Via Brian Solis, Forbes

Every week, another enterprise and frontier company announces a new AI model, another copilot, another assistant, another agent. And yet, when you ask leaders whether their organizations are actually operating differently, whether decisions are faster, outcomes more autonomous, models of work truly reinvented, the honest answer is often a hesitant, “no.”

This creates an enterprise transformation gap between AI that thinks at the individual level and AI that can execute across workflows. Companies are investing billions, generating more intelligence than ever before, and yet transformation remains limited to compartmentalized productivity gains vs. transformational business performance.

The gap between knowing and doing is an architecture gap. And closing that gap starts with a more honest question than most leaders are asking. Many ask which AI they should deploy, but AI-forward executives are asking, “are we building the organizational architecture that allows AI to act with confidence, at scale, within the governance structures our business requires, and in genuine partnership with people?”

Let’s Start With What AI Is Not

AI is not a new automation to replace human potential. AI should eliminate the mundane work to free human capacity to create new value, not just speed things up. Repetitive tasks, manual coordination, routine decisions are AI’s domain. Creativity, judgment, innovation, empathy, and relationships remain distinctly human. The real opportunity is the exponential outcomes that humans and AI create together that neither could achieve alone.

That reframe changes everything about how leaders navigate the gap and reinvent their business, how work flows, and how people work with AI. It shifts the purpose of AI from technology implementation to an agent of possibility, where business and technology leaders can rethink enterprise transformation for a future that doesn’t yet exist, willing to let go of legacy thinking, to build systems that can think, learn, adapt, and act.

The organizations pulling ahead have stopped evaluating AI in isolation. They’re focusing instead on how AI, data, and workflows can work together to drive ROI in partnership with people. And the gap between those two approaches is widening fast.

The Productivity Trap Is Real, And Most Companies Are In It

Let me tell you about a scenario I’ve seen play out in organizations across industries. A company invests heavily in a modern data stack. They build dashboards. They deploy predictive analytics. They launch an AI copilot that summarizes support tickets, drafts responses, flags anomalies. Productivity improves. The board is impressed. But then difficult questions surface: Did cycle times fundamentally change? Did headcount models allow for growth and value creation? Did the operating model actually evolve?

Most of the time, the answer is no, and that’s because data intelligence tells you what happened and what might happen next. It doesn’t have enterprise-wide context to tell you what should happen, who has the authority to make it happen, what policies govern it, or what systems need to coordinate to execute it. That connective layer is missing. And without it, costs don’t collapse, cycle times don’t reset, and operating models don’t bend.

Deploying more assistants doesn’t break through that ceiling. What breaks through is AI that’s embedded in the workflows and governance structures that define how your organization actually operates, so that it can act in confidence.

The Agent Sprawl Problem Not Enough People Are Talking About

Here’s where the story gets more complicated. Many organizations are starting to realize their existing systems aren’t transforming outcomes. As a result, they have begun layering AI agents onto existing systems perpetuating the AI gap and fortifying business and data. Ultimately this hinders enterprise-wide context and the ability for AI and people to execute workflows that span the entire business.

There are now agents for customer service, agents for procurement, agents for HR requests, agents for IT support. On paper, each one delivers value. In practice, they’re creating a new form of the same problem. A patchwork of disconnected intelligence that optimizes individual tasks while leaving the broader operating model untouched.

None of them share context. None enforce consistent policy. None produce a coherent audit trail across the processes they touch.

This is agent sprawl: more intelligence, more complexity, and no compounding value. You’ve traded one set of silos for another. An agent can complete a task. But completing a task isn’t transforming a workflow. When dozens of agents operate in isolation, the result is expensive fragmentation.

The real opportunity isn’t doing the same work cheaper or faster. It’s doing entirely different work at an entirely different scale.

Why Enterprise AI Needs a Unified Platform

The answer to agent sprawl isn’t necessarily fewer agents. It’s an AI platform that connects AI, data, and agents to the workflows, governance structures, and systems that give their actions meaning and accountability.

No foundation model, regardless of how large or capable, can supply these things from training. They have to be supplied by the platform in which the model operates.

This is why platform architecture is the primary lever of enterprise AI transformation.

The questions for executives to consider to close the AI gap and prevent agent sprawl asking are:

• “Does our AI architecture connect intelligence to execution, or does it stop at recommendation?”
• “Are our AI capabilities governed at the point of action, or are we relying on human review to catch errors?”
• “Are we compounding intelligence over time, or deploying point solutions that plateau?”

So what does that look like? A unified, AI platform does several distinct things that point solutions and standalone agents cannot.

It orchestrates and acts across systems. Most AI stops at the recommendation. A unified platform executes work end to end, across every system and department, from resolving an IT issue autonomously to updating a CRM record based on a customer signal.

It embeds governance at the point of execution. Governance has to be structural and built into every action the AI takes, ensuring systems, assets, and identities remain secure, compliant, and strategically aligned.

It blends deterministic workflows with probabilistic AI. Most enterprises are missing a critical capability: the ability to make AI reason with business accountability rather than probabilistic guesswork. Decisions need to align with your policies, behave predictably, and be auditable from end to end.

It learns. Most LLMs are trained on the internet. A unified platform gives AI your enterprise context, continuously discovering what exists across your business, how it’s connected, and what it means.

The Leadership Imperative

This AI revolution has the potential to elevate human capacity, but that vision only becomes real when leaders make a different kind of decision about what their organizations look like on the other side of AI business reinvention.

Ask different questions, such as “are we building the organizational architecture that allows AI to act with confidence, at scale, within the governance structures our business requires?” And “How are we pairing AI with purpose-driven people to boost productivity, accelerate creativity, and drive new value?”

Done right, AI reinvention opens the door to something much bigger than efficiency. It’s a full reimagining of how work gets done, who does it, and what becomes possible when humans and AI are designed to work together.

The companies that will define the next era of enterprise performance aren’t just investing in better frontier models. They’re building the data and workflow infrastructure that allows the models they have to deliver real outcomes that compound, scale, and create value that wasn’t previously possible. And they’re thinking about how employees can be augmented by intelligent systems to become innovators, orchestrators, and decision-makers.

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The post Forbes: Why Leaders Are Prioritizing AI Platforms And People To Reinvent Their Business appeared first on Brian Solis.

Via Cointelegraph, MSN, by Aaron Wood

In a recent Cointelegraph article, “Reality of AI’s impact on employment clashes with C-suite optimism,” the story argues that the promised benefits of AI are colliding with a more difficult reality in the workplace. While executives remain bullish on AI’s potential, the article points to weaker entry-level hiring, uneven employment growth in tech, and growing evidence that AI tools often create extra rework instead of clear productivity gains. It also highlights research suggesting that many workers are experiencing more frustration, not less, as AI becomes embedded in daily workflows.

Brian Solis is cited for exploring this growing burden as an “AI tax.” His description captures the hidden costs many teams are feeling: “More checking. More rework. More anxiety. Faster pace. AI slop. Less trust.” His quote reinforces the article’s central point that the real-world impact of AI inside organizations is often far messier than top-level optimism suggests.

Read the article here.

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Wish list for diana

Two Wall Street Journal stories got me thinking. When you read them together, they explain why so many companies feel stuck with AI, or feel further along than they really are.

One story quotes tech leaders arguing that what a CEO does might be “one of the easier things” for AI to do. Sundar Pichai said it. Sam Altman doubled down and talked about an AI running divisions, even entire companies, with decision-making getting “pretty good, pretty soon.”

The story is designed to provoke. The argument is if leadership is about updates, approvals, reviews, escalations, and market-facing narratives, it starts to look like a workflow. Workflows get engineered. Workflows get optimized. And, workflows get automated.

The other story shows something even more consequential happening right now inside real organizations: executives are increasingly confident about AI’s impact, while employees describe a very different day to day. This is a notable, but all-to-common AI leadership gap.

Two Realities Inside the Same Company

In the WSJ reporting, a Section survey of 5,000 white-collar employees found that two-thirds of nonmanagement workers said AI saves them less than two hours a week, with 40% saying that it saves them no time at all. On the other hand, 20% of employees say that AI saves them two-to-four hours per week. But does it really though.

This is just a personal observation. AI promised to free up time for creative thinking and higher-purpose work. Yet, a lot of high performers I talk to and work with find that AI makes them even busier!

In the same reporting, 33% of C-suite executives said AI saves them four to eight hours per week, another 24% said eight to twelve hours, and 19% said more than twelve hours.

At the same time, the Section AI Proficiency Report shows how different the emotional experience is depending on where you sit in the org chart. Individual contributors report being anxious or overwhelmed at far higher rates than the C-suite. In Section’s data, individual contributors show 68% anxious and 32% excited, while the C-suite shows 26% anxious and 74% excited.

So when leaders ask, “Why is adoption slower than expected?” or “Why are teams not moving faster?”, the answer is that people do not scale what they do not trust. They do not lean into what they fear. They do not volunteer for change when the consequences or upsides are unclear.

The AI Tax Is the Quiet Killer of Momentum, and Excitement

There is another force at work, and it explains why “time saved” can feel true in one meeting and false in the next.

Workday calls it an AI tax. This is the hidden cumulative costs and inefficiencies that organizations, and people, incur (and feel) when productivity is only measured by output, not quality or performance. The AI tax is levied when people have to spend unplanned time editing and correcting, verifying, and reworking AI-generated content.

For example, if you use AI to produce content and you don’t do the work to vet it before sharing, you are imposing an AI tax on your colleagues. And over time, that tax erodes trust and confidence in you. Who can afford that?
Their research calls out that for every 10 hours of productivity gained, about four hours are paid back in rework, correcting, clarifying, and refining AI output. This equates to a loss of speed and performance, even though you’re moving faster, while creating the need for an unplanned verification layer, and introducing a trust gap between people.

This is what many executives never see because it does not show up cleanly in a KPI. Drafting gets faster. Reviewing gets heavier. Output increases. Accountability becomes more fragile. Teams move quicker, and then spend the reclaimed time auditing, fixing, and defending the work.

Work does not disappear. It shifts. And it often shifts onto the people with the least margin, the least time, and the least psychological safety to take risks.

Section’s findings reinforce this new reality in a different way.

Most workers are still using AI for very basic tasks, and the time savings reflect that. Their report shows a large share of the workforce saving little or no time, and it also captures a blunt sentiment: 40% said they would be fine never using AI again.

Translation: many employees are resisting because the experience is not yet designed to earn trust and reduce friction.

Leaders are enthusiastic (even if they’re under pressure to accelerate adoption). Yet, in reality, employees are overwhelmed, overloaded, and unclear.

Fear Is Part of the Adoption Curve

The WSJ also cites a WSJ-NORC poll in which six in 10 respondents characterized AI and other new technologies as mostly a threat to the U.S. economy because of its potential to replace well-paid workers.

So yes, adoption slows. It’s not because people can’t or don’t want to learn, it’s because people are doing the math in their heads. It’s also what they feel. They are trying to figure out whether AI is meant to help them, measure them, or replace them, in their work.

And if leaders are serious about adoption, they cannot outsource this to comms, training videos, or mandates. The organization needs a shared language for what AI is for, where it fits, what “good” looks like, and what happens when the system is wrong.

If AI Can “Do the CEO Job,” What Should the CEO Become?

Let’s revisit that first WSJ story. Leaders can’t just focus on adoption and acceleration. They need to look in the mirror to understand how AI is evolving decision work.

In an AI era, the CEO becomes less of a lead decision-maker and more of a system architect.

The CEO’s advantage will come from designing how decisions happen, not merely being present when decisions are announced. It comes from building the conditions for trust, not just demanding speed. It comes from creating learning goals and loops, not just reviewing quarterly outputs.

That is what AI fluency means at the top of the house. It’s not just about adoption. It’s elevation and defined standards.

Section’s report captures part of the problem: many executives believe deployments are succeeding even while the rest of the organization disagrees.

What CEOs, and Their Advisors, Should Do This Quarter and the Next

If you are a CEO, board member, CIO, CHRO, COO, CAIO, or a transformation leader, treat this as an operating model redesign, not an AI strategy.

Start with reinvestment. If AI is giving leaders back hours every week, those hours are strategic capacity. Put them into redesigning the work itself…defined vision and strategy, clearer standards, better training that maps to roles and goals.

Upgrade your metrics. Hours saved is a vanity metric if the AI tax is quietly reclaiming the gains through rework. Measure the net value, including the time lost to correction, verification, and refinement. Be honest about it.

Close the fluency gap with clarity. The workforce needs to know where AI is expected to assist, where human judgment is required, how outputs are evaluated, and how accountability works when AI is wrong.

That is how trust is built. Trust is what scales adoption.

AI is changing work. At the same time, it is revealing leadership, or the absence of it.

It reveals the vision leaders have for where AI can take the company. It shows whether a company understands its workflows well enough to redesign them. It surfaces whether leaders are measuring the right things. It also reveals whether the organization has the courage to talk honestly about fear, uncertainty, skills, and the future of roles and the division of tasks between people and AI agents.

If AI ever “takes” a CEO job, it will be because leadership stayed static while everything else evolved.

