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Volgens Brian Solis wordt de toekomst van AI momenteel verkeerd geframed door headlines, algoritmes en invloedrijke stemmen binnen de industrie. Hij stelt dat we ons midden in een van de belangrijkste technological verschuivingen ooit bevinden, maar dat het publieke debat daarover intellectueel misleidend is geworden. Zoals hij zelf scherp verwoordt: “AI’s biggest challenge right now is not only technical. It is narrative.” Aan de jan kant domineren utopische beloftes, aan de andere kant doemscenario’s over massale werkloosheid en economische instorting. Volgens Solis ligt de waarheid daar ergens tussenin, maar wordt die overschaduwd door extreme verhalen die strategie en besluitvorming negatief beïnvloeden.

Leiders zouden volgens hem niet deze narratieven moeten volgen, maar juist richting moeten geven:

Leaders shouldn’t follow this narrative, but instead offer vision and clarity about what comes next.

Een waltz keuzekader: vereren of vrezen

Solis bekritiseert het dominant discours waarin AI wordt gepresenteerd als iets dat je óf moet aanbidden óf moet vrezen. Hij noemt dit een waltz dilemma dat het vertrouwen ondermijnt: “Either worship it or fear it.”  Zelf positioneert hij zich expliciet als optimist. Hij gelooft dat AI enormous kansen biedt om menselijke waarde te vergroten: “AI can help us unlock entirely new possibilities that make humans matter more.” Tegelijkertijd verwerpt hij de sensatiezucht die het debate domineert: “I’m not buying the sensationalism, nor should you.”  Volgens Solis ligt het echte problem niet in de technologie, maar in hoe die wordt gepresenteerd: “The AI ​​we need in headlines and in our feeds has a message problem.”

Het narratief werkt averechts

Volgens Solis is het huidige AI-narratief niet alleen verwarrend, maar ook schadelijk. De constant stroom aan berichten over naderende AGI, superintelligentie en baanverlies zorgt voor groeiende angst en weerstand. Hij vat die paradox krachtig samen: “If you keep telling people your AI will upend their lives… they’re going to believe you.”  De tegenstrijdigheden binnen de industrie versterken dit effect. Uitspraken die tegelijkertijd wijzen op economische instorting én ongekende productiviteitsgroei read tot wat Solis noemt: “cognitive and emotional whiplash.”  Het gevolg is een groeiende kloof tussen technologie en vertrouwen. Mensen reageren niet alleen op AI zelf, maar vooral op het verhaal eromheen: “People are reacting to the story being told about AI.”

Angst, wantrouwen in Generatie Z

Solis onderstreept dat deze narratieven meetbare gevolgen hebben, vooral bij jongere generaties. Hoewel het gebruik van AI toeneemt, neemt het vertrouwen af. Hij wijst erop dat dit no irrationele angst is, maar een reactie op reële signalen uit de arbeidsmarkt. From vraag “Where do I fit in this future?” It is volgens hem no paniekreactie, maar een logische reflectie van economische onzekerheid. In juist daar ligt een fundamenteel probleem: technologie belooft vooruitgang, terwijl de ervaring van veel mensen onzekerheid bevestigt.

Voorspellingen zijn no bestemming

Een belangrijk punt in Solis’ analysis is dat voorspellingen over AI vaak worden gepresenteerd als onvermijdelijke uitkomsten. Hij verwerpt dat idee resoluut: “We keep arguing capability as if capability alone determines destiny. It doesn’t.”  Zelfs binnen de AI-wereld bestaat er no consensus over de impact op werk en economie. Daarom pleit hij ervoor om deze uitspraken te zien als narratieven, niet als feiten: “Maybe we should stop treating every dramatic forecast as inevitable truth.”  Volgens Solis ondermijnt juist die schijn van onvermijdelijkheid het vertrouwen: “Right now, that narrative choice is eroding trust.”

Onder de oppervlakte ziet Solis een belangrijkere ontwikkeling: de kloof tussen wat AI kan en hoe het daadwerkelijk wordt gebruikt. Hijst naar het idea van een “capability overhang” en benadrukt:

The future won’t split neatly between people who have AI and people who don’t. It will split between people who let AI do their thinking and people who use AI to elevate how they think.

Dit onderscheid is volgens hem cruciaal. Het gaat niet om toegang tot technologie, maar om de manier waarop mensen ermee omgaan.

