others or driven from within. In moderation, devotion to a cause can energize people and build camaraderie toward the pursuit of noble objectives. But when it’s understood that “we do whatever it takes” and “the cause always comes first,” self-sacrifice is valorized and leaves burned-out and disillusioned workers as collateral damage. Our survey respondents in units of this type reported feeling a significantly weaker sense of belonging and less emotional attachment to their units compared with people in other units. They were also more likely to report that some people are treated as impersonal objects and that people have become more callous toward each other over time.

The healthiest type of social exchanges we identified reflected what we call shared principled commitment. Here, people are committed to the organization’s mission and will prioritize it when hard choices are necessary, but not without two-way regard for the humans involved. When organizations have a high degree of shared principled commitment, they’re more likely to benefit from people telling hard truths and holding one another and their leaders accountable to the mission and its underlying shared values.

What’s the benefit of this approach? Survey respondents who reported shared principled commitment as the most common form of social exchange in their unit also reported significantly higher overall performance than respondents in units dominated by all other types of exchange. That superior performance included the quantity, quality, and efficiency of work done. It also included the generation of innovative solutions to improve the unit or organization, which is what likely makes shared principled commitment more durable than other types of social exchanges. When turbulence hits, when values are tested, and when decisions are unpopular, shared purpose and mutual respect combined help people persevere and remain committed.

Shared principled commitment isn’t reserved for organizations with a lofty mission, like those in health care or environmental conservation. It can be found in any organization that has created a compelling reason for people to come and stay together in the service of something no one could achieve individually. At WD-40, former CEO Garry Ridge established a set of company values: The first was, “we value doing the right thing,” and the second was, “we value creating positive, lasting memories in all of our relationships.” That ethos went far beyond selling lubricant. It was about how employees treated one another, customers, and partners every day. Ridge often said that the real product wasn’t the can on the shelf but rather the trust, connection, and sense of belonging people experienced through their work. By elevating “making memories” as a shared human purpose, he gave employees a reason to stick together that was bigger than profits or personal gain.

Taking Stock of Your Organization

What types of relationships define your unit or organization? Presumably you’d prefer not to be seen as coercing allegiance or rewarding sycophants. But how can you know?

Your first step needs to be an honest assessment of what the people around you see as the primary currency of social exchanges. Below are some hard but important questions to ask. And, given that the answers may well be unflattering and thus risky for those whose truth you most need to hear, you need to be sure to ask everyone for their input and do so in a way that guarantees they will suffer no consequences for their honesty throughout the process.

• What kind of behavior is most likely to get you ahead around here? Unwavering commitment to specific people, sycophancy, or silence? Clear commitment to the mission and the truth, even if it upsets people sometimes?
• Does our organization have “loyalty tests”? Loyalty to whom or what?
• What’s the implicit definition of disloyal held by those in power? What happens to those deemed disloyal?
• Do people with power show as much concern for and commitment to the individuals they lead as they expect from them?
• Are most people willing to make sacrifices for the greater good, or are they ultimately transacting in a self-interested, self-promotional way?

You’ll also want to ask people whether the pattern of social exchanges seems to be changing or has changed in ways that are undesirable. Such shifts can happen in reaction to perceived signals about what behaviors are rewarded or punished. You might learn, for example, that what was once primarily principled commitment has drifted into less-healthy idealized devotion. Or what began as strong mutual trust may have shifted toward imbalanced allegiance because leaders have centralized authority and now routinely punish dissent.

We suspect that in most organizations, the honest answers to these questions will reveal gaps between what actually seems virtuous and suitable in organizational life and what’s playing out around you.

Building Culture Based on Shared Commitment

It’s easy to say that shared principled commitment is the most desirable kind of relationship we’ve described. But doing the work to get there and maintain it is hard; it’s work that will likely demand personal sacrifice and new behaviors as you commit to putting mutual regard in pursuit of the organization’s purpose over personal agendas. If you’re serious about earning the upsides of shared principled commitment — discretionary effort, trust, high morale, sustained commitment — here are some ways to begin.

1. Live the organization’s values in everything you do. If purpose is a driver, it must be more than a slogan. When people see leaders aligning actions with mission-based values, especially when those actions cost them something, it signals true commitment to a shared purpose, not just to personalities or performance outcomes that exploit the organization for individual benefit. This consistent modeling reinforces the mutual trust that earns long-term commitment of the right kind.