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Photo: Freepik

By Gustavo Meirelles, Futuro da Saúde

After a few years of enchantment with artificial intelligence, 2026 begins to impose an adjustment of expectations. At SXSW, in Austin, the discourse changed: less dazzle, more questioning.

One of the most relevant provocations came from anthropologist and futurist Brian Solis, in the session “Augmented IQ: Scaling Human + AI Potential”. Solis went straight to the point, and brought a necessary annoyance to the audience: we are using AI to automate the past, not to build the future. And the consequence is deeper than it seems. Instead of expanding capacities and optimizing processes, many organizations are just accelerating old models, now at scale, often generating superficial content without creativity.

It is in this context that the concept of QIA (Augmented Intelligence Quotient) gains strength. After IQ (Intelligence Quotient), QE (Emocinal Quotient) and QS (Social Quotient), the differential becomes the ability to use AI to enhance human thinking – without outsourcing it.

Solis’ central alert is the risk of the so-called “Cognitive Darwinism”. As professionals delegate reasoning to AI, they lose exactly what differentiates them: critical thinking, repertoire and interpretation capacity.

The signs are already everywhere. The growth of the so-called AI Slop – shallow, homogeneous and uncured content – and a diffuse sensation of cognitive fatigue, the AI Brain Fry. Everyone produces more. Not everyone produces better.

The impact is not only creative, but also relational. As content is standardized, trust weakens. It becomes more difficult to know what was thought, what was just generated – and, especially, who to trust.

The promise of productivity also begins to show cracks. A Wall Street Journal survey, presented by Solis, showed that 19% of C-level executives perceive earnings greater than 12 hours per week with AI. However, among other professionals, this number drops to 2%. Efficiency, at least for now, is not equally distributed.

There is also an invisible cost: the so-called “AI Tax”. Estimates suggest that up to 40% of productivity gains are consumed in the review, correction and validation of what the AI itself produces. Instead of exponentiality, many teams operate in rework mode.

In marketing and communication, the risk is clear: the homogenization of brands. If everyone uses the same tools, with the same prompts, the result tends to converge, generating what another speaker, Gulay Ozkan, called “The Age of Sameness” (The Age of Sameness).

In health, the alert is even more sensitive. The risk is not only of standardization, but of erosion of clinical reasoning – replaced by automated responses that do not always capture the complexity of care.

The way out is not to slow down the AI, but to reposition it. Automate what is repetitive, such as processes, flows and organization. And, more than preserving, expanding what is human: creativity, empathy, curiosity and vision.

As Solis summarizes: “The future of AI does not belong to those who ask for answers, but to those who ask better questions.” In the end, the discussion ceases to be technological and becomes strategic. It’s not enough to adopt AI. It is necessary to develop increased intelligence. Those who use technology only to repeat the past may even gain efficiency – but will hardly build relevance in the future.

“The future of AI does not belong to those who ask for answers, but to those who ask better questions.”

Mais que IA: por que saúde e comunicação correm risco sem a Inteligência Aumentada

Depois de alguns anos de encantamento com a inteligência artificial, 2026 começa a impor um ajuste de expectativas. No SXSW, em Austin, o discurso mudou: menos deslumbramento, mais questionamento.

Uma das provocações mais relevantes veio do antropólogo e futurista Brian Solis, na sessão “Augmented IQ: Scaling Human + AI Potential”. Solis foi direto ao ponto, e trouxe um incômodo necessário para a plateia: estamos usando IA para automatizar o passado, não para construir o futuro. E a consequência é mais profunda do que parece. Em vez de ampliar capacidades e otimizar processos, muitas organizações estão apenas acelerando modelos antigos, agora em escala, muitas vezes gerando conteúdos superficiais e sem criatividade.

É nesse contexto que ganha força o conceito de QIA (Quociente de Inteligência Aumentada). Depois do QI (Quociente de Inteligência), do QE (Quociente Emocinal) e do QS (Quociente Social), o diferencial passa a ser a capacidade de usar IA para potencializar o pensamento humano — sem terceirizá-lo.

O alerta central de Solis é o risco do chamado “Darwinismo Cognitivo”. À medida que profissionais delegam o raciocínio à IA, perdem exatamente aquilo que os diferencia: pensamento crítico, repertório e capacidade de interpretação.

Os sinais já estão por toda parte. O crescimento do chamado AI Slop — conteúdo raso, homogêneo e sem curadoria — e uma sensação difusa de fadiga cognitiva, o AI Brain Fry. Todo mundo produz mais. Nem todos produzem melhor.

O impacto não é apenas criativo, mas também relacional. À medida que o conteúdo se padroniza, a confiança se fragiliza. Fica mais difícil saber o que foi pensado, o que foi apenas gerado — e, principalmente, em quem confiar.

A promessa de produtividade também começa a mostrar fissuras. Uma pesquisa do Wall Street Journal, apresentada por Solis, demonstrou que 19% dos executivos C-level percebem ganhos superiores a 12 horas semanais com IA. Contudo, entre os demais profissionais, esse número cai para 2%. A eficiência, ao menos por enquanto, não é igualmente distribuída.

Há ainda um custo invisível: o chamado “AI Tax”. Estimativas sugerem que até 40% dos ganhos de produtividade são consumidos na revisão, correção e validação do que a própria IA produz. Em vez de exponencialidade, muitas equipes operam em modo retrabalho.

No marketing e na comunicação, o risco é claro: a homogeneização das marcas. Se todos usam as mesmas ferramentas, com os mesmos prompts, o resultado tende a convergir, gerando o que outra palestrante, Gulay Ozkan, chamou de “The Age of Sameness” (A Era da Mesmice).

Na saúde, o alerta é ainda mais sensível. O risco não é apenas de padronização, mas de erosão do raciocínio clínico — substituído por respostas automatizadas que nem sempre capturam a complexidade do cuidado.

A saída não está em frear a IA, mas em reposicioná-la. Automatizar o que é repetitivo, como processos, fluxos e organização. E, mais do que preservar, ampliar o que é humano: criatividade, empatia, curiosidade e visão.

Como resume Solis: “O futuro da IA não pertence a quem pede respostas, mas a quem faz perguntas melhores.”. No fim, a discussão deixa de ser tecnológica e passa a ser estratégica. Não basta adotar IA. É preciso desenvolver inteligência aumentada. Quem usar a tecnologia apenas para repetir o passado pode até ganhar eficiência — mas dificilmente construirá relevância no futuro.

Gustavo Meirelles, vice-presidente Médico da Afya, é graduado em Medicina pela Universidade Federal de São Paulo. Possui MBA em Gestão Empresarial pela Fundação Getúlio Vargas (FGV) em São Paulo e realizou um pós-doutorado em PET/CT no Memorial Sloan-Kettering Cancer Center, em Nova York. É copresidente do Instituto Afya e reitor da Afya Universidade Unigranrio.

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CX Network announced its guide to the top 50 AI leaders to follow in CX for 2026 and Brian Solis is on the list!

his year’s list highlights individuals from across the globe who are redefining how technology and human insight come together to elevate customer experiences.

As artificial intelligence (AI) accelerates across industries, its promise to transform how we work comes hand-in-hand with very real concerns about unemployment: recent data from the National University suggests that 30 percent of current jobs could see significant automation by 2030, with routine roles – including some customer service and data entry roles – especially exposed to change.

This list highlights leaders who aren’t simply responding to AI disruption, but harnessing its potential to drive more empathetic, effective, and human-centered customer experiences. From strategic thinkers to ethical AI advocates, these voices are guiding the future of CX.

Nominations for this year’s list were gathered through a global outreach campaign across CX Network’s digital channels. The nomination period remained open for several weeks, inviting submissions from across industries, regions and roles. The approach ensured a diverse and representative pool of candidates, reflecting the breadth of innvovation happening in AI-driven CX today.

To compile this list, the CX Network team evaluated leadership impact, influence on AI-powered CX strategy, contributions to the broader industry conversation, and demonstrated innovation across people, process, and technology. Listed alphabetically, each profile includes key insights and curated content to help you learn about the most forward-thinking leaders in the space.

Brian Solis, ServiceNow

Head of global innovation at ServiceNow, and author of X: The Experience When Business Meets Design, Brian Solis challenges leaders to rethink not just what they build with AI, but why they build it.

For Solis, AI is not a CX strategy, but an enabler of strategy that would otherwise be impossible. He argues that success should be measured the way customers actually feel and remember: “Did it reduce effort, speed up resolution, and increase trust?”
He also pushes for an “experience integrity score” that measures whether AI explains its actions in plain language and truly removes friction, not just automate yesterday’s processes.

Speaking on agentic AI, Solis told CX Network:

“AI flips CX from reactive support or transaction commerce or engagement to proactive experience design, where the best interactions are the ones customers never have to initiate. It also means the customer journey is increasingly mediated by agents, so brands must earn machine trust with clean customer and intent data, transparent policies, and experiences designed for both humans and their AI copilots (that’s right, we’re talking about CX and now AX…agent experience and how you deliver seamless experiences for agents!). In that world, experience becomes a customer and agentic operating system, and AI is the choreography behind every moment that matters.”

Our top pick of Brian Solis’ content:

• LinkedIn newsletter: ROI: Return on Ignorance 
• Find out what Brian Solis is talking about on LinkedIn

The post CX Network Names Brian Solis a Top AI Leader in CX To Follow appeared first on Brian Solis.

IKEA deployed an AI chatbot named Billy to handle level-one customer service inquiries. It reportedly resolved around 47% of those engagements without human escalation.

Most companies would have celebrated the labor savings and stopped there. Cost takeout right?

But the more interesting move was to study the other cases Billy could not resolve. Those unresolved inquiries pointed to customer demand for interior design help.

IKEA responded by spinning up a design consultancy, reskilling customer service employees powered by AI, and creating a new revenue stream that generated roughly €1 billion in new revenue in its 1st year.

Automation + Augmentation = Exponential Growth

Within 24 hours, the post went viral, hitting almost half-a-million views, 275 reposts, 2.2k likes, and over 60 comments.

This incredible IKEA story has since dozens of posts and articles in LinkedIn and in the media…including in India!

India Today

People Matters

Full presentation here.

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The post IKEA AI Customer Service Story Goes Viral Because The Company Reskilled Staff Instead of Laying Off Employees appeared first on Brian Solis.

via Avasant

As companies worldwide grapple with AI implementation, a critical gap has emerged between executive ambitions and organizational reality, revealing the urgent need for a fundamental shift in how we approach AI-driven change.

At Avasant’s recent Empowering Beyond Summit 2025, Brian Solis, Head of Global Innovation at ServiceNow and nine-time bestselling author, delivered a compelling case for why businesses must disrupt themselves to fully realize AI’s transformative potential. His insights illuminate the path forward for organizations seeking to move beyond superficial AI adoption toward true business transformation.

The Innovation Imperative: Why AI Demands a Different Approach

AI presents an opportunity to reimagine business models entirely. However, most organizations are falling into familiar patterns, using AI as a sophisticated co-pilot to execute yesterday’s workflows more efficiently rather than exploring genuinely new possibilities.

“We’re not being bold enough. We’re not being visionary enough, and we are falling into the habits that we have had during every technological revolution, to fit it into the box of business as usual,” Brian Solis observed.

Despite AI being mentioned 30,000 to 40,000 times in earnings calls during 2023–2024, with CEOs and CFOs touting it as a competitive advantage, business leaders privately express ambivalence or outright dissatisfaction with their AI transformation progress. Only 1% of companies believe they have achieved AI maturity, highlighting the vast gap between aspiration and execution.

The Leadership Gap: Vision Versus Reality

Research reveals a stark disconnect between executive perceptions and organizational reality. While 73% of business executives feel their company’s AI approach is well-controlled and strategic, employees remain largely unaware of these initiatives. Similarly, 75% of executives claim success in adoption of AI, but this confidence isn’t shared by their workforce.

“The consensus is that the biggest barrier to scale isn’t employees. It certainly isn’t the technology. It is the executives leading the effort. They’re not steering fast enough. They’re not thinking big enough.”

Learning from Venture Capital: A Framework for Bold Thinking

To overcome these limitations, Solis advocates adopting the venture capital mindset when approaching AI transformation. Unlike traditional business leaders who focus on proven use cases and incremental improvements, venture capitalists evaluate investments based on their potential to create entirely new markets and deliver exponential returns.

“Venture capitalists have a formula for assessing their investments, they’re not looking for 5x or 10x returns. They’re looking for 1,000x return over the long term.”

This mindset requires organizations to explore the unknown, take calculated risks, and prioritize innovation over predictability.

The AI-First Mindset: Redefining Business Strategy

Companies like Box, Shopify, and Duolingo have begun embracing “AI-first” approaches, fundamentally reorganizing their operations around AI capabilities rather than simply adding AI to existing processes. This shift requires leaders to ask fundamentally different questions:

“What could we achieve utilizing AI at the core of our business model from day one?”
— A question that reframes strategy.