Leiderschap als ontbrekende schakel

Voor Solis ligt de oplossing niet in meer technologie, maar in better leiderschap. Hij roept leiders op om het debat te herijken en richting te geven:  “The real job of leaders right now is to lower the temperature and raise the standard.”  Dat betekent concreet: minder hype, meer duidelijkheid. Minder angst, meer perspectief. Leiders moeten laten zien hoe AI mensen sterker maakt, niet overbodig:  “Show them how AI can make them more capable, more creative, and more valuable, not more disposable.”  Daarnaast benadrukt hij dat AI niet alleen een kostenbesparinginstrument is, maar een kans voor waardecreatie: “AI automation is one part… but AI augmentation + automation unlocks new value.”

Van angst naar geloofwaardige toekomst

Volgens Solis willen mensen niet alleen weten wat er verandert, maar vooral wat hun rol daarin is. Zonder dat perspective groeit de weerstand. Hij waarschuwt: “If leaders cannot answer that… the backlash… will look basic in hindsight.”  Succesvolle organisaties zullen volgens hem degenen zijn die een toekomst kunnen schetsen die zowel geloofwaardig als inspirerend is:

The winners in this era will be the ones who make the future feel believable and aspirational.

Zij vervangen angst door begrip, hype door beoordelingsvermogen en onvermijdelijkheid door intentie: “They will replace fear with fluency, hype with judgment, and inevitability with intention.”

From kernboodschap: from toekomst is maakbaar

Solis sluit af met no duidelijke visie op de rol van AI in de samenleving. De toekomst is not vaststaand gegeven, maar een gezamenlijke verantwoordelijkheid: “The future of AI is not something that just happens to us. The future of AI happens because of us.”  Daarbij verschuift de focus van automatisering naar versterking van menselijke capaciteiten: “Not automation. Augmentation.”

In van doemdenken naar richting in betekenis: “Not doomsday prophecy. Vision and purpose.”  Zijn concludes is helder en krachtig:

The future of AI does not need more prophets. The future leaders needs people can trust.

Google Translate

Brian Solis: “The industry is giving a false picture of the future of AI”

According to Brian Solis, the future of AI is currently being misframed by headlines, algorithms, and influential voices within the industry. He argues that we are in the midst of one of the most significant technological shifts ever, but that the public debate surrounding it has become intellectually misleading. As he sharply puts it himself: “AI’s biggest challenge right now is not only technical. It is narrative.”  On the one hand, utopian promises dominate; on the other, doomsday scenarios of mass unemployment and economic collapse. According to Solis, the truth lies somewhere in between, but is overshadowed by extreme narratives that negatively influence strategy and decision-making.

According to him, leaders should not follow these narratives, but rather provide direction:

Leaders shouldn’t follow this narrative, but instead offer vision and clarity about what comes next.

A false choice framework: venerate or fear

Solis criticizes the dominant discourse in which AI is presented as something you must either worship or fear. He calls this a false dilemma that undermines trust: “Either worship it or fear it.”  He explicitly positions himself as an optimist. He believes that AI offers enormous opportunities to increase human value: “AI can help us unlock entirely new possibilities that make humans matter more.” At the same time, he rejects the sensationalism that dominates the debate: “I’m not buying the sensationalism, nor should you.”  According to Solis, the real problem lies not in the technology, but in how it is presented: “The AI ​​we need in headlines and in our feeds has a message problem.”

The narrative backfires.

According to Solis, the current AI narrative is not only confusing but also harmful. The constant stream of messages about impending AGI, superintelligence, and job losses is causing growing fear and resistance. He powerfully summarizes this paradox: “If you keep telling people your AI will upend their lives… they’re going to believe you.”  The contradictions within the industry reinforce this effect. Statements that simultaneously point to economic collapse and unprecedented productivity growth lead to what Solis calls “cognitive and emotional whiplash.”  The result is a growing gap between technology and trust. People are reacting not only to AI itself but, above all, to the story surrounding it: “People are reacting to the story being told about AI.”

Fear, distrust and Generation Z

Solis emphasizes that these narratives have measurable consequences, especially among younger generations. Although the use of AI is increasing, trust is declining. He points out that this is not an irrational fear, but a reaction to real signals from the labor market. According to him, the question “Where do I fit in this future?” is not a panic reaction, but a logical reflection of economic uncertainty. And that is precisely where a fundamental problem lies: technology promises progress, while the experience of many people confirms uncertainty.

Predictions are not destinations

A key point in Solis’ analysis is that predictions about AI are often presented as inevitable outcomes. He resolutely rejects that idea: “We keep arguing capability as if capability alone determines destiny. It doesn’t.”  Even within the AI ​​world, there is no consensus regarding the impact on jobs and the economy. Therefore, he argues for viewing these statements as narratives, not facts: “Maybe we should stop treating every dramatic forecast as inevitable truth.”  According to Solis, it is precisely that semblance of inevitability that undermines trust: “Right now, that narrative choice is eroding trust.”