Where your work on organizational purpose has been cursory or grown stale, identify (or clarify) three to five core values that should underlie purpose-driven decision-making. Then audit recent decisions (such as ones related to hiring, budget, or conflict resolution) and consider whether they reflect those values. A leader who names fairness and merit as core values and follows through by ensuring consistent criteria and processes for determining promotions across the organization is one example.

Earn buy-in — don’t simply demand it — by communicating not only what has been decided but why. Tie the rationale for every significant decision explicitly back to values and purpose. And monitor yourself: Create mechanisms for others to hold you accountable when your actions drift from declared values.

2. Welcome challenge and dissent. People who speak up to share different ideas or views are invested, not disloyal. Creating space for dissent signals that the mission is more important than avoiding conflict. When challenge is welcomed, people feel safer acting in service of what really matters, even when it’s uncomfortable.

Explicitly frame dissent as taking responsibility: “This organization’s purpose is ours; everyone must speak up to pursue and defend it.” Then publicly thank individuals who raise tough issues, even when those issues have yet to be worked through. If, for example, a direct report raises concerns about a flawed product strategy championed by a powerful team, thank them and make space to reevaluate the plan.

During meetings, ask, “What’s a viewpoint we haven’t considered yet?” and “Why might this thinking be wrong?” Look for the people most likely to challenge you when your decisions seem at odds with the organization’s core values or mission, and appoint them to your team or put them in other key roles.

3. Remove the sycophants. People who do whatever you say, or even try to anticipate what you want and do it without question, aren’t invaluable — they’re dangerous. They enable and amplify whatever blind spots and biases you have rather than helping you do better. If you feel completely comfortable going into every meeting with your team, you’re almost certainly lacking the “team of rivals” President Abraham Lincoln successfully surrounded himself with to pressure-test his thinking and decision-making on behalf of his core purpose of keeping the United States of America unified.

Track (dis)agreement and pushback expressed by each member of your team. Remove those who never dare to differ, take their cues from you for every decision, or don’t ask hard questions.

4. Demonstrate relational reciprocity. Healthy, sustainable relationships require ongoing demonstrations of fairness, advocacy, and care. They require that you stand up for people even when it costs you something to do so — when you’re reciprocating based on their commitment and contributions to the organization’s values and purpose, not to reward their fealty.

Advocate for people in the rooms they are and aren’t in (for example, in talent reviews or budget debates). Fight to protect people who are serving the mission when decisions to enrich or empower others are being considered. That might mean, for example, speaking out against proposed layoffs of talented, committed people that may undermine the organization’s long-term well-being.

Nurture relationships by, for example, normalizing checking in on people’s well-being at work and in life outside of work — not as surveillance but as an act of caring. Where people are falling short, develop your capacity to tell them hard truths in ways that show respect and care.

5. Put limits on your leadership. It’s hard for employees to fully separate an organization’s stated mission and values from the behavior of people at the top. It’s why people do very good or very bad things for charismatic leaders; it’s why people quit organizations with terrible leaders despite loving the mission. So it’s not enough to say that “the mission is bigger than me” and that “your commitment should be to the mission, not me.” You have to show it by routinely illustrating the distinction.

One way to do this is to regularly ask yourself and your truth-tellers, “Am I the best person to lead this decision, or should I step back?” You can also share credit widely and name contributions you couldn’t have made alone or don’t deserve credit for.

And signal that the mission matters more than your personal control of it by involving others in shaping direction, not just executing it. A founder might step back from day-to-day decision-making and instead trust a new team to lead an important growth phase, for example.

Leaders often point proudly to the “loyalty” they command when, in reality, the term reflects relationships based in self-serving, short-sighted motives. They conflate obedience, indebtedness, or favoritism with commitment to the organization’s purpose and values. And they fail to recognize that mission-first, care-based relationships aren’t something that can be demanded or assumed. They must be earned, reciprocated, and grounded in consistently aligned behavior. It’s hard, sometimes painful work — work that is likely clouded and undermined by relying on the word loyalty where it isn’t helpful or needed.