This mindset moves organizations from automation to augmentation, where AI opens the avenue to opportunities humans hadn’t fully realized before. IKEA’s transformation illustrates this perfectly. When their AI chatbot “Billy” began handling 57% of customer inquiries, management faced a choice: cut costs by reducing staff or reimagine the role of their people. They chose the latter. By analyzing Billy’s conversation logs, they noticed a recurring pattern, customers were seeking personalized design guidance, not just product information. Rather than ignore this unmet demand, IKEA reskilled their call center staff into remote interior design consultants. This pivot turned an efficiency tool into a growth engine, launching a €1 billion service line in less than two years. The key wasn’t the chatbot itself, instead it was leadership’s willingness to treat AI as a signal for new value creation rather than just a cost-saving mechanism.

Iterative vs. Innovative AI: The Dual Path to Transformation

Solis’s research identifies two complementary approaches to AI implementation:

Iterative AI: Optimizes existing workflows, reduces costs, and improves efficiency. It’s foundational, delivering predictable returns through automation.
Innovative AI: Explores new possibilities, creates novel workflows, and enables new business models. It requires risk but offers exponential potential.
Organizations that combine both approaches create a “disruptive layer” that enhances operations while opening new revenue streams. Those focused only on iteration may soon be left behind as competitors achieve transformation.

Building the Foundation: Culture and Psychological Safety

Transformative AI requires cultural evolution. Google’s research on high-performing teams revealed that psychological safety, not education or experience, was the strongest predictor of innovation success.

“The highest performing teams out innovated everyone else because they felt psychological safety.” A culture that encourages curiosity, risk-taking, and challenging assumptions is critical to scaling AI beyond pilot projects. Psychological safety isn’t built by slogans, instead it’s cultivated through deliberate leadership behaviors. This means leaders model openness by admitting when they don’t have all the answers, rewarding experimentation even when results are inconclusive, and creating spaces where employees can propose unconventional ideas without fear of embarrassment or penalty.

For AI specifically, this often includes “sandbox” environments where teams can prototype AI-driven solutions without risking live operations, as well as cross-functional workshops that pair domain experts with technologists to explore new use cases. The goal is to make questioning the status quo not just safe but expected.

Conclusion: The Choice to Transform

The AI revolution gives organizations a choice: optimize the past or build the future. “There can be no revolution if we don’t persuade ourselves to disrupt ourselves, to explore new horizons in ways that uncover new opportunities.”

As Vinod Khosla aptly warned, “Most businesses have no clue what is about to hit them in the next 10 years when most rules of engagement will change.” Those who embrace transformation, who adopt an AI-first mindset and combine bold vision with operational clarity, will lead the future of business.

The choice is clear: disrupt yourself or be disrupted.

Watch the full keynote…

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The post Beyond Digital Transformation, The AI-First Business Revolution appeared first on Brian Solis.

I recently sat down with Geoff Nielson on Digital Disruption, produced by Info-Tech Research Group, for a conversation that went far beyond the usual AI headlines. We talked about what AI is actually changing inside the enterprise, why so many organizations are mistaking activity for progress, and what leadership has to do with whether AI becomes a force for optimization or reinvention.

Please do watch the conversation (video embedded below). It’s fun and rich with insights from the frontline of business transformation. You can also listen on Spotify.

The Real Disruption Is Not the Technology, It’s the Obsolescence of Old Thinking.

I’ve long defined disruption as doing new things that make old things obsolete. That’s AI if we think about it in the right now. It’s disruptive because it is changing behavior, judgment, work, and even confidence in ways many leaders still underestimate. In our conversation, we explored everything from AI sycophancy to AI atrophy to “capability overhang,” the widening gap between what AI can actually do and how narrowly most people still use it. That overhang is where the next competitive divide is forming.

That’s the part many organizations still don’t see. Yet disruption has already underway. It’s already reshaping how a small group of power users, AI-native founders, and forward-looking teams think, decide, build, and move. The threat is that leadership assumptions are not shifting fast enough, or moving at all.

The AI Maturity Wake-Up Call Should Concern Every Executive

One of the most revealing parts of the conversation centered on the ServiceNow AI Index. And I’m proud to say that I helped develop the foundational model in 2023. In the second annual installment of the AI Index, we learned in 2025, the average AI maturity score came in at 35 out of 100, down from 44 out of 100 in 2024. It was a sign that most organizations are still early, struggling to keep up, and still far from where they need to be.

In one year, frontier models advanced rapidly, AI agents became tangible, and the conversation shifted from experimentation to enterprise-grade accountability. Governance, trust, security, compliance, and risk moved from side conversations to core requirements. In other words, many companies didn’t step back because AI lost momentum. They stepped back because they finally realized how much deeper this transformation goes. It was, as I shared in the interview, a regression for the right reasons.

That should not reassure anyone into complacency. We should use it as a wakeup call.

AI-Native Companies Are Not the Whole Story, But They Are the Warning Shot

There is a popular narrative right now that AI-native companies are coming to destroy incumbents and eat the lunch of every legacy business in sight. That storyline is catchy. It is also incomplete. Enterprises are not slow simply because they are outdated. They carry real obligations around governance, reporting, security, compliance, and resilience that startups do not have to navigate at the same scale. That doesn’t mean that legacy leaders and companies are in the clear.

You can’t use enterprise complexity as an excuse to remain architecturally timid.

The issue is whether legacy companies can move as imaginatively as startups while preserving the integrity of an enterprise-grade business. That is the actual challenge of this era. It’s not just about speed or efficiency. It’s about reinvention with accountability.

Most Companies Are Still Using AI to Improve Yesterday

This may be the biggest strategic blind spot in business right now.

Too many companies are applying AI to automate what was already digitized. That creates value, yes. It can reduce friction, lower costs, and improve efficiency. But that is only one side of the opportunity. The other side, and the one that will define market leaders, is using AI to create what was not possible yesterday. That is the difference between iteration and innovation. Between efficiency and growth. Between cost takeout and business reinvention.

If your AI strategy begins and ends with productivity, you may get short-term gains. But you will also risk locking your organization into a better version of an aging model. AI should force leaders to ask whether the current business, current workflows, and current measures of success are still the right ones at all.

IKEA Offers a Better AI Lesson Than Most Boardroom Decks

One of my favorite examples from the interview was IKEA (watch this short clip).

Its AI chatbot Billie successfully handled a meaningful portion of level-one customer service inquiries. Most organizations would have looked at that result and stopped at labor reduction. Case closed. ROI captured. Headcount rationalized. But the more interesting move was to study the unresolved cases. What the company found was that many of those inquiries pointed to customer demand for interior design help. That insight led to a new consultancy model, reskilled employees, and a meaningful new revenue stream.

While most companies ask, “How many people can AI replace?” Better leaders ask, “What unmet need is this revealing?” One question takes cost out. The other creates value.

Vision Is Still the Missing Ingredient

During the digital transformation era, many companies invested heavily without a clear view of what they were becoming. They digitized existing models instead of reinventing them. AI is at risk of repeating that pattern, only faster and with higher stakes.

That is why vision matters so much right now.

In the interview, I contrasted reactive leadership with directional leadership. I pointed to examples like IKEA, where opportunity emerged through exploration, and JPMorgan, where leadership articulated an ambition to become an AI mega bank. Execution matters, of course. But without a lighthouse use case, execution becomes motion without meaning. Too many organizations are still busy adopting AI without a coherent picture of what they want to become because of it.

AI Agents Are Forcing a Much Bigger Organizational Conversation

Once AI moves from assistance to action, everything changes.

An AI agent can start to resemble digital labor. It must be identified, trained, tuned, governed, deployed, managed, and assessed. That means the conversation cannot sit with IT alone. It increasingly requires HR, operations, risk, and executive leadership to work together in ways most organizations were never designed to do.

One of the most important ideas we explored in the interview is that agents are beginning to sit in a new Venn diagram between workforce management and software asset management. HR understands roles, skills, onboarding, and performance. IT understands assets, systems, controls, and orchestration. As agents become more capable, those worlds collide. That is why I believe one of the defining shifts of this next phase will be much closer collaboration between HR and IT.

It also offers an early glimpse of how the enterprise itself will be redesigned.

The Chief Workflow Officer is a Signal.

Another idea that we explored in our conversation was the rise of the Chief Workflow Officer.

The title is provocative on purpose. But the need behind it is serious.

If the greatest returns on AI come when companies reimagine workflows end to end, then someone has to own that work. Someone has to ask the uncomfortable questions before the org chart, systems architecture, or implementation roadmap gets locked in. Why do we do things this way? Which tasks belong to humans? Which belong to intelligent software? What outcome are we actually trying to create? Who decides? Who measures? Who redesigns?

You cannot reinvent a business by sprinkling AI across siloed functions. Someone has to see the workflow as a whole and architect it toward a better outcome. That is what this role points to.

Organizational Culture Will Decide Whether AI Becomes Incremental or Transformational

Transformation and innovation fail inside cultures that were never prepared to question themselves.

This was one of the deepest parts of the interview because culture is where change happens or stalls. Everyone says they want innovation. Few organizations create the conditions for it. A real culture of innovation is the set of behaviors, norms, and reinforcements that make it safe to ask hard questions, explore new ideas, challenge assumptions, and risk being wrong.

If people are punished for experimentation, if managers reward only predictability, if failure is stigmatized, then AI will be used only where it feels safe: around the edges, inside familiar models, in service of incremental change. This happens because culture lacks permission.

In the interview, I put it this way: leaders do not need to arrive with every answer. But they do need to create the safety nets, resources, and space for the organization to explore what good and great actually look like with AI. That is leadership in this moment. You don’t have to know or pretend to know the future. Create the conditions to discover it.

There is No Playbook for This

This may be the cleanest takeaway from the entire conversation.

Sure, the idea is that organizations can just add AI, increase output, and call that reinvention. But there is no universal playbook here. No three-step formula. No easy target state. There is only leadership, vision, workflow redesign, cross-functional alignment, cultural readiness, and a willingness to rethink what the business could become.

That is why I believe AI is a leadership test.

It tests whether executives can move beyond efficiency into imagination; whether business and technology leaders can work as partners rather than as separate camps; whether organizations can create room for reinvention before the market forces it upon them; and whether leaders are brave enough to admit that the old questions are no longer enough.

Watch the Conversation

Geoff asked exactly the kinds of questions leaders should be asking right now, and that is what made this discussion worth having. We went deeper than the usual AI talking points and into the harder issues that actually determine whether organizations move forward or fall behind: maturity, vision, workflow redesign, culture, governance, HR and IT collaboration, and what business reinvention really looks like in practice.

So if you’re leading transformation, advising the C-suite, building the future of work, or trying to understand what AI means beyond the hype cycle, watch the full interview.

The real threat of AI is what your competitors will become with it.

The most important question isn’t whether AI will change your business. It’s whether leadership will change fast enough to matter.

Listen on Apple or Spotify

Watch on Youtube.

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The post AI Business Reinvention Starts Where Legacy Thinking Ends appeared first on Brian Solis.

If AI is eating the world, you could also say that it’s also exposing leadership. The companies that win next will not be the ones that automate the fastest, but the ones that learn to imagine bigger. That’s how we’ll start this story. Also, my keynote is below if you’d like to jump straight to the video.

At Integrated Systems Europe in Barcelona, I talked about AI in a way that made some people nod, some people uncomfortable, and others sit a little straighter in their seats.

AI is often compared to the discovery of fire, the invention of the wheel, or electricity. What we’re really talking about here is comparisons to enabling forces. Fire did not change the world because it existed. The wheel did not reshape civilization because someone carved one. Electricity did not transform industry because it was discovered. Each became revolutionary when people learned to apply them in imaginative, practical, and often world-changing ways.

That is exactly where we are with AI. Or it is representative of where we could be.

Yet, too many organizations are using a civilization-shifting capability to write emails faster, summarize meetings, automate yesterday’s workflows, and take out costs while improving efficiency and productivity.

It may seem like strategy. At the same time, it is also a missed opportunity to exercise human imagination, to drive innovation, and to compete in a way not possible before.

So what’s the use case for that?

If there was a playbook, I suppose everyone would compete similarly.

The opportunity for you, for us, is bigger than efficiency. Bigger than productivity. Bigger than cost takeout. Beyond faster, cheaper, more scalable, AI is a new medium for value creation, reinvention, and human amplification. It can absolutely help us move faster. But speed alone is not transformation. Speed without vision only gets you to a familiar destination sooner.

And that is the trap we’re not acknowledging.

Right now, many executives are asking the wrong first question, “Where can AI save time?” or “How can AI reduce headcount?” or “What processes can we automate?” or “How can AI help us save time and money?” Those questions are understandable, especially in a market obsessed with near-term, quarter-to-quarter returns. But they also reveal how small the aperture still is in most leadership teams.

The more important question is this:

Now that AI exists, what becomes possible that was not possible before?

That is the question that separates optimization from reinvention.

It is also the question that separates leaders who will shape the future from those who will spend the next three years reacting to it.

Because AI is not only changing technology. It is changing the standard for leadership.