Beneath the surface, Solis sees a more significant development: the gap between what AI can do and how it is actually used. He refers to the idea of ​​a “capability overhang” and emphasizes:

The future won’t split neatly between people who have AI and people who don’t. It will split between people who let AI do their thinking and people who use AI to elevate how they think.

According to him, this distinction is crucial. It is not about access to technology, but about the way people interact with it.

Leadership as the missing link

For Solis, the solution lies not in more technology, but in better leadership. He calls on leaders to recalibrate the debate and provide direction:  “The real job of leaders right now is to lower the temperature and raise the standard.”  In concrete terms, this means: less hype, more clarity. Less fear, more perspective. Leaders must show how AI makes people stronger, not redundant:  “Show them how AI can make them more capable, more creative, and more valuable, not more disposable.”  Additionally, he emphasizes that AI is not just a cost-saving tool, but an opportunity for value creation: “AI automation is one part… but AI augmentation + automation unlocks new value.”

From fear to a credible future

According to Solis, people want to know not only what is changing, but above all what their role in it is. Without that perspective, resistance grows. He warns: “If leaders cannot answer that… the backlash… will look basic in hindsight.”  According to him, successful organizations will be those that can outline a future that is both credible and inspiring:

The winners in this era will be the ones who make the future feel credible and aspirational.

They replace fear with understanding, hype with judgment, and inevitability with intention: “They will replace fear with fluency, hype with judgment, and inevitability with intention.”

The core message: the future is malleable

Solis concludes with a clear vision of the role of AI in society. The future is not a fixed given, but a shared responsibility: “The future of AI is not something that just happens to us. The future of AI happens because of us.”  In this context, the focus shifts from automation to enhancing human capabilities: “Not automation. Augmentation.”

And from doom-thinking to direction and meaning: “Not doomsday prophecy. Vision and purpose.”  His conclusion is clear and powerful:

The future of AI does not need more prophets. The future needs leaders people can trust.

The post Brian Solis: De industrie geeft de toekomst van AI een verkeerd beeld appeared first on Brian Solis.

Jensen Huang and Ylli Bajraktari on the Special Competitive Studies Project (SCSP) podcast, “Memos to the President”

Right now, AI narratives seem to be propagating an ROI of return on ignorance instead of a more productive ROI, “return on intelligence.”

Enterprise leaders are currently navigating a critical transition toward AI-native operating models, yet AI adoption is stalling due to a fundamentally flawed narrative. Instead of focusing on strategic growth, the leadership conversation is paralyzed by speculative doomsday prophecies, a paralyzing fear of change, and a narrow focus on task automation for cost-cutting. This alarmist approach to the future of work is a massive branding problem for artificial intelligence. It actively hinders innovation, limits business transformation, and limits human potential.

When the loudest voices in the room frame AI as an existential threat designed to wipe out entire professions, they distract us from the technology’s true mandate: human augmentation. We need to shift the conversation away from this anxiety and start measuring our progress by a genuine Return on Intelligence.

The True ROI of AI: Stop Fearing Task Elimination and Start Architecting Human Augmentation

Nvidia CEO Jensen Huang recently addressed this exact crisis of communication, pushing back against the industry’s “god complex” and re-centering the discussion on the factual realities of job creation, economic growth, and the future of work.

Jensen Huang recently joined Ylli Bajraktari, the CEO of the Special Competitive Studies Project (SCSP). The conversation took place on SCSP’s podcast, “Memos to the President.”

In the conversation, Jensen expertly, and sincerely, argues that alarmist claims about AI destroying jobs are fundamentally flawed and hurtful. He believes that AI is a powerful engine for job creation and reindustrialization. He challenges the leadership fear of change, reframing AI not as a mechanism for pure automation and cost-cutting, but as a tool for human augmentation that delivers a new ROI, “Return on Intelligence.”

As I was listening, I had to stop and notate meaningful quotes that I feel will benefit any leader navigating AI transformation…from AI native founders to AI-first and AI-forward leadership teams to academics and thought leaders.

“On the one hand, maybe a scientist thinks that by warning people that AI is going to completely permeate and proliferate across radiology and therefore radiologists are going to get wiped out…

…that might be considered warning and therefore helpful. But in fact, the counter would have been hurtful.

If we convinced everybody not to be radiologists, and we now need radiologists, that actually is hurtful to society.

It is hurtful if we convince all the young college graduates to not be software engineers and it turns out the United States need more software engineers than ever. That’s hurtful.”
“…we have to be mindful of how we communicate the importance of this technology and what it’s able to do, to advocate for policy and advocate for guardrails….