We’re not suggesting that the effort it takes to create shared principled commitment is worth it just because it feels more virtuous than social exchanges based on problematic forms of allegiance. As our data and others’ suggest, doing so may also make your organization more resilient in the long run. When people stay merely because they feel stuck, or when they don’t believe that their leader cares about them or demonstrates reciprocal concern, exploitation increases and performance and commitment decline. Conversely, when they feel strong emotional attachment to and identification with their organization and have trust in their leader, performance and prosocial behaviors increase, and absences and turnover decline.7

If that’s not enough to interest you in shared principled commitment, we suggest at least being honest about the deal you’re offering. Using “loyalty” as a euphemism for ignoble ways of transacting fools only the fools, and the rest will just trust you even less. And using the complicated, oft-abused term to describe healthier forms of relating isn’t necessary or helpful either. It’s time to lay off loyalty and employ something better.

“I feel set up,” said the CEO of a professional services firm, breaking the long silence after his presentation. The transformation he had envisioned — and his executive team had championed and worked on for three years — had failed to deliver returns, and the hope that his leadership had once inspired had soured into blame. We witnessed that scene toward the end of a four-year study of the leadership team that strategized and implemented that firmwide transformation. It was the anticlimax of a drama that we have heard many executives recount.

The script of that drama goes like this: The future is uncertain. Trends in the world, the market, or the industry call for radical change. Executives craft a vision of transformation centered on adopting new technology, new structures, or new ways of working. Plans and people are drafted; projects are put in motion. Those initiatives generate enthusiasm. Over time, however, tension and exhaustion ensue. Then resentment emerges, leading to disengagement and, in the worst cases — as in the one we studied — to the organization’s demise.

Analyses of such cases often highlight that transformations fail because people lack the awareness that change is needed or the motivation to pursue it. They may point to the absence of strong leadership, sound strategy, committed employees, or the right process. Our work, in this study and beyond, has made us wary of such explanations. Transformations fail even when awareness and motivation are as abundant as leadership, strategy, and commitment. An urgent issue, or a crisis, provides a burning platform that leaders use as a bonfire to gather people around. But the fire starts to spread, and eventually people flee or burn out.

Our research uncovered what fuels that fire. It is anxiety, an emotion that can be mobilizing at first but becomes paralyzing if it is neglected and uncontained by leaders. As we saw in the case we studied, it may then be organized in ways that keep everyone busy while the status quo endures.

Change may be the only constant, but anxiety is only human. When leaders lose the capacity to name, share, and manage anxiety, their efforts to organize change, and the work of people in the organization, can get co-opted to keep anxiety at bay. This covert function sabotages the overt aim of transformation. We call that process defensive organizing. Below, we describe its unfolding and outline how leaders can avoid it by learning to work with anxiety. We begin by discussing the roots of defensive organizing because people must recognize how easy it is, as leaders and followers, to be taken down a defensive route. Only then can they stop, get to work, and set a different path.

Fear of the Future

Two conditions lay the foundation for defensive organizing. First, one or more executives identify with their organization. Second, the workforce trusts those executives’ competence and benevolence. Those conditions make leaders feel responsible and followers feel reliant.

Leaders who are responsible and trusted are vigilant and agentic, attentive to turbulence in their environments and eager to steer their organizations through it. To do that well, they must make sense of what is happening around them; understand their own reactions, fears, and impulses; and act according to their sense of what must be kept running and what needs to change. However, that capacity often eludes them just when they need it most: When uncertainty rises, they care, and anxiety follows.

It can happen to leaders, we observed, when their organization’s performance flounders. When leaders care, those dips challenge more than their reputation. They threaten their sense of self. Performance anxiety morphs into existential anxiety (a concern that one might not be the leader that one aspires to be) and social anxiety (a concern that one might not be the leader others believe and expect them to be).

In the professional services firm mentioned at the beginning of this article — we’ll use the pseudonym “Recco” — we observed that as its growth slowed, executives acknowledged those worries privately but would not share them with one another. As one confided, “I don’t want to look like a chump because everyone can deliver … and I can’t.” We have heard similar concerns in our executive development work. People in leadership roles often feel uncomfortable or, more precisely, embarrassed to admit anxiety — even to themselves.

Sometimes the reason is personal: A work predicament may trigger anxiety tied to particular sensitivities. More often, the reason is cultural. In many organizations, admitting anxiety is disqualifying for leaders; a common reason executives seek coaching, we have observed, is to discuss their anxieties in private. Such cultures estrange anxiety from leadership, turning a normal, common, and often useful human experience into a problem.