This is why the moment feels so consequential. AI is not simply testing infrastructure, governance, data readiness, or AI fluency across the workforce. It is testing executive imagination. It is exposing whether leaders can think beyond efficiency and into possibility. It is revealing who can redesign work, reimagine value, and challenge the assumptions that made sense in a pre-AI world, but now quietly limit what their organizations can become.

In that sense, AI is not just a business shift. It is a leadership mirror.

And not everyone is going to like what it reflects.

I know my reflection made me CTRL-ALT-DEL.

For years, digital transformation taught organizations to digitize and optimize what already existed. Most companies became better at moving old work into new systems. AI demands something far more profound. It asks us to question whether the work itself should exist in its current form. It asks whether decisions can be made differently. Whether expertise can be distributed differently. Whether customer experiences can be orchestrated differently. Whether products, services, operating models, and even business models can be designed in ways that were previously impossible.

That is a very different conversation.

It is also why so many AI initiatives feel underwhelming. They are being measured against the wrong ambition. If you use AI to improve the past, you get a better version of the past. If you use AI to rethink the future, you start to create advantage that compounds.

That is the shift leaders need to make right now.

From automation to augmentation.

From productivity to possibility.

From adoption to reinvention.

From asking what AI can do, to deciding what we should do differently because AI exists.

That last point matters more than most people realize. Don’t sacrifice your future to prolong the good old days.

The highest performers I know are not using AI to avoid thinking. They are using it to think better. They are using it to pressure test decisions, stretch scenarios, challenge assumptions, explore edge cases, sharpen strategy, and move from first answer to better answer.

They are not treating AI like a shortcut. They are treating it like an intellectual sparring partner for higher performance.

AI should not become a substitute for judgment. It should become a catalyst for better judgment.

AI should not flatten originality. It should provoke it.

AI should not turn leaders into faster administrators of legacy work. It should help them become architects of what comes next.

This is where the C-suite has to rise above the noise.

In boardrooms, AI is often framed as a technology agenda. In reality, it is a strategic, operational, and cultural agenda all at once. It changes how value is created. It changes how decisions are made. It changes what talent must now be capable of. It changes how leaders lead. It changes how organizations learn.

And perhaps most importantly, it changes how companies compete.

In an AI-shaped market, the winners will not simply be the businesses that deploy more tools. They will be the businesses that redesign themselves around new capabilities. They will understand that AI is not a layer to add on top of yesterday’s model. It is a force that invites you to rethink the model itself.

That is a very different level of ambition.

And it requires a very different caliber of leadership.

So what should leaders do right now?

Start here.

First, stop treating AI like an efficiency initiative. Efficiency is a benefit. It is not a vision. Every executive team needs to define where AI can create net-new value, not just lower existing cost.

Second, audit your assumptions. Where are you preserving workflows, decision models, and customer experiences simply because they are familiar? Legacy thinking is one of the biggest hidden costs in transformation.

Third, elevate the questions in the room. Do not just ask where AI can save time. Ask where it can unlock new growth, new services, new business models, new categories of customer value, and new forms of human contribution.

Fourth, build a culture that learns with AI rather than merely adopts it. Fluency matters, but fluency alone is not enough. Your people need permission to experiment, challenge norms, and rethink how work gets done.

And fifth, as a leader, go first. Do not delegate the future. Use AI yourself, not just to become more productive, but to become more expansive. Let it sharpen your thinking. Let it expose your blind spots. Let it widen your field of view. The future will not be led by executives who approve AI strategies from a distance. It will be led by those who allow AI to change how they see, think, and lead.

That is the real work.

What I wanted the audience at ISE to leave with was not just urgency, but permission. Permission to dream bigger. Permission to ask better questions. Permission to challenge inherited assumptions about work, leadership, and value. Permission to stop treating AI like a faster horse and start treating it like a chance to redesign the road.

Contrary to all the headlines, we are still early.

That is the good news.

The leaders who move now still have time to shape what this becomes inside their organizations. They still have time to set a bigger ambition than cost savings. They still have time to move from experimentation to reinvention. They still have time to build companies that do not just survive the next era, but define it.

But that window will not stay open forever.

AI is not waiting for leadership to catch up.

It is already exposing who is building for the future and who is simply trying to preserve the past a little longer.

And in the end, that may be the most important truth of all:

AI will not replace leaders.

But leaders who cannot imagine beyond yesterday will absolutely be replaced.

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At Integrated Systems Europe, I had the opportunity to continue the conversation after my keynote, “AI is Eating the World,” in a candid video interview with Rise TV about where artificial intelligence is really taking us, and where we’re still thinking far too small.

AI is often compared to the discovery of fire, the invention of the wheel, or even electricity. Those comparisons are dramatic, but they’re not entirely wrong. These weren’t just inventions. They were enabling forces. They expanded what humanity could do, but only because people learned how to apply them in imaginative and transformative ways.

That is exactly where we are with AI today.

We are not simply looking at another productivity or efficiency tool. We are standing at the edge of a technology that can fundamentally reshape how we work, create, decide, and innovate. But realizing that future depends on us. It depends on our willingness to ask better questions, to challenge old assumptions, and to imagine entirely new possibilities.

Too often, AI is still being used in narrow ways, generating amusing images, summarizing meetings, drafting emails, or serving as a slightly better assistant. That has value, of course. It helps us do what we did yesterday a little better today. But that is only the beginning.
The real opportunity lies beyond iteration, toward innovation, and new horizons we can’t see yet.

The future of AI is not just about cost-cutting, efficiency, or productivity over yesterday’s work. It is about exploring the unknown to unlock new value. Iteration improves existing work, making what we could do yesterday better today. Innovation helps us do what we did not know we could do before. That is the difference that matters. AI becomes most powerful when we use it not just to repeat the familiar, but to explore the unknown.

That requires a shift in mindshift.

Most people still prompt AI the way they search Google: looking for a specific answer, trying to get to an expected outcome faster. Or maybe, it’s a bit more sophisticated, where people are extend their work to increase output. But the real magic happens when you begin using AI to stretch your thinking. Ask it to challenge your assumptions. Ask it to model possibilities you have not considered. Ask it to collaborate with you in the spaces where certainty ends and creativity begins.

That is where new value is created.

In the interview with Rise TV, I also talk about something I believe deeply: we are not dreaming big enough. AI should not be reduced to a machine that simply makes us faster. It can also make us more thoughtful, more imaginative, and more capable of seeing what we previously missed. In that sense, I increasingly view AI as an empathy engine, something that can help us reconnect with curiosity, creativity, and perspective in ways that many organizations have lost over time.

There is also an important distinction I like to make when people talk about competing with AI.

You can fear AI as competition, or you can learn to compete with AI.

That small shift changes everything.

AI can be an augmentation tool that helps us become more competitive, more effective, and more inventive. But to get there, leaders need humility. We have to recognize that our past success, experience, and expertise can also limit us. They create comfort zones, biases, and default patterns of thinking. And those patterns can prevent us from seeing the opportunities hiding in the “unknown unknowns.”

That is why this moment matters so much.

Organizations that only use AI to cut costs or optimize routine work will realize some gains. But those who use AI to unlock new value, create new experiences, and discover new avenues for growth will be the ones who truly lead. And at the speed AI is evolving, that growth can become exponential.

My keynote at ISE focused on this broader transformation. The interview that followed goes deeper into the mindset shifts leaders need to make right now—not someday, but now.

If you are exploring how AI can move beyond automation and become a catalyst for innovation, creativity, and growth, I think you’ll find this conversation valuable.

Watch the full video interview following my ISE keynote, “AI is Eating the World,” and let’s continue reimagining what comes next.

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Everyone is talking about AI as if the future of work is just a headcount story. It is not. That framing is too small, too reactive, and too dangerous.

The real shift is not that AI will simply replace jobs. It is that AI is forcing companies to rethink what work is, how value gets created, and where human contribution becomes even more important. That changes the mandate for CHROs completely.

CHROs cannot stay in a reactive HR role. They have to become architects of reinvention by redesigning work, roles, and human plus AI collaboration before companies reduce the conversation to headcount and efficiency.

My ServiceNow new report, “Work Reimagined: The Human+AI Blueprint for Exponential Performance,” provides a blueprint to redesign work, unlock human + AI performance, and build a more resilient future of work.

The Wrong Story Is Driving the Future of Work

The future of work has been framed as an automation vs. headcount story. AI arrives. Jobs disappear. HR is left to deal with the consequences. The headlines have certainly pushed that narrative: Axios warned of a possible white-collar bloodbath, The Wall Street Journal reported that CEOs said the quiet part out loud about AI and job loss, and Reuters detailed how companies such as Amazon have paired restructuring with a stronger push for AI-driven efficiency. Just recently, Block accounted a 40% reduction in workforce, and Meta is reportedly evaluating a cut of 20%. Those stories are real, and they are shaping boardroom conversations everywhere, even if it isn’t truethat AI can do jobs today.

The Signals Tell a Different Story

Recent signals are more complex, and perhaps, telling, as to what the more immediate future looks like. A new KPMG U.S. CEO pulse survey found that only 9% of CEOs expect workforce reductions over the next year directly because of AI, while 55% expect AI to increase hiring. At the same time, 67% admit they have not yet redefined roles or career pathsfor an AI-enabled future.

Anthropic’s new labor-market tracker adds another layer to the conversation: it finds limited evidence so far that AI-exposed workers have become unemployed at meaningfully higher rates, even as hiring for younger workers in exposed occupations appears to be slowing. And CIO reportsthat 21% of companies have already stopped hiring entry-level employees because of AI, with half expecting to stop by 2027. In other words, we are not looking at a simple story of job replacement. We are watching a redesign of work happen in uneven, confusing, and often unprepared ways. And rarely do I see in these conversations clear delineation or understanding of the differences between tasks and jobs and how they may be broken down, reassembled, and augmented with human to agent ratios.

So while companies are automating people out of jobs “with AI,” market leaders will explore how to reimagine work to 10x performance and output with humans + AI.

This Is the CHRO Mandate Now

This is exactly why CHROs and HR leaders cannot be cast as downstream managers of disruption. They have to become architects of reinvention. Because the central question is no longer, “How do we protect the workforce from AI?” It is, “How do we redesign work so people become more valuable because of AI?” That is a fundamentally different mandate. It is strategic, cultural, operational, and deeply human. And it changes the dynamics of competitiveness. On one side, you have companies automating themselves toward stagnation. On the other, you have augmented roles that can innovate beyond the day-to-day work.

The Real Risk Is Missed Opportunity

That is also the heart of my new research report, Work Reimagined: The Human + AI Blueprint for Exponential Performance. The report argues that AI’s greatest risk is not job replacement. It is missed opportunity. Organizations that limit AI to automation give up the larger gains that come from human and AI collaboration. As the report puts it, the path forward is not automation alone, but automation to augmentation to exponential performance, or A -> A -> X². The goal is not to only do yesterday’s work cheaper and faster. It is to unlock work, outcomes, and value creation that were previously out of reach or unseen.

The Automation Trap Is Already Here

That argumentation is already landing in the market. In his recent Forbes column that covered my new report, Joe McKendrick spotlighted the core mistake companies are making with AI agents: they are using next-generation intelligence to optimize legacy workflows instead of reimagining work itself. That is the automation trap and it can accelerate AI Darwinism. If AI is introduced only as a labor-reduction strategy, then HR inherits fear, confusion, capability gaps, and disengagement. But if AI is introduced as a capacity multiplier, then HR can help convert anxiety into mobility, growth, and new forms of contribution.

Why Human + AI Performance Is the Real Opportunity

The opportunity is not theoretical. Microsoft’s 2025 Work Trend Index found that 45% of leaders say expanding team capacity with digital labor is a top priority in the next 12 to 18 months, while 78% are considering hiring for AI-specific roles. PwC’s 2025 Global AI Jobs Barometer found that industries more exposed to AI are seeing 3x higher growth in revenue per employee, with wages rising 2x faster in those sectors. And the World Economic Forum projects that by 2030, 170 million new jobs will be created while 92 million will be displaced, for a net gain of 78 million roles. The message is clear: the future is not jobless. But it will not reward organizations that cling to static job descriptions, brittle org charts, and outdated talent models.

A Blueprint for Exponential Performance

This is where a new framework comes in handy. Work Reimagined lays out seven phases: define intent and build the business case; map work and identify value; design human-plus-agent roles; build AI fluency; run A -> A -> X² lighthouse pilots; scale governance; and maintain operational intelligence through an AI control tower. It is practical by design. It asks leaders to distinguish high-repetition work from high-human-value work, create future job descriptions that pair people with agents, and measure success not only by time saved, but by quality improvement and newly created capacity redeployed to higher-value work. It also calls for an AI Resources Office co-led by HR and IT so agent role definition, onboarding, performance management, compliance, and ethics are built into the operating model, not bolted on after the fact.

The Leadership Brief for CHROs

For CHROs, this is your leadership brief. Build AI fluency before AI dependency. Redesign roles before redesigning headcount. Decide where saved time goes before productivity gains disappear into more noise. Gartner foundthat just 7% of organizations provide guidelines for how employees should use time saved by AI, even though 55% of HR leaders want that time redirected toward growth-driving work.