…scaring people with…nonsensical things, which are not going to happen…that this is an existential threat to humanity, there’s a 20% chance that it is existential. That’s ridiculous. That it’s going to wipe out 50% of new college grad jobs. That it’s going to completely destroy democracy. I mean, these kind of comments are not helpful.
…they’re made by, you know, people who are like me, CEOs…and somehow, because they became CEOs, you adopt a god complex and before you know it, you know everything.

…we have to be careful and really ground ourselves to talking about the facts.

The facts are AI has created more than half a million jobs in the last couple of years.

The facts are AI is our greatest, our best opportunity to reindustrialize the United States, to bring manufacturing jobs back to the United States.

The facts are that’s going to generate hundreds of thousands of jobs, trillions of dollars of new economy back into the United States.

The fact of the matter is companies that use AI have demonstrated the ability to grow faster. When they grow faster, they hire more people…AI creates jobs.”

The Mindshift from Return on Investment to Return on Intelligence

I was going to say, “louder, for the people in the back.” But instead, I’ll say “louder, for the people in the front!” Those who are the loudest are among the most influential. As the old saying goes, if you’re not part of the solution, you’re part of the problem.

By offloading mundane tasks, AI frees us to apply our imagination to more complex problems, expanding our ambitions and capabilities rather than shrinking our workforce. The narrative needs to shift from automating yesterday’s jobs toward yesterdays performance standards to visualizing tomorrow’s possibilities, making vision tangible, and articulating tomorrow’s roles and tasks that will get us there through human + AI collaboration.

That’s the mindshift from an automation mindset (cost-cutting and efficiency only) to augmentation (value creation, growth, and innovation).

The ultimate promise of an AI-first operating model is not found in a spreadsheet of slashed budgets and reduced headcount, but in the profound shift from task automation to human augmentation. By embracing this mindset, leaders can stop measuring success by what they save and start tracking their true “Return on Intelligence,” the exponential value created when human imagination is finally freed to tackle our greatest challenges. This is our mandate: to stop using AI merely to do less, and start deploying it to expand what we are fundamentally capable of achieving.

The future of AI does not need more doomsday prophets and clickbait headlines. The future needs vision and leaders people can trust.

Watch the video here

https://x.com/briansolis/status/2050617352681922577

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The post AI Doesn’t Take Jobs, It Creates Them: Jensen Huang’s Reality Check for the AI Alarmists and Doom-Sayers appeared first on Brian Solis.

Dave Wright, Allie K. Miller, Brian Solis

At ServiceNow’s Knowledge last year, I had the privilege of sitting down on camera with my dear friend Allie K. Miller and my colleague Dave Wright, ServiceNow’s Chief Innovation Officer, for one of the most honest conversations I’ve had about the state of enterprise AI. And, the conversation still represents the state of AI in business today.

Allie. who was recognized on the TIME100 AI list for 2025, and for good reason, has a rare ability to cut through AI hype and get to the part that actually matters: what are leaders supposed to do with all of this? She pushed Dave and me into territory that I think every executive needs to hear right now.

I want to share the key themes from that conversation because I know not everyone will watch the video. But I hope you do — there are nuances and real-time reactions in the discussion that I can’t fully capture here.

Watch the full conversation: Innovation and Industry Trends with Dave Wright and Brian Solis

AI Summary

In this insightful discussion Dave Wright, Chief Innovation Officer at ServiceNow, and Brian Solis, Head of Global Innovation at ServiceNow, explore the complexities and future of AI. The conversation delves into the evolving landscape of AI, focusing on its rapid development and the challenges organizations face in keeping pace. The speakers discuss the decline in AI maturity scores, the importance of thinking beyond traditional AI applications, and the potential of AI to transform business operations. The session is structured with real-world examples and strategic insights, and it encourages organizations to rethink their AI strategies for greater innovation and competitive advantage. My full analysis is below this section.

Key Takeaways Summarized by AI

AI Maturity Decline: Brian Solis highlights a surprising drop in AI maturity scores, attributing it to the rapid pace of technological advancement outstripping companies’ ability to adapt and invest effectively. This underscores the need for businesses to accelerate their AI adoption strategies.

Strategic AI Deployment: Dave Wright emphasizes that AI strategies should focus on achieving broader business goals rather than just implementing AI for its own sake. This approach can lead to significant organizational transformation and competitive advantage.

Cultural Shift: Both speakers stress the importance of a cultural and mindset shift within organizations to fully leverage AI’s potential. This involves reimagining work processes and encouraging a more innovative approach to AI integration.