Leaders with a modicum of humanity are bound to worry on occasion that they might fall short or let others down. If simply experiencing that worry feels like a failing, leaders may lose the capacity to modulate and make sense of anxiety, and it can become overwhelming. If leaders believe that their job is to ooze confidence and align people behind a vision of change, that becomes their coping strategy. Anxiety then shapes the vision and the choices that flow from it, and defensive organizing is on its way.

Tracing the Path of Defensive Organizing

Defensive organizing follows a route that begins with senior leaders and spreads across the organization as more people are drafted into the effort to keep anxiety at bay. Our research allowed us to draw a road map of sorts that will help you recognize the signs that you are being led down that route and find the exits at different points.

The comforting vision. Defending against anxiety often takes the form of adopting a reassuring narrative. The first step of defensive organizing therefore requires leaders to convince themselves that performance is ailing for reasons unrelated to them and that they can do something to reverse course. In our study, Recco’s CEO concluded that the firm’s hub-and-spoke structure was too rigid for a fast-changing market. At a strategy retreat, he proposed breaking down silos, operating as a shared leadership team, and championing collaboration across the organization. We watched the word “collaboration” generate a ripple of excitement that seemed to make the palpable worry about the firm’s performance vanish.

The unquestioning enthusiasm of a group coalescing around an idea that affirms their leadership is the first sign of defensive organizing. It is a sign of idealization, a defense mechanism that protects us from the conscious experience of anxiety by crediting something — a principle, a process, a product — with having the potential to restore a bright future. In companies we have worked with, we have seen ideas like digital transformation or customer centricity serve as that rallying point. These mantras then spread through the organization, mentioned in every town hall, strategy document, and performance review.

Not every bold idea is a form of idealization. The sign that idealization might be taking hold is that people avoid asking for evidence that the idea will work. At Recco’s strategic retreat, no one challenged the CEO’s diagnosis or asked what sharing leadership would mean in practice. This lack of inquiry reveals the real purpose of many a strategic vision: to give leaders a story about the future that makes the present feel manageable.

At Recco, the idea of collaboration was plausible and the leader reasonable. The company’s rigid structure indeed limited its agility, and the CEO had a record of being a thoughtful and responsible boss, which made it harder to question his thinking. That silent participation is a form of unconscious collusion with the emerging defense. It is a scenario we have witnessed many times. The future is uncertain, a leader feels pressured to come up with a vision, and their team becomes relieved when they do. It is a plot twist everybody expects — the leader showing that “we can do something,” whether it is introducing a new focus on customer centricity, reorganizing to flatten a hierarchy, or making a large investment in artificial intelligence.

Idealization short-circuits sensemaking by locating the organization’s problems not in the competence of its leaders but in the structures that confine them. A fervor for planning without real action is another symptom. For months, Recco’s leadership team met to strategize collaboration initiatives while making very few changes to the way they ran their business.

The convenient foe. In the second stage of defensive organizing, leaders must find a reason for their vision’s failure to transform the organization’s fate — a reason that is, again, beyond them. That reason is usually found in other people’s lack of competence and care. Others, usually with less power, are drawn into the defensive drama to become targets of blame.

Growing uneasy about the lack of substantive progress, the Recco leadership team identified a group of “future leaders” among their direct reports to form project teams. On the surface, it looked like a textbook change process, thorough and participative. The teams were assigned transformation initiatives and tasked with presenting analyses and recommendations. But the more they took up their work, the easier it seemed for the leadership team to critique them.

The reason was that the data kept pointing back to the leaders’ work. While they championed collaboration, each executive held on to control. When one project team uncovered fierce competition between divisions for new recruits — with vice presidents cherry-picking the best candidates before others could see them — the leadership team attacked the messenger, with one even calling for a senior manager to be fired on the spot. The mirror the project teams were asked to hold up to the leaders presented an unflattering image, and the leaders declared it faulty.

This rise of intergroup tension is the second stage of defensive organizing. The mechanism underpinning it is projective identification, a defense in which one group, usually more powerful, unconsciously attributes to another group qualities they wish to disown in themselves. In Recco’s case, leaders anxious about their competence came to see others as incompetent and then acted in ways that undercut those others’ competence by putting obstacles in their paths, stoking frustration, or setting them up to fail.