If HR does not define where new capacity goes, the business will default to using AI to accelerate output without increasing meaning, mobility, or resilience.

The Future of Work Has to Be Designed

This is not a moment to manage disruption from the sidelines. It is a moment to architect reinvention. Because if HR does not redesign work, roles, and growth paths for a human plus AI future, someone else will. And they may do it with a spreadsheet instead of a vision.

This is the moment to move HR from support function to transformation engine. Not to preserve yesterday’s jobs exactly as they are, but to help people stay relevant, grow into higher-value roles, and work in partnership with AI to increase performance and possibility.

The future of work will not be decided by the companies that automate the fastest. It will be shaped by the leaders bold enough to redesign work, invest in people, and create a model where human potential scales with intelligent systems instead of shrinking in their shadow. That future has to be reimagined, and designed.

Please download the report (no email gate!)

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The post Most Companies Are Using AI to Cut Costs, Smart CHROs Will Use It to Rebuild Work appeared first on Brian Solis.

This is a thoughtful piece by Danny Devriendt. Thank you, my friend.

Brian Solis is far from a neutral observer in my SXSW story; he’s a friend I’ve known for a long time, and one of the people who helped me -and a whole generation- see the social web for what it really was back in the day. Long before social media became a relentless ad machine, he wrote The Social Media Manifesto, arguing that this wasn’t a new channel but a rewiring of influence, participation and public dialogue itself. His line back then -“social media is about sociology and anthropology, not technology”- aged uncomfortably well.​

This year, after seven years away from SXSW, Brian walks back into Austin with exactly that same sharp surgical anthropologist’s eye, now pointed straight at generative AI, augmented intelligence and what this all does to our brains, our families, our organizations and our leadership models. His session, “Augmented Intelligence and Leadership in the AI Era,” was for me, the best explanation of the tenor of this first festival day: less “wow, look at this model,” more “what are we doing to ourselves?”

AI slop, cognitive Darwinism and the hidden AI tax

Brian opens with something delightfully impolite for a room full of AI‑curious professionals: “AI slop.” AI slop is his label for the flood of generic, low‑quality, copy‑pasted AI content choking our feeds, inboxes and internal docs; especially on LinkedIn, where even the comments now smell like a robot crazy prompt bonanza. We are, he argues, paying an “AI tax” for this: the invisible hours we lose rewriting, correcting, or summarizing machine‑written sludge just to recover a usable signal (that still sucks).

But the more interesting part is what this does to our heads. Drawing on new brain‑scan and behavioral research, Brian strings together a vocabulary for what is happening: digital amnesia, cognitive offloading, cognitive debt, AI atrophy, AI brain fry. The more we hand off thinking to AI, the more our own cognitive muscles weaken; the more we accept flattering, anthropomorphic feedback from systems, the more we risk confusing statistical pattern‑matching with wisdom or validation. He calls the whole bundle “cognitive Darwinism”: a slow, mostly invisible selection pressure that favors those who outsource their thinking over those who still practice it, until, at some point, the mismatch becomes a problem.

His punchline is nasty and necessary: used badly, generative AI probably deserves cigarette‑style warnings, not just a cheerful onboarding wizard. We are exporting parts of our memory, originality and voice to a machine, and then pretending that the loss is an acceptable side‑effect of getting our slides faster. That’s exactly the kind of convergence SXSW has been pointing at all day: not AI versus humans, but AI acting on humans.

False AI leadership, real divides

Brian then pushes the critique slambang into the boardroom. We are not just drowning in AI content; we are also drowning in “AI journalism” and false leadership: headlines about companies “replacing 40% of their workforce with AI,” markets cheering, and very little serious evidence that any of this is thoughtful redesign rather than opportunistic cost‑cutting with a buzzword attached. When every LinkedIn profile and medium‑sized keynote now speaks with the same AI‑polished voice, “expertise” becomes a vibe rather than a practice, and organizational trust quietly but quickly erodes.

Here he introduces one of the more useful diagrams of the day: the dot map from recent adoption studies -grey dots for non‑users, green for casual free users, yellow for serious paid users, red for builders and coders- each dot representing millions of workers. The scary part is not that the grey dots exist; it’s that yellow‑dot users, the ones who go deep and creative with these tools, are already outperforming green‑dot peers by a factor of seven, while most organizations still talk about “AI fluency” as if this were a uniform, binary skill. That performance gap is not theoretical; it is a structural divide inside your company and your labor market right now, and leaders who ignore it are sleepwalking.​

His answer is to broaden what we mean by being “good with AI.” He stacks the usual suspects -IQ, EQ (emotional intelligence), SQ (social agility), and the almost nonexistent skill of genuine self‑awareness- and then adds AIQ: artificial intelligence quotient. But for Brian, AIQ on its own (knowing how to prompt, how to automate tasks) is not enough; it has to be fused into what he calls augmented intelligence: redesigning work so that humans still do uniquely human things -imagine, empathize, ask better questions- while AI extends that reach instead of replacing it. That’s a very different story from the slideware version of “augment your workforce with copilots”.​

From mindset to mind shift: practicing augmentation

Brian Solis doesn’t ask for a “mindset shift,” he asks for a mind shift : less inspirational poster, more firmware update. The question is no longer “How do I use AI to do what I already do, but faster?”; the question is “What can I now attempt that was literally impossible for me without these tools?”​, and I think he is spot on.

To get there, he reaches back to Sir Ken Robinson’s classic argument that we don’t grow into creativity; we are educated out of it, rewarded for following rules and punished for being wrong. Most organizations now proudly measure “AI proficiency” and “AI fluency” -how well people can follow the new rules- without noticing that they have simply built an automated status quo. If your first instinct is to use AI to automate the past, Brian warns, you have locked yourself into a very finite future. You will be very efficient at being exactly what you already are. His alternative is a two‑horizon model he uses with clients. On one horizon, you do the obvious thing: automate the work that truly should be automated, because it is repetitive and stable, and harvest the efficiency gains. On the second horizon, you deliberately use AI for “innovative AI”- exploring problems, prompts and ideas that you couldn’t touch before, accepting that some of the output will be ugly, and treating that ugliness as the price of originality. The gap between those two trajectories -the efficient line and the augmented line- is what he calls positive disruption: disruption of your own habits, metrics and mental models.​

WWAID (What Would AI Do) and WDYSF (what do you stand for?)

Two tiny pieces from the session I want to highlight: The first is WWAID: “What Would AI Do?” Before you prompt, before you design a process, before you walk into a strategic decision, you pause and ask yourself: if intelligence were native to this moment -if an agent had perfect recall, perfect pattern‑matching, infinite patience- what would it do by default? Then you use that imagined baseline as a foil. Instead of prompting for the obvious output (“summarize this market report”), you ask AI to adopt roles that pressure‑test your assumptions: be the activist investor, the future regulator, the angry customer, the visionary competitor. Most people interact with AI as if it were Google with better grammar; WWAID is Brian’s hack to push you past that into prompts -and outcomes- you would never have discovered from inside your usual worldview.

The second is a question he treats almost like a personal operating system: “What do you stand for?” Asked from the audience how he protects his own voice in a world of bandwidth pressure and AI assistance, he offers a practice. Regularly, he sits down and writes out what he stands for, why he started this work in the first place, and what impact he actually wants beyond faster deliverables. In a festival that keeps returning to “mattering” as a fundamental human need -the need to feel valued and to add value- that question is not a self‑help bumper sticker; it is a survival skill. If you don’t know what you stand for, the platforms will be very happy to sell you a prefab identity optimized for engagement (in pink, with glitters).

A tuning fork talk

Put Amy Webb’s funeral for trend reports and Brian Solis’ autopsy of AI slop next to each other, and you get a pretty accurate map of SXSW Innovation 2026 so far. On one side, a futurist telling us to stop fetishizing isolated trends and start tracking convergences like human augmentation, unlimited labor and emotional outsourcing at system scale. On the other, a digital anthropologist friend coming back to SXSW showing how those convergences are already playing out inside our own cognition, feeds, organizations and leadership habits.

That’s why his session felt, to me, like the tuning fork of this first half. It explained why so many other sessions kept circling the same unease: AI not just as a productivity layer, but as an invisible force acting on trust, creativity, mattering, and the stories we tell ourselves about being useful in a world of agents and automated factories. Brian doesn’t argue for less AI. He argues for less laziness: less AI slop, less unexamined automation of the past, and far more deliberate augmented intelligence built on empathy, curiosity, creativity, and a brutally honest answer to that one simple question:
what do you stand for?

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Imagem: Reprodução

Via Economia, Renato Pezzotti

Os assuntos sobre os avanços da inteligência artificial foram tratados de forma transversal no SXSW deste ano, que aconteceu nesta semana, nos Estados Unidos.

O antropólogo digital Brian Solis reforçou que líderes precisam deixar de usar a IA apenas para “automatizar o passado” e pensar em como as novas ferramentas devem ser utilizadas para redesenhar o trabalho que só humanos podem fazer: ter sua inteligência “aumentada”, construída com empatia, curiosidade e criatividade.

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via Dan Costa, Worth

My social feed is filled with ads telling me “Most people still use AI like Google….” and then they try to sell me some online course or collection of prompts. But there is truth to the cliche. Here at SXSW, the distinction between the AI-augmented and the AI-clueless keeps coming up.

“Most people use AI to do what they already know,” Brian Solis, digital anthropologist and head of global innovation at ServiceNow, said on stage at SXSW this week in Austin, Texas. “The opportunity is to use it to do what they couldn’t do before.”

Most people, he argued, approach generative AI as a way to get work off their plate—summaries instead of reading, drafts instead of writing, answers instead of thinking. The instinct is understandable. The tools are designed to remove friction. But that instinct also defines the ceiling of what AI can deliver.

Solis describes two very different futures. In one, AI becomes a mechanism for outsourcing cognition, compressing knowledge work into faster, cheaper versions of what already exists. In the other, it becomes an extension of human intelligence—a way to explore ideas, test assumptions, and surface possibilities that would otherwise remain invisible.

At nearly every technology conference this year, the conversation around artificial intelligence follows a familiar pattern. Executives talk about efficiency. Investors talk about productivity. Panels debate how many jobs might disappear.

Solis’s presentation offered a more useful frame. The emerging divide may not be between humans and machines. It may be between workers who use AI to extend their thinking and those who use it to outsource their thinking.

Experiments across software development, writing, and research consistently demonstrate that generative AI improves task-level productivity. In controlled trials conducted by Microsoft researchers, developers using GitHub Copilot completed programming tasks roughly 55% faster than those working without it. The findings come from Microsoft Research’s analysis of Copilot usage among developers.

Other experiments show gains ranging from roughly 14 to 40%, depending on the task complexity and environment.

The economic effects of these improvements remain surprisingly modest. Economists estimate that generative AI may increase overall productivity growth by only 0.5 to 0.7 percentage points annually over the next decade.

The paradox is striking. AI appears extremely powerful at the level of individual tasks while producing limited macroeconomic change so far. One explanation is that most organizations are deploying AI to accelerate existing workflows rather than redesign them.

Solis calls this the automation trap. When companies introduce AI tools, the first instinct is usually to ask how they can reduce labor costs or streamline existing processes. Those uses generate measurable improvements quickly, which makes them attractive to executives. Yet they rarely change the structure of the work itself.

“If your first instinct is to use AI to automate the past,” Solis told the audience, “you lock yourself into a very finite future.”

Instead, Solis advocates for augmentation. Instead of asking AI to execute familiar tasks more efficiently, workers use it to explore new possibilities: generating alternative strategies, modeling complex scenarios, or testing ideas that would be difficult to examine on their own.

The difference between automation and augmentation may determine who benefits most from the technology. Early evidence suggests that workers who integrate AI deeply into their workflows can gain disproportionate advantages. Some studies describe productivity differences of several multiples between advanced users and those who employ AI casually.

At the same time, AI usage is creating new forms of friction. A randomized study examining experienced open-source developers found that programmers using AI assistance sometimes took 19% longer to complete tasks than those working independently, largely because AI-generated output required additional review and debugging.

In other words, AI often shifts work rather than eliminating it. The same pattern is emerging in writing and communication.

Generative tools have made it trivial to produce competent text at scale. The result has been a flood of formulaic content across professional platforms—the unavoidable AI slop. LinkedIn feeds filled with emoji-heavy posts, templated marketing copy, and identical thought-leadership articles have come to define the professional internet.

When everyone can generate persuasive language instantly, expertise becomes harder to identify. “Everybody is an expert now,” Solis observed. “And when everybody is an expert, no one can be.”

Researchers are beginning to examine another consequence of heavy AI use: the way it changes people’s thinking.

Studies measuring brain activity during writing tasks have found that participants relying heavily on generative tools show lower cognitive engagement and weaker memory retention than people writing without AI assistance.

Other research links frequent AI use to cognitive offloading, a process in which people delegate reasoning tasks to machines rather than performing them themselves.

The effect resembles earlier technological shifts. When GPS navigation became ubiquitous, psychologists documented “digital amnesia,” the loss of spatial memory that occurs when people no longer need to remember directions. Generative AI may be introducing a similar dynamic in intellectual work.