We have seen this pattern repeatedly, and the result is resentment for the wasted effort and lack of appreciation. In this case, the leaders’ pushback was overt and intense, and the project teams soon reciprocated their acrimony. “I’m not prepared to put my head on the block for this,” said one team leader. “We are running around doing all this work and getting pushback. It’s not acceptable.” As the pattern continued, the teams stopped caring.

The empty ritual. As intergroup friction keeps everyone from learning, people get tired of conflict and tune out. They let the embers of embattlement smolder under the ashes of disillusionment and go through the motions of initiatives they barely believe in.

At Recco, the recognition that momentum was stalling, and the ongoing unconscious effort to deny having caused it, led the firm’s leaders to launch an internal communication campaign. All managers were asked to gather their teams for regular Monday meetings meant to reengage employees by reaffirming the strategy of collaboration and soliciting ideas to speed up its implementation. In another company going through a transformation lull, teams were cajoled to document small wins in short videos shared internally. Both initiatives had the appearance of change management best practice. In informal spaces, however, people were cynical and complained about the extra work.

This tension between public commitment and private disillusionment is often described as “change fatigue,” but it would be more accurate to call it “change initiative fatigue,” since what drains people’s energy is not actual change but dissonance. The rift between what people say in the engagement meetings and record in the videos, and what they feel and say in the corridor, is costly: for individuals, for their relationships, and for their organizations.

The mechanism underlying this stage of defensive organizing is the production of a secondary problem, where the very principle, process, or practice once idealized comes to be regarded as an obstacle. At Recco, this was collaboration, which soon began to draw blame for the company’s inertia. Leaders complained that involving more people slowed them down. Senior managers resented the extra demands it placed on them. Worrying about the downsides of collaboration became a substitute for confronting what truly ailed the firm.

This is the moment when the purpose of defensive organizing is revealed. It does not produce any substantive change. It produces a shared problem of the organization’s own making. Anxiety stays covered, positions stay intact, and everyone feels justified in looking inward.

The reckoning. Failure to make substantive changes — and the depletion of both the energy and the meaning that keeping the status quo entails — eventually endangers the organization, eroding its ability to deliver. When that occurs, we found, people may resort to a defense of last resort: scapegoating. The leader whose vision had ignited hope becomes the lightning rod for everyone’s blame.

At Recco, as performance continued to falter and disillusioned employees put little effort into the transformation projects, pressure mounted among executives. The CEO set up a change oversight group to prioritize initiatives. It made no headway, however, and the leadership team gave the work back to the CEO, asking him to prioritize the initiatives alone. At their next meeting, he presented a spreadsheet scoring 16 initiatives against seven criteria. The team stared in bewilderment. Thirteen were identified as high priority. “So we are prioritizing everything,” one vice president said flatly. “How does that work?”

A long silence followed. The CEO broke it with emotional honesty, at last: “I feel set up.” He had been, and he had been an active if unconscious participant in that setup. The effort that had begun three years earlier with a room full of hope ended with one man holding a spreadsheet that his team openly mocked. Shortly after that, the firm was acquired and the leaders lost their jobs. The tragedy they quietly feared eventually came to pass.

The Recco story is not unique. Nokia’s mobile phone business, for example, followed a similar trajectory between 2005 and 2010. As Timo Vuori and Quy Huy have documented, Nokia’s senior leaders became caught up by worries about competitors’ smartphones while middle managers worried about the leaders’ opinions. Those unacknowledged worries led the executives to abandon a promising internal platform and bet on the Windows Phone operating system, with little pushback from their skeptical direct reports. Windows Phone never took off, and Nokia’s mobile phones are mostly a fond memory for those who owned one before the iPhone launched.

Defensive organizing distorts leaders’ sensemaking — that is, the effort to interpret what is happening and act accordingly. Sound sensemaking requires paying attention to strong signals and weak ones. Strong signals are visible and urgent: the performance gap, the warring teams, the stalling initiative. Weak signals are subtler and easily ignored: the worries that no one shares, the barely disguised cynicism, the feelings of going through meaningless motions, the sheer exhaustion. Defensive organizing makes leaders focus on strong signals and ignore weak ones. People are more likely to fall prey to it when they cannot admit to, share, examine, and process the anxiety that comes with living and that leading only amplifies.

The Anxiety of Leading

Our research was a study of failure. We documented a leadership team defeated by anxiety. However, much work in clinical psychology and organizational behavior offers useful pointers for countering defensive organizing. Dealing with anxiety productively, that work suggests, does not require strategic or structural changes. It requires changes in mindset and relations. Leaders must stop estranging anxiety and begin to befriend it instead, turning anxiety into an inevitable and even informative experience that is best managed by sharing it with others.

Working well with anxiety requires three capacities that leaders must cultivate within themselves and demonstrate around them: the courage to recognize anxiety, the curiosity to inquire about it with others, and the care to see and soothe distress. Each of those capacities counters one of the stages of defensive organizing we described above, whereas a capacity’s absence helps that stage take hold. What follows is a brief guide for cultivating and demonstrating those capacities.

Brave the worry. The first exit from defensive organizing involves challenging the heroic stereotype of leadership. In our study, executives on the leadership team acknowledged their anxiety in private, but they did not share it. Like many leaders we have worked with, they saw their worries as shameful weaknesses that a strong leader should keep under control.
As neuroscientist Joseph LeDoux discovered, however, human brains are not designed to control anxiety. Our worries are too useful for survival. It is anxiety that can hijack our brains. When anxious brains belong to leaders, we found, anxiety hijacks organizations, too.

While anxiety can hardly be controlled, it can be managed. Doing so requires a different kind of courage than the one featured in heroic leadership portraits — the kind for which scholars have borrowed John Keats’s poetic term “negative capability.” That is the courage to be with one’s experience: to recognize without judgment or reaction when and how one gets anxious. Perhaps it manifests as difficulty sleeping, drinking more than usual, or feeling restless and ill at ease.

Reframing anxiety from a personal failing or a dire prediction to a simple signal that requires attention and interpretation, like all other data, makes it easier to demonstrate courage too. You might say, for example, “Before I share my vision for what we might do, I want to say that I find our situation worrying, and I wonder if others do too.” Then invite a conversation about why we worry and what that signal says about the team and its circumstances.

This is not about being vulnerable; rather, it’s about showing the bravery to normalize acknowledgements of anxiety. Doing so can be framed as a sign of strength. “I do not want us to be the kind of leaders who jump into reckless action because we are too scared to think,” you might say. “I am confident that we can figure out what we are concerned about, share it with each other, and take care of it.”

The courage to be the first to say what everyone else is thinking is the antidote to idealization. It signals that no one needs to perform confidence they do not feel by feigning certainty they do not have. If covert anxiety hijacks sensemaking, emotional honesty frees it up again.

Find your truth tellers. In our study, the leadership team defensively dismissed feedback from senior managers who knew them well and were close to operations. But those people are a leader’s best allies to stress-test a vision.

Cultivating curiosity begins right after you find the courage to acknowledge your anxiety. Once you have a good understanding of what worries you, as discussed above, find allies you trust to share your anxieties with and to challenge you, your team, and the viability of your vision. Tell them that you care about their views and that you hope they care enough to tell the truth.

You are not asking for criticism or appointing a devil’s advocate; those only trigger more anxiety. You are asking for robust support. You are imparting the same emotional honesty that, ideally, you have cultivated and demonstrated with your team. You might seek feedback in two areas: What would have to change in the way you work, and the way you work with others, for the vision to succeed? And what would have to change in the way the business works? Then listen carefully. Pay attention to insights that you find unfounded or unpleasant. If the feedback seems inaccurate, ask yourself what data you could share to make it more precise. If it seems inconsiderate — that is, fails to recognize your own efforts and the difficulties you are facing — ask yourself whether you have shown how much you care.

Curiosity, like courage, requires one to suspend judgment. When you notice contempt for another group’s competence or commitment, ask not “How do we fix them?” but “What is this judgment protecting me from?” That question will help you avoid projecting onto others what you fear in yourself and will foster collaborations that modulate anxiety and unlock change.

Care visibly. The third capacity that helps leaders work with anxiety is care: giving it and receiving it. Cultivating care toward oneself, through mindful practices and supportive relationships, is essential to demonstrating care toward others. Leaders cannot care if they do not have energy, and they are seldom capable of showing care in ways that they have not experienced themselves.

Leaders who can acknowledge and investigate their own anxiety are likely to realize that others feel it too. One senior executive we know was aware that his anxiety sometimes made him initiate change, and that change became a source of anxiety for others while old ways were being dismantled and new ones were still works in progress. “If others help reassure me by going along, I need to reassure them by being there,” he noted. That comment captures what it means to work with anxiety — to care rather than defend against it.