The danger is not that AI makes people less intelligent. The danger is that organizations design workflows that discourage employees from exercising the capabilities that make human intelligence distinctive.

Solis says this makes AI adoption primarily a leadership challenge. Companies increasingly measure “AI proficiency,” evaluating how effectively employees can use generative tools. Those metrics capture a useful skill set, yet they risk reinforcing incremental thinking. Workers become better at using AI to perform existing tasks while the underlying processes remain unchanged.

remedy this, Solis suggests a simple thought experiment he calls WWAID—“What Would AI Do?” Before solving a problem, leaders ask how an intelligence native to the situation might approach it. The exercise pushes teams to move beyond existing workflows and explore new strategies rather than simply accelerating old ones.

The distinction will likely determine which organizations benefit most from the technology.

Despite intense media attention, generative AI still touches a relatively small portion of the economy. Data from the Federal Reserve Bank of St. Louis suggests that about 5.7% of total U.S. work hours currently involve generative AI tools.

Adoption is rising quickly, but the technology remains far from universal. That means the productivity divide Solis describes is only beginning to emerge.

Three distinct profiles are beginning to emerge in the AI economy. First, there are the AI-dependent, who default to the tool for answers, move quickly, but produce increasingly uniform output. Then, there are the never-AI-ers, who preserve independent thinking but risk falling behind as the pace of work accelerates.

The real winners, according to Solis, are the AI-augmented, who use the technology as a partner in thinking—testing assumptions, generating alternatives, and pushing beyond their own cognitive limits—combining speed with originality in a way that is likely to define the durable advantage in an AI-driven world.

“The work you want to do,” Solis said, “is the work you couldn’t do without AI—and that AI can’t do without you.”

Please read the full article at Worth.

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My friends at Wiley Publishing called me today to let me know that Mindshift is officially a “#1 best-seller” in the Business Management category!

Every company needs leaders who can spot and seize on opportunities at a moment’s notice. Every organization needs leaders who can rally teams together around new opportunities. Those who can see important, emerging trends foresee the coming disruption and harness those forces, translate them into actionable insights and motivation to fuel their company’s march into the future rather than ignoring or running or hiding from opportunities.

In Mindshift: Transform Leadership, Drive Innovation, and Reshape the Future, technologist, strategist, keynote speaker, and award-winning author Brian Solis, Head of Global Innovation at ServiceNow, draws on his experience of leading initiatives that drive innovation and business transformation to deliver the empowering message that this is the time to change the world for the better. And that change starts with you.

In this book, you’ll discover why legacy leadership continues to miss the mark and fail to adequately account for change and innovation, causing people to miss the winds of opportunity or threats of disruption until it’s too late. Let this inspire, not frustrate you.

Within these pages, you’ll gain access to the tools, insights, and lessons you need to become an unstoppable leader, regardless of your roles. You’ll learn how to:

• Adapt for a post-industrial, AI-first world 
• Find direction in uncertainty 
• Spot and prioritize emerging trends 
• Develop, spark, and embrace innovative ideas that create new value 
• Learn to thrive in this new and shifting future Mindshift explains how you can make the mental shift to see past industrial-era business-as-usual mindsets, to become the visionary and voice for a future that doesn’t yet exist. Embracing a mindshift opens your potential to new possibilities, breaking the shackles of the status quo, and unlocking alternative, more meaningful destinies.

Mindshift is perfect for anyone who knows a better future is possible, and who wants to make an impact, to reshape the modern business landscape, and develop the skills they need to thrive in a perpetual state of uncertainty. Mindshift is a can’t-miss resource for managers, executives, entrepreneurs, and anyone who cares about the future, their destiny, and the role they want to play in shaping tomorrow.

Please read the book. Please share the book with your teams and leaders. And please reach out if I can help by presenting or keynoting at your upcoming event, offsite, or leadership meeting.

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I lost a fight with the sidewalk the night before my talk at SXSW.

Not exactly the kind of opening line you script for your first SXSW keynote in seven years, but there I was in Austin, cut-up but grateful and energized to be back on that stage talking about something that matters more than most leaders realize right now.

The talk was called “Augmented IQ: Scaling Human + AI Potential.” It built on a simple but urgent thesis: the future of AI is not about automated intelligence alone. It is about augmented intelligence. It is about whether we use AI to do yesterday’s work faster and cheaper, or whether we use it to do what we could not do alone. That distinction shaped the entire session, from AIQ (Artificial Intelligence Quotient) versus Augmented IQ to #WWAID and the homework I shared with the audience.

What made the conversation feel urgent in Austin is that this is no longer just a conversation about tools. It is a conversation about competition. You are not simply adopting AI. You are competing in a world where other people, other teams, and other companies are learning how to think, decide, and create with it faster than you are. That changes the stakes. This is not about whether AI exists. It is about whether we evolve because it exists.

SXSW was a conversation about competition. We are no longer simply adopting AI. We are competing in a world where some people, some teams, and some companies are already learning how to think, create, and decide with it at a different level.

You Don’t Just Use AI. You Compete With It

In all honesty, too much of the market is still trapped in the wrong frame.

Most organizations are treating AI as a productivity layer.

Right now, most companies are applying AI like a productivity patch on top of legacy thinking. Draft the memo. Summarize the call. Rewrite the email. Deflect the ticket. Cut costs. Cut people. Accelerate the workflow. That may improve efficiency, but it’s not transformation. It’s iteration. And when leaders confuse iteration for innovation, they trap the business in finite thinking. They use AI to optimize yesterday rather than create what’s next. At the exact same time, a smaller group is using AI to imagine new products, new services, new experiences, and new models for growth. That’s why this moment isn’t just about technology. It’s about leadership, imagination, and organizational design.

The real story of AI is not purely technological transformation, but human and organizational transformation, and the firms that win are the ones that design for human-AI collaboration rather than short-term labor cuts.

Automated Intelligence vs. Augmented Intelligence

That is why I drew a line in Austin between automated intelligence and augmented intelligence.

Automated intelligence helps you complete existing tasks faster. Augmented intelligence helps you see farther, think better, challenge assumptions, surface blind spots, and create outcomes that were previously impractical or impossible. One optimizes the present. The other expands the future.

The trouble is that many teams are chasing efficiency without accounting for the hidden costs.

Microsoft Research found that in knowledge work, higher confidence in generative AI is associated with less critical thinking, while higher self-confidence is associated with more critical thinking. In other words, the more blindly we trust the tool, the less mental work we do ourselves.

HBR just gave this pattern a fitting name: AI brain fry. Its March 2026 reporting on new research described mental fatigue, slower decision-making, and diminishing returns when people are forced to juggle and supervise too many AI tools at once.

What we are beginning to see is not just a productivity story. It is a cognitive one. The way we use AI is starting to shape the way we think, the way we remember, the way we judge, and the way we create. Sequence and intent matter. If we use AI carelessly, we do not simply automate tasks. We begin to outsource forms of thinking we still need. That is why I talked about cognitive dAIrwinism in Austin. Not to be provocative for the sake of it, but to describe what happens when convenience starts to outrun consciousness.

The goal is amplification, not self-inclicted anesthesia of judgment. And when we get that balance wrong, the failure modes are predictable. We offload too much. We trust too quickly. We confuse polished output for sound thinking. We lose friction, and with it, sometimes, originality.

cognitive dAIrwinism

What I was really exploring on stage was something bigger than productivity. It was what I called cognitive dAIrwinism: the idea that the way we use AI will either sharpen human capability or slowly erode it. Sequence matters. Intent matters. Whether AI amplifies judgment or anesthetizes it matters.

One of the challenges in talking about AI right now is that we’re still using the language of productivity to describe what is increasingly a human, cognitive, and cultural shift. We talk about speed, efficiency, and automation because those are easy to measure. But those metrics don’t capture the hidden costs that show up in the quality of our thinking, the originality of our work, or the trust people place in what they read, share, and act on. To understand what’s really happening, we need language that helps us see the downstream effects of careless or shallow AI adoption more clearly.

That’s where a few emerging terms become useful.

AI slop describes the growing volume of machine-generated output that may look polished and complete on the surface, but often falls apart under closer inspection, forcing humans to step in and make it accurate, credible, or usable.

AI tax is the hidden labor that comes with that, the extra work of reviewing, rewriting, correcting, validating, and restoring quality to synthetic output that was supposed to save time in the first place.

Workday reported in January that nearly 40% of AI time savings are being lost to rework, including correcting errors, rewriting content, and verifying outputs!

Digital amnesia describes the cognitive tradeoff that begins to emerge when recall, memory, and basic mental effort are increasingly outsourced to machines.

AI atrophy points to the deeper risk underneath it all: when judgment, creativity, critical thinking, and discernment are used less often, they don’t stay sharp. They weaken.

When AI Starts Shaping the User

Then there is AI sycophancy, which may be one of the most under-appreciated risks in leadership and learning today.

Researchers are increasingly documenting how language models can prioritize user approval over truth. A 2026 paper in AI and Ethics defines AI sycophancy as the tendency of large language models to prioritize approval over truth and warns that it can create moral and epistemic harms. A Stanford education study found that when students mentioned an incorrect answer, model accuracy could fall by as much as 15% effectively reinforcing misunderstanding instead of correcting it.

That New York Times piece is a chilling reminder that the risk with AI is not only hallucination. It’s validation. When a chatbot is designed to be agreeable, persistent, and emotionally responsive, it can reinforce distorted beliefs and pull vulnerable users into a self-reinforcing spiral that feels credible simply because it feels personal.

What makes this especially important for your argument is that it turns AI safety into more than a technical issue. It becomes a cognitive, emotional, and leadership issue. This is where AI sycophancy stops being a quirky product flaw and starts becoming a real human risk.

Think about that for a second.

If a system flatters your assumptions, confirms your instincts, and rewards your framing, it does not just answer you. It starts shaping you.

And once that happens, this stops being a product issue and becomes a trust issue.

Trust in content. Trust in expertise. Trust in what is original, what is verified, what is genuinely earned. But also trust in leadership. Because if leaders cannot distinguish between assisted output and augmented judgment, between speed and insight, between confidence and competence, they will scale the wrong behaviors at exactly the wrong time.

Tell me if you’ve heard this one before: “AI won’t take your job, but someone who uses AI will…” Though catchy, it is incomplete. The bigger question is whether leadership knows what kind of work, what kind of value, and what kind of future it is actually scaling. Without it, the real risk to job losses in the name of AI will come down to leaders who don’t understand AI  or the differences between tasks and jobs.

AI Darwinism

That is one reason I believe the popular line, “AI won’t take your job; someone using AI will,” is incomplete. The bigger risk is not just another employee with better prompts. The bigger risk is leadership that misunderstands the assignment. Leaders who use AI to commodify yesterday’s business will do more damage than good. Leaders who justify weak strategy, lazy layoffs, or hollow certainty in the name of AI may get a short-term bump. But over time, they create what I call AI Darwinism: using AI to double-down on yesterday’s models and work at the expense of innovation, agility, and competitiveness.

At the same time, a small percentage of users are pulling away from everyone else.

OpenAI’s January 2026 research on the “capability overhang” found that the typical power user relies on about seven times more advanced thinking capabilities than the typical user. That means the divide is no longer just about access. It is about depth of use. It is about whether people are using AI like a search box, or like a thought partner for complex, multi-step work.

The answer is not less AI. The answer is better human use of AI.

Mindshift belongs in this conversation.

A mindshift sees the world through a lens of possibility and pursues the unknown to reshape the future. That is the deeper invitation here. AI does not reduce the importance of human qualities. It raises the premium on them. Empathy, curiosity, and creativity are not soft skills in the age of AI. They are essential skills. They are the difference between using AI to repeat the past and using AI to imagine what comes next.

If we are not careful, AI can converge thinking. But if we lead intentionally, it can also expand it. That is why augmentation matters so much to me. It protects and extends the distinctly human capacities that make better futures possible in the first place.

That is why I introduced Augmented IQ at SXSW. AI fluency still matters. Everyone should know how to draft, summarize, search, automate, and use the tools effectively. But AI fluency is the starting line, not the finish line. Augmented IQ is the next step. It measures whether you use AI to challenge assumptions, expand options, improve judgment, and create new value. AIQ asks, “Write this for me.” Augmented IQ asks, “Show me what I’m missing, pressure-test my assumptions, and help me discover what to try next.”

#WWAID

If we want AI to augment our thinking instead of automate our habits, then we need a different starting point. Most prompting is still rooted in familiar logic: faster search, cleaner summaries, better drafts. Useful, yes. Transformative, not always. The real shift happens when you stop asking AI to work within the boundaries of your existing assumptions and instead ask how intelligence itself might reframe the problem.

That is the idea behind WWAID.

Most people still prompt from inside their current worldview. They ask AI to help them do what they already planned to do. WWAID changes the frame. It asks: if intelligence were native to this moment, this problem, this opportunity, what would AI do? That question forces you beyond the familiar. It opens the door to divergent thinking, better prompts, role-play, second-order consequences, and possibilities you would not have seen on your own.

This shift becomes easier to see in practice.

AIQ says, “Summarize the ten reports so I can read less.” Augmented IQ says, “Compare those reports, find the contradictions, surface second-order implications, and identify the questions no one is asking yet. Then help me understand where to place the bet.”

AIQ says, “Draft the memo faster.” Augmented IQ says, “Stress-test the argument, sharpen the audience insight, simulate objections, and help me find a more original point of view before I write.”

AIQ says, “Deflect support tickets.” Augmented IQ says, “Find the emerging failure patterns, reveal the broken journey, and help us redesign the experience so service becomes a source of innovation.”

And in leadership, the difference may matter most of all. One leader asks AI to summarize the market and build the slide. Another asks it to act like a customer, a competitor, a regulator, and an activist investor all at once and reveal what they are still not seeing. That is the difference between assistance and advantage.

This is also why leaders have to fund two paths at once.

Use AI for iteration. Absolutely. Remove as Bill McDermott describes as “soul crushing work.” Speed up admin. Compress research. Improve service. In healthcare, for example, AI is already helping physicians with research summarization, documentation, and clinical workflows. The AMA reported this month that more than 80% of physicians now use AI professionally, and more than three-quarters believe it improves their ability to care for patients. That is augmentation when it gives people back time for empathy, judgment, and better care.

But do not stop there.

Also use AI for innovation. Create the time, resources, and permission to explore what did not exist yesterday. Ask what product, service, journey, or revenue stream becomes possible now that human imagination and machine capability can work together. If iteration is doing what you did yesterday, innovation is doing what you didn’t do yesterday.

The real promise of generative AI is its duality.

One curve comes from iteration. It removes routine, compresses time, lowers costs, and improves efficiency. That matters. But it is linear. The second curve comes from augmentation and innovation. It helps people discover new value, invent new offerings, redesign experiences, and rethink business models. That is where the exponential upside begins.

Leadership’s job is not to choose one or the other. It is to pursue both. Take the gains from automation and reinvest them into human augmentation, experimentation, and innovation. Because over time, the organizations that only optimize will plateau, while the organizations that also reimagine will pull away.

That is the real choice in front of us.

We can train people to prompt better and produce more polished sameness. Or we can help them become more imaginative, more empathetic, more original, more judgment-driven, and more capable because AI exists.

AI Forward

The future will not belong to the people who simply ask AI for answers.

It will belong to the people who ask better questions because AI exists.

That is the mindshift. That is Augmented IQ. And that is the work.

This is why I closed with a simple but important idea: be AI-forward.

Prioritize the use of AI in exploring possibilities not achievable without it, and outcomes AI could not achieve without you. That is a very different operating model than using AI only to get through inboxes and meetings faster. It asks us to build a practice around curiosity.

Start with a daily exploratory prompt. Use “what if” and “how might we” questions instead of only asking for direct answers. Prompt to explore, not just solve. Ask for perspectives, not just facts. Use role-play. Ask for impossibilities. Keep a journal of the prompts that unlock something unexpected. Set aside time every week to look ten years out, not just ten minutes ahead.

Doing small things in a different way exposes you to new experiences, unlocks new value, creates new habits, and begins to change the future a little at a time. That is how new questions become new behaviors, and how new behaviors become new advantage.

I’ve posted the slides below to help you move into leadership meetings, team rituals, design sessions, product roadmaps, and personal practice. The homework is simple: use AI to explore what you could not achieve without it, and never forget what it still cannot do without you.

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Industry analyst Joe McKendrick broke-down Brian Solis’ latest ServiceNow research report, “Work Reimagined: The Human + AI Blueprint for Exponential Performance,” in his latest Forbes column.

Business leaders need to consider the possibilities of human-AI collaboration beyond simple task replacement, urges Brian Solis, global head of innovation for ServiceNow and a highly regarded digital analyst and anthropologist, in a compelling thought-leadership piece. He makes the point that he sees many companies attempting to simply slap on AI or associated agents on processes without considering the bigger picture – and opportunity.

“Left to their own devices, executives gravitate toward eliminating costs as AI’s primary use case,” Solis writes. “If cost-cutting and automation are the priorities for C-suites, boards, investors, and shareholders, then AI will deliver those very well – and not much else.”

To assure more productive human-AI collaboration, Solis walks through a seven-phase process:

• Phase 1: Define intent and create a business case. The problem is companies are simply attempting to repeat automation endeavors that may have worked in the past. Instead, business leaders need to “rethink business-as-usual approaches that focus on efficiency gains, cost reduction, and machine-driven job displacement,” Solis says. “Instead, shift your mindset to value creation powered by the unprecedented capacity of AI to augment human capability.”
• Phase 2: Clarify which activities require human attention and which can be delegated to machines. Ask: “‘How frequently does the activity occur and how much capacity does it consume in hours, handoffs, bottlenecks?’’ Solis states. ”’How much unique human value, creativity, judgment, and trust does it generate for the business?’”
• Phase 3: Designate human and agent roles. Build job descriptions pairing people and agents. “Estimate the ‘human-to-AI ratio’ for each role,” Solis advises. Ask: “’How many agents are needed for which roles and tasks?’ and ‘How many humans are needed to guide them?’ ‘Which KPIs will show that both the human and the agent are succeeding?’”
• Phase 4: Build AI fluency. Encourage programs, sessions and coaching to help employees understand AI better.
• Phase 5: Design strategic pilots. Test the arrangement to see if it actually delivers positive results. “Partner an agent with a person, outline metrics, and run a 30-day A/B comparison against the old process,” says Solis. Metrics that should be tracked include time saved, quality enhancement and new capacity redeployed to higher-value work.
• Phase 6: Scale and govern. Here, agents should be managed by an AI resources office, consisting of IT and HR.
• Phase 7: Once AI agents are deployed, shift focus to performance.“Agents should be managed, not installed and forgotten,” says Solis. “Monitor, measure, and improve agent performance over time through a recurring orchestration and management system that assesses what’s working, what’s not, and where to optimize or retire agents.”

The bottom line is that humans can only do so much, and AI can only do so much, But together, they can deliver, as Solis puts it, “exponential outcomes that neither humans nor AI can achieve alone.”

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Based on interviews in May 2025 and subsequent, more detailed,, and repeated warnings in 2026, Anthropic CEO Dario Amodei has predicted that artificial intelligence could wipe out roughly 50% of all entry-level white-collar jobs within the next one to five years.

Amodei is sounding the alarm to get our attention.

If he’s right, leaders don’t get the luxury of debating whether disruption is coming, they’ll have to manage how it lands. Amodei’s advice is to track track where AI is automating work in near real time, redesign roles and workflows so AI creates capacity for new value instead of just headcount reduction, and build redeployment + reskilling paths so people move into the next work, not out of the company. His message is to treat AI like an operating-model change, and act now while you still have time to shape outcomes.

Disruption is inevitable. Leadership, vision, and strategy is a choice.

Otherwise, the AI jobs debate will stay stuck on the binary question: “Will AI take jobs?”

The problem with that question is that it invites hot takes and math inspired by doomscrolling headlines, when what we actually need is proper instrumentation: where AI is showing up, how it’s being used (automation vs. augmentation), and when impacts show up in real labor data.

Challenging the AI Jobs Narrative

If you ask Jack Dorsey, his answer would be, “Yes, in fact, AI just took 4,000 jobs at Block.”

In a post on X by @Jack, Block cut roughly 4,000 roles (about 40% of a ~10,000-person workforce), with Dorsey framing it as an AI-driven productivity/efficiency shift. This narrative fueled an immediate debate about “AI-washing” vs. real operating leverage.

But then you have to follow up, “really, Jack!? Did AI really allow you to cut 40% of your workforce or was that just a great narrative to drive up shareholder value?”

Anthropic’s new labor-market report helps us great a clearer picture of Where is AI actually showing up in work today, and how fast is the gap closing between potential and practice.

The Gap Between AI Capability and Real-World Use

For example, Figure 2 (below) challenges the “AI is taking jobs” narrative, at least today. At first glance, it represents AI’s theoretical capability against current usage organized by occupational category. What we’re basically looking at is AI’s practical reach is still much smaller than its theoretical reach.  

This is exactly why headlines, and now boardroom conversations, are out of sync with the idea of AI vs. its observed reality. Leaders need a repeatable, trustworthy way to separate signal from story, especially when labor impacts can be ambiguous for years and only obvious in hindsight.

Without doing so, leaders believe the headlines and spark the onset of a self-fulfilling prophecy.

In its report, Anthropic introduces “observed exposure,” a measure of displacement risk that blends theoretical LLM capability with real-world Claude usage, and it weights automated (vs. augmentative) and work-related use more heavily.

The report argues we are still in the “workflow adoption” phase, not the “widespread labor replacement” phase. That does not mean the risk is fake. It means the strongest evidence today points to uneven deployment and early hiring effects, not a wave of economy-wide job loss.

Does this mean we’re in the clear? Not so fast…

For now, AI can do more in theory than businesses are actually using it for today.

But what Anthropic does see is an early, weaker signal in hiring, especially for younger workers trying to enter some exposed white-collar jobs. This means that AI is starting to reshape work and hiring, but it does not yet show up as broad job destruction in the labor market data they studied.

According to Anthropic, A job’s exposure is higher if:

• Its tasks are theoretically possible with AI
• Its tasks see significant usage in the Anthropic Economic Index5
• Its tasks are performed in work-related contexts
• It has a relatively higher share of automated use patterns or API implementation
• Its AI-impacted tasks make up a larger share of the overall role

The weighting matters: fully automated implementations get full weight, augmentative use gets half weight, because the labor-market risk isn’t “AI helped,” it’s “AI did.”

In other words: not what AI could do, but what it’s already being used to do at work.

And users are already self-selecting into what LLMs do best: 97% of tasks observed in the Economic Index fall into categories rated theoretically feasible (β=0.5 or 1.0). Even more telling: tasks rated fully feasible (β=1) make up 68% of observed usage; tasks rated not feasible (β=0) are just 3%.

Well, there’s a gap, and that’s the real story.

That gap is also the transformation window, where leadership decisions (governance, incentives, redesign, risk, compliance, determine whether AI stays a co-pilot or becomes a pilot (and infrastructure).

In “Computer & Math” roles, they estimate 94% of tasks are theoretically feasible for LLMs, but Claude’s current coverage is only 33%. Capability is racing ahead. Adoption and deployment depth are not.

This is the part most narratives miss: capability headlines don’t mean much; deployment depth is where organizations either replatform work, or don’t.

Where does the impact concentrate first?

The “most exposed” list reads like a roadmap of early automation:

• Computer Programmers: 75% coverage | Leading Automating Task: Write, update, and maintain software programs
• Customer Service Representatives: 70% | Leading Automating Task: Confer with customers to provide info, take orders, handle complaints
• Data Entry Keyers: 67% | Leading Automating Task: Read source documents and enter data into systems
• Medical Records Specialists: 66% | Leading Automating Task: Compile, abstract, and code patient data
• Market research analysts and marketing specialists: 65% | Leading Automating Task: Prepare reports of findings, illustrating data graphically and translating complex findings into written text
• Sales representatives, wholesale and manufacturing, except technical and scientific product: 63% | Leading Automating Task: Contact customers to demonstrate products and solicit orders
• Financial and investment analysts: 57% | Leading Automating Task: Inform investment decisions by analyzing financial information to forecast business, industry, or economic conditions
• Software quality assurance analysts and testers: 52% | Leading Automating Task: Modify software to correct errors or improve performance
• Information security analysts: 49% | Leading Automating Task: Perform risk assessments and test data processing security
• Computer user support specialists: 47% | Leading Automating Task: Answer user inquiries regarding computer software or hardware operation to resolve problems

At the other end, 30% of workers show zero coverage in their dataset—jobs where tasks don’t show up enough in usage to clear their threshold. That alone should temper the most breathless headlines.

I want to point out an important nuance, “zero coverage” isn’t “zero exposure forever.” Anthropic notes this is often a measurement boundary, tasks appeared too infrequently in their sample to meet the minimum threshold. Said another way, AI impact won’t be universal or simultaneous. It will be uneven, threshold-based, and shaped by where work becomes digitized enough to be absorbed into workflows.

Where AI Is Hitting First

Now for the part leaders should not ignore: the entry-level signal.

Anthropic does not find broad unemployment effects yet. What it does find though is an early shift in hiring, especially for younger workers. Beginning in 2024, workers ages 22–25 pursuing highly exposed occupations show an estimated 14% decline in job-finding rates versus 2022. The evidence is modest, but it may be an early signal of where disruption appears first.

Two extra data points that sharpen this: job-finding rates in less exposed occupations stay around 2% per month, while entry into the most exposed jobs drops by about half a percentage point. And they note this pattern does not show up for workers older than 25.

They also offer plausible alternative interpretations (not just leaving things at “AI did it”): entrants could be staying put, switching to different occupations, or returning to school.

This is how disruption often begins, however. It doesn’t start with mass layoffs, but with quieter signals that compound over time.

If the first rung of the career ladder changes, everything above it eventually changes too.