During a long change process, a gap will often open up between public commitment and private concerns, between what is said in meetings and what is grumbled about in the corridors. You might be tempted to try to close that gap by pushing people to speak up about the actions they will take to accelerate change. What we found, however, is that those pushes ring hollow because they let people avoid a conversation about what they are feeling.

If you take time to genuinely care what people are feeling, you will find out what is working, what is not, and who needs what. The organization we studied failed to do this. It only needed its leaders to acknowledge that they had lost the plot and that they cared about their employees’ exhaustion, not just about their productivity. Instead, the leaders invested their care in devising a performative ritual to reengage the crew.

Better care can take two forms. One is interpersonal. It begins with asking, “Whose efforts are being ignored?” and showing them gratitude, or, “Who is feeling forced to pretend that they are OK when they are not?” and letting them know that they can stop. Another is institutional. It begins with asking, “What is working, and what is not?” and directing resources toward the former while pruning the latter. Both forms of care show that leaders are committed to supporting an ongoing change process, not imposing a change initiative.

Taking care to develop a culture where people can share their worries and what might be causing them is the best antidote against anxiety festering into a collective feeling that nothing matters, no one can be real, and the safest choice is to hide and wait. A culture that estranges anxiety constrains not only leaders but everyone from working productively with anxiety.

The Only Constant

When we say that anxiety is only human, we mean that it is inevitable in the life of an organism that can forge attachments and imagine the future. As people do both, they come to worry about loss — of face, of people, of activities that give them meaning, and of life itself. This is especially so when events remind them that loss is possible. Anxiety in the face of uncertainty, then, is not a leadership failure. It is a leadership condition. Our research and our work with leaders show that when they acknowledge uncertainty but deny anxiety, the latter becomes overwhelming and distorts how they deal with the former. It pushes people to make and to fall for empty promises, to blame others for their own failures, and to eventually withdraw.

When we look around in the workplace and beyond, we see those defensive patterns more and more. They draw energy from the shame of acknowledging anxiety. This article is an attempt to make anxiety less unthinkable or unspeakable, and less shameful. Anxiety can be informative and humanizing. If you meet it with courage, curiosity, and care when it emerges within you, you will be able to show the same capacities to people around you. You will grow into a more connected leader, and your organization will grow more adaptive for it.

John Pearson has been CEO of DHL Express and on the DHL Group board of management since 2019. He joined the global logistics and courier company in 1986 and has held senior management positions in its divisions in the Middle East, the Asia-Pacific region, the U.S., and Europe. MIT Sloan Management Review spoke with Pearson about DHL’s commitment to a corporate culture rooted in its purpose: “Connecting people, improving lives.” This interview has been edited for clarity and length.

What do you see as the role of corporate purpose in supporting alignment and performance at DHL Express?

John Pearson: It starts with our traditional four pillars: hiring motivated people, driving service quality, creating customer loyalty, and delivering a profitable network. I simplified that to three letters: P plus Q equals G — people plus quality equals growth. In both cases, we start with people — getting people engaged and being a great place to work.

People are at the center of everything we do, and what people relate to most is purpose: knowing what we turn up to work for, what we do every day.

There are two sides to purpose at DHL. One side is what we do every day: connecting people and connecting businesses. We know that society and countries are better off when they are more open and connected. The other side amplifies that through our Go programs that support employee volunteering and social impact: GoTeach, GoHelp, GoTrade, DHL’s Got Heart. The more I speak with young people coming into our organization, the more I realize that while they’re proud to say they connect countries and businesses, they get particularly motivated by that second side, and they ask for more volunteering time. Being a global company in 219 countries, which is the most global company of any, there is a lot of opportunity to volunteer in small and rather remote communities all around the world.

Does support for corporate purpose factor into managerial evaluations?

Pearson: We ask our leaders to take self-assessments where they rank themselves low, medium, or high on six leadership attributes. One is how much they manifest and develop the purpose of the company. Leaders have to give themselves one low mark and one high mark. It used to be that purpose was the default option for the low mark among about 60% of our executives; they might think, “Maybe I’ll put that as the low [attribute] because we haven’t talked about it much.” I raised this and talked about purpose with members of my team. The following year, that 60% dropped to 40%, and then it became around 20%.

Do you have advice for leaders who are considering how their organizations can express their purpose through action?

Pearson: You should use the assets that you have to define how your purpose is implemented. Our assets are aircraft, trucks, vans, and having people in just about every locale in the world. We are located on just about every runway on the planet. In partnership with the U.N., we’ve developed the GARD program — Get Airports Ready for Disaster. The program helps airports around the world be better prepared to manage incoming humanitarian aid and personnel when disaster response situations arise. This is a great example of using what you have in your toolkit to live your purpose.

Human beings are not good at separating fact from fiction. But surely hard-nosed B2B professionals are different? Our research suggests otherwise.

In two experiments with 622 B2B sales professionals, we gave participants a negotiation scenario in which a buyer either lied or told the truth. We then gave half the participants a story from the buyer — a brief anecdote about their company helping a farming family in need. The other half received no story.

Salespeople who read the story were 17% more willing to make concessions and developed 10% more trust in the buyer’s integrity. Surprisingly, the story worked just as well even when the buyer had lied.

Why? Psychologists call it narrative transportation: When people become immersed in a story, they momentarily lose sight of their context and their skepticism. Think of crying at a movie you know is fictional.

This vulnerability may become harder to guard against as AI tools proliferate. In a separate unpublished experiment, only 17% of 308 participants recognized that their negotiation counterpart was a bot. The inability to distinguish human from AI, coupled with our vulnerability to narrative persuasion, points to a risk that warrants attention as AI becomes more capable.

Together, these findings suggest that in negotiations, we are vulnerable to deceptive storytellers — human or not. Here are three things managers should do differently.

1. Don’t decide in the shadow of a story. Stories are most persuasive the moment they’re told, because that’s when human judgment is most impaired. Introduce a simple rule: No pricing or concession decisions during or immediately after listening to a storyteller.

2. Separate persuasion from verification. Assign someone on your team the explicit role of checking facts in real time, not participating in the negotiation. Their job is to surface inconsistencies while the main negotiator is in the flow.

3. Verify your counterpart, not just their claims. Don’t assume that you’re interacting with a human. For high-stakes negotiations, move to a video call or incorporate checks that require human judgment.

In a world where machines can negotiate on someone’s behalf, knowing when you’re being told a story — and by whom — has never mattered more.

Companies are betting that AI chatbots will deliver faster and cheaper customer service. But if you’ve ever tried to circumvent a chatbot and get to a human, you’re not alone. Here’s what three recent studies discovered about when customers will and won’t let AI do a human’s job.

1. Customers avoid chatbots for two compounding reasons. In a study simulating a customer service scenario, participants repeatedly chose between two unlabeled options: One required waiting in line before their request was resolved with certainty; the other skipped the line, but occasionally failed, routing the customer into the line for the first option. Researchers designed the choices so that a person optimizing for time saved should have picked each option about equally often. Instead, participants chose the no-queue option just 28% of the time — a reluctance researchers call gatekeeper aversion, driven by its uncertainty and multistage structure, regardless of who or what runs it. When that same no-queue option was presented as a chatbot rather than a person, adoption fell by another 10 to 20 percentage points — a separate effect called algorithm aversion. Follow-up experiments hint at possible remedies: Offering transparency about what the chatbot can and can’t do, and showing customers the expected wait time for each option, appears to increase chatbot uptake.

2. AI is a better messenger for bad news; humans, for good news. Across several experiments, customers who received a worse-than-expected offer (say, a low resale price) were more likely to accept it from an AI than from a human agent. In one study, 78.6% accepted an AI’s offer, versus 60.4% for a human’s offer. But in another study, when the offer was better than expected, the human agent’s offer was accepted 89% of the time, versus 76% for the AI. The reason: People don’t ascribe human intentions to AI, so don’t regard it as “selfish” when it lowballs them, nor as “generous” when it overdelivers. The effect is strongest when the AI is presented as machinelike; a humanlike persona erodes the advantage for delivering bad news.

3. A simple two-question test can predict whether customers will embrace or reject an AI. A meta-analysis of 163 studies involving over 82,000 participants found that customer preference for AI over humans comes down to two factors: whether the AI is seen as more capable at the task than a person, and whether the task is seen as requiring personalization. When AI is seen as more capable and personalization is seen as unnecessary, such as when forecasting sales or playing chess, people prefer it. In every other combination, people favor humans out of a desire for individualized treatment. Before automating a customer-facing role, leaders should weigh AI’s capability against customers’ expectations of personalized service.