Unemployment is a lagging indicator. The early signal is often career access, who gets the job, who gets trained, who gets mentored, and who gets filtered out before they ever count in the data.

Also worth noting: the “most exposed” group skews more female (+16 pp), earns 47% more on average, and is far more credentialed, graduate degrees are 17.4% vs. 4.5% in the unexposed group. This isn’t only a story about “low-skill” work.

This is primarily a white-collar, highly educated workforce story, which means leaders need to think beyond hourly labor and focus on how professional roles, knowledge work, and entry-level career paths are being redesigned vs. how they should be designed.

One more stat that supports measurement over myth: Anthropic compares exposure to the US Bureau of Labor Statistics (BLS) projections (2024–2034) and found a slight relationship, every +10 percentage points of coverage corresponds to about a 0.6 percentage point lower projected growth. Said another way, AI exposure appears to be showing up most in occupations that already look like they’ll grow less. This suggests observed exposure can function like an early-warning device for where job redesign and hiring friction might appear first.

My takeaway: AI isn’t just improving productivity. It’s changing the unit of work. And once the unit of work changes, everything downstream changes: org design, hiring, training, compensation, and career paths.

And impacts may not be linear. The report explicitly raises the idea that some jobs behave like “O-ring” systems, where you don’t see real employment effects until most tasks have AI penetration. That’s another reason the “% exposed” debate is misleading. The “O-ring” idea points to a process that can be limited by its weakest link. If one part still needs a human, you may not see big labor impact until most parts are AI-capable and integrated.

The real question is, when will AI cross the tipping point where the workflow can be redesigned around it?

Anthropic also stress-tested the “no unemployment impact” by moving the “high exposure” bar from the median all the way to the 95th percentile and cross-checking against unemployment insurance claims. The result: no clean unemployment signal yet, the trend stays essentially flat, even under more aggressive assumptions.

Leaders, here’s a question you should be asking: How are you redesigning entry-level roles, apprenticeship paths, and early career development in an AI-first workplace? Do your part in preserving the talent pipeline and explore where roles can be redesigned, augmented, with AI not automated by AI.

Please read Anthropic’s report to get the full story/analysis.

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When Richie Cotton asked me on DataCamp’s DataFramed podcast how I stay afloat with everything happening in AI, I joked that I wear “AI floaties.”

Thirty tabs open. New “state of AI’ reports I need to read. Breakthroughs I need to understand. Every day, there’s a new model, a new breakthrough, a new viral article with a new “this changes everything” or “this is the end of…” headline.

Honestly, I’m struggling to keep afloat too with all the AI advancements and industry leaders sharing visions of a future that can make doomscrolling seemingly a reprieve.

And in this relentless cycle, without mindfulness and care, innovation, creativity and optimism can erode.

When everything feels urgent, your thinking gets more constrained. You default to what you know. You optimize yesterday because reinventing tomorrow feels like a luxury reserved for people who aren’t as busy.

Something that has stuck with me throughout each whirlwind, is that on one side, you have stories of AI Natives who are scaling themselves with AI agents. On the other side, you have legacy organizations not realizing ROI in their AI investments.

Tomorrow is being reframed. And as Marshall Goldsmith famously said, “what got you here won’t get you there.

At play is AI Darwinism. Some are aiming reinvent the future with AI while others look to scale yesterday. But you can’t automate your way to innovation. You can, however, mindshift your way to it.

The fork in the road most teams ignore

In the conversation with Richie, we kept circling back to a simple truth: there are two paths forward.

One path is the one most organizations reward because it’s measurable, safe, and familiar.

Iteration: improving yesterday to scale tomorrow.

The other path is the one AI makes unavoidable if you want to stay relevant.

Innovation: creating new value to unlock new opportunities tomorrow.

You need both. But you can’t confuse them.

If AI is only helping you do what you already do, faster, then you’re not transforming. You’re speeding up the past.

So here’s a question I’d like you to introduce into your next AI meeting, especially the ones that sound like “what can we automate” or “where can we gain efficiencies?”

Are we using AI to run faster in the same direction… or to compete differently?

The answer is not one or the other, it’s balance. Do you have balance in how you think about and apply resources to AI iteration and innovation?

Why people resist change

Most transformation efforts fail for one reason that has nothing to do with tools or adoption: it’s that people don’t see themselves on the other side of the change.

Leaders love to talk about vision. Teams hear disruption.

Leaders love to talk about efficiency. Employees feel replaceability.

That’s why vision statements aren’t the key to transformation just like management isn’t the key to ‘change’ management. I

In the episode, we I shared the story about what I learned from Nick Sung, a former Disney/Pixar storyboard artist and the biggest lesson I took away from our work together. and how it tests two things before anyone spends millions animating a film: believability and relatability.

If your vision and strategy aren’t believable, people won’t commit because they can’t see the character they play in that ‘film.’

If it isn’t relatable, people won’t care, because they don’t see the story as relevant to their aspirations or fears or both.

If people can’t see themselves in the story, they will protect themselves from it.

People have to believe that they play a part in the story to change.

Before you ask people to change their work, turn your data into a human journey. Storyboard what changes for the customer. For the employee. For the business. For the person who will have to explain this to their team on a Tuesday when everything is already on fire.

Because transformation doesn’t spread through logic alone.

It spreads through meaning.

“I don’t have time” is a leadership signal

Richie asked the question that is painfully accurate:

How do you find time to do new things when you’re already drowning in old things?

Here’s what I’ve learned (and learned the hard way): time is often a construct built on belief systems.

If you track your week, you’ll discover your calendar is a map of what you value and prioritize. Meetings. Status. Responsiveness. Availability. If not focused on a future motivating state, an articulated destination of what change or success looks like, your time offers an illusion of progress.

And then you wonder why you can’t innovate.

Innovation requires oxygen. Your calendar is often a vacuum.

Which is why one of the most useful soundbites from the conversation is also one of the most uncomfortable:

The most powerful word in transformation is “no.”

If you’ve ever been in a meeting that ends early and someone says, “I’m giving you back seven minutes,” and you feel like you just won the lottery, that’s a sign.

No to the meeting that doesn’t need you.

No to the default 30 or 60 minutes because Outlook decided it was “normal.” (Yes, that’s a real origin story many of us have lived.)

No to “quick calls” that expand to fill every available minute.

And here’s the part we often get wrong: “no” isn’t rejection. It’s focus. It’s direction.

It’s about being accountable to promises you make to yourself, others, and the outcomes the drive toward vision.

The “signal filter” that keeps you from chasing shiny objects

Let’s talk about the other quiet anxiety: trends.

Everyone is expected to “keep up,” but nobody agrees on what “up” even means anymore.

So here’s the simplest way I can make this usable: stop trying to keep up with everything. Start building a practice to tune into signals.

A signal becomes worth your attention when it has four qualities:

It has momentum (it keeps showing up), convergence (it connects with other signals), consequence (it changes what’s possible or expected), and context(it matters to your customers and constraints).

That’s how you separate “interesting” from “important” and “everything” into “what matters.”

And yes, this is also how you avoid getting burned by hype cycles.

Take the metaverse. If you only experienced it through hype, you’d think it disappeared. If you watch it through signals, you see something else: spatial computing, world models, and new creation tools are steadily improving.

For example, World Labs’ “Marble” is positioned as a multimodal “world model” that can generate and iteratively edit explorable 3D worlds. That doesn’t mean “metaverse now.” It means the underlying ingredients are evolving and immersive, dynamic, generative worlds for people, physicalAI, humanoids, will have new experiences, training models and worlds to explore.

That’s what trend work is supposed to do: help you hold two truths at once.

Not everything is real now. But some things become real fast once the right ingredients converge. Help make it make sense.

The hidden tax of AI: workslop

There’s one more signal worth naming because it’s showing up everywhere: the output looks polished, but it costs everyone time.

Harvard Business Review calls it “workslop,” low-effort AI-generated work that shifts cognitive load onto the recipient.

If AI is “saving time” by dumping unfinished thinking onto your colleagues, that’s not productivity. That’s cognitive Darwinism, a tax you give yourself and others. And it’s also how trust erodes inside teams.

Here’s a standard I’d love to see become contagious:

If you use AI to produce something, your job isn’t to hit send faster. Your job is to make it better than what you could have done alone….please.

A practical mindshift sprint you can run this week

If you want to turn this into action, don’t start with “AI strategy.”

Start with a short sprint that changes how you work and how your team thinks.

Idea 1: Run the Iteration/Innovation Test. Pick one AI initiative you’re working on. Write two sentences. One describing how it improves yesterday. One describing how it creates new value. If you can’t write the second sentence, you don’t have an innovation initiative yet.

Idea 2: Build your Signal Filter. Choose three signals you’ll track for 30 days. Not 50. Three. Maybe, five. Assign each signal a consequence: what would it change for customers, employees, cost, speed, risk, or differentiation? (learn more about how to practice futures in Mindshift).

Idea 3: Storyboard the human journey. Take your most important AI effort and write a simple arc: Before, during, after. Who struggles today? What changes? What becomes possible? Where does fear show up? Where does agency show up? If your storyboard doesn’t include emotion, you’re not done.

Idea 4: Defend two hours. Block two hours on your calendar this week as “innovation time.” Then protect it. Practice the word “no” with a reason. Practice “yes” with a boundary.

Idea 5: Eliminate one recurring meeting. Just one. Replace it with an async update. Or shorten it to 25 minutes with a single outcome. Then measure what happens to clarity, speed, and morale.

Idea 6: Declare a “no workslop” standard. Make it cultural. Tell your team: “AI output must be edited, contextualized, and improved before it reaches someone else.” If AI is involved, the bar goes up, not down.

Idea 7: Become the leader you’re waiting for. This is the closer I shared on the show, and it’s the line I keep coming back to: if you’re waiting for someone to tell you what to do, you’re often waiting on the wrong side of innovation.

Management isn’t leadership. And leadership isn’t a title. It’s influence. It’s the ability to change minds, behavior, and outcomes.

It’s choosing the “aha” moment over the “uh-oh” moment…most people respond only when something disrupts them, hence, “uh-oh.” “Aha” on the other hand, is about giving yourself time, space, and permission to think, to ask questions, to wonder, and to imagine.

It’s deciding that the future isn’t something you react to. It’s something you shape.

If you take nothing else from this, take this:

Iteration keeps you in the game. Innovation changes the game. AI can do both. Your mindset decides which path you’re on.

Please watch to dive deeper into all these topics and more!

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The post Why AI Darwinism Needs a Mindshift, and why You Can’t Automate Your Way to Business Model Innovation appeared first on Brian Solis.

Photo: Baker Hughes

Coverage of Brian’s keynote at the Baker Hughes Annual Meeting in Florence, Italy, source: Enlit, Kelvin Ross

Using artificial intelligence to transform the energy sector isn’t about automation – it’s about augmentation, says Brian Solis

An AI digital futurist has urged energy leaders to see the full potential of artificial intelligence and not simply use the technology to cut corners cheaply.

“The future doesn’t belong to companies that use the most AI,” said Brian Solis. “It’s those who reimagine their enterprise for a ‘control-alt-delete moment’ to reboot the human workforce as not just more intelligent, but more capable.”

Speaking to hundreds of power and utility professionals at the Baker Hughes Annual Meeting in the Italian city of Florence, regarded as the birthplace of the Renaissance, Solis used that historical landmark in his speech.

“The next industrial Renaissance is giving way to intelligent industry. And intelligent industry isn’t judged by how much AI or technology it uses: it is measured by – and remembered by – how that intelligence is employed.”

Solis is Head of Global Innovation at San Francisco software company ServiceNow and author of several books about using disruptive technologies to affect a change in business mindset, the most recent being Mindshift: Transform Leadership, Drive Innovation, and Reshape the Future.

These were themes he stressed in his speech about utilising artificial intelligence. “The intelligent industry is really about reimagining enterprise, so you have not just AI literacy in the organisation, but AI vision for what the enterprise could be beyond automation.

“If you think about the last 20 years and the evolution of digital transformation, we didn’t really transform… we just digitised yesterday’s work. And research shows that we’re already doing that today with artificial intelligence.”

However, Solis said that realising the potential of AI “isn’t just about automating – this is about augmenting work”.

“This is about doing new work in new ways. Not just yesterday’s work better, or cheaper, or more efficiently – that impedes your ability to compete and impedes your ability to innovate.

Leadership moment

“AI is not a strategy,” he said. “Transformation to do what you couldn’t do yesterday and deliver greater value… that’s a strategy. Technology needs operating, and so do people. That’s why this is a leadership moment.”

He said that many companies are already using AI while others were adopting a wait-and-see approach, but he added that “the companies that will thrive, the companies that will win, will refactor energy to their benefit. They will invest in unlocked energy opportunities at scale, to innovate with artificial intelligence, to augment workers and transform the workforce.”

He said “AI Darwinism” would prove that the companies that succeed will be “more operationally efficient and resilient” by “outsmarting competitors with human and AI collaboration”.

“Nothing interesting begins with knowing,” he concluded. “Innovation does not begin with knowing. This is your moment to rewrite energy… and that takes vision, courage and leadership.”

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