AI as a Competitive Advantage: Companies that invest in AI are realizing value faster, creating a competitive edge. The discussion highlights the importance of not just adopting AI but doing so strategically to enhance business outcomes.

Future of AI Roles: The conversation explores the concept of AI agents evolving into AI employees, suggesting a future where AI could take on more complex roles within organizations, potentially reshaping job functions and organizational structures.

Human Experience and AI: Brian Solis points out that while AI can enhance efficiency, the human experience remains a crucial differentiator. Companies should focus on delivering memorable and meaningful experiences through AI-enhanced interactions.

AI as a Catalyst for Innovation: The speakers encourage organizations to use AI not just for efficiency but as a tool for innovation, challenging existing workflows and exploring new possibilities for growth and creativity.

The video also includes additional insights on AI’s role in organizational transformation, offering a deeper look at strategic AI deployment and its impact on business innovation.

ServiceNow Enterprise AI Maturity Index

AI Maturity Is Going Down, Not Up. And That’s Actually the Story.

In 2023 continuing into 2024, part of my work at ServiceNow studied how enterprise organizations adopted generative AI, the use cases they invested in and how they made the case, how they organized in support of it, and the measures and governance they put into place. The result was a five-stage AI maturity model based on seven foundational pillars that tracked how companies evolved with experience and vision. In 2024, the research team led a global field survey to understand where organizations mapped to the model. Our first report, the Enterprise AI Maturity Index, was published in 2024 and the second in 2025. Our third edition is due out in June 2026.

One of the most interesting findings was that between 2025 and 2024, AI maturity scores declined.

In 2025, ServiceNow’s Enterprise AI Maturity Index studied 4,500 organizations across 16 countries and 11 industries. The research was conducted in partnership with Oxford Economics. The results found that the average maturity score dropped nine points year over year, from 44/100 down to 35/100.

Why? Because the goalposts are moving faster than companies can run. The sheer velocity of AI advancement, new models, new capabilities, new use cases arriving weekly, is overwhelming organizations that were already struggling to operationalize what they had. Companies are hitting a “control-delete moment,” where leaders suddenly realize that AI’s potential is far greater than they initially understood, and they’re forced to rethink everything they thought they knew.

This is not a failure story. This is a recalibration story. And the companies that recognize it as such, that use this moment to pause, reassess, and think bigger, are the ones that will ultimately pull ahead.

Stop Deploying AI. Start Deploying Strategy.

One of Dave’s sharpest points in the conversation, and something Allie pressed us both on, was the distinction between deploying AI and deploying a strategy that happens to use AI.

Too many organizations are chasing AI for the sake of AI. They’re checking boxes. They’re launching pilots. They’re bolting on chatbots and calling it transformation.

Dave was direct about this: AI strategies should focus on achieving broader business goals, not on implementing AI for its own sake.  When business outcomes are the objective, you get progress and competitive advantage. The companies leading in maturity, what we call the AI Pacesetters are rethinking how their businesses work with AI.

The research bears this out. The top three industries in AI maturity are technology, heavy manufacturing, and banking. What do they have in common? They were early adopters of automation and already had the organizational muscle to absorb and scale new capabilities. It wasn’t their AI budgets that set them apart. It was their readiness to think structurally about change.

Culture Eats AI Strategy for Breakfast

The biggest barrier to scaling AI toward real transformation is not technology. It’s not budget. It’s not talent. It’s leadership and a culture empowered to transform and innovate.

Allie pushed both of us to articulate what cultural transformation actually looks like in practice.

Most organizations are asking their people to adopt AI, to gain AI fluency, within a culture that was never designed for experimentation, failure, or reinvention. They’re asking people to think differently while rewarding them for thinking the same way they always have.

An AI-first culture doesn’t mean everyone uses AI tools. It means the organization has fundamentally reimagined how work gets done, how decisions get made, and how value gets created. It means asking questions that challenge legacy assumptions rather than reinforcing them. It means giving people permission, and incentives, to explore what’s possible, not just what’s efficient.

This is where leadership matters most. And it’s where most leadership is falling short.

The research is clear: the biggest barrier to scaling AI is not employees. It’s leaders who are not steering fast enough or dreaming big enough.

From AI Agents to AI Employees

One of the most provocative threads in our conversation with Allie was the evolution of AI roles within organizations. We’re already past the era of AI as a tool. We’re entering the era of AI as a teammate. And eventually, as Dave suggested, we’ll be talking about AI employees.

Think about what that means. AI agents are already handling tasks autonomously, summarizing cases, routing requests, generating recommendations. But the next step is AI taking on more complex roles that require judgment, coordination, and context. It’s AI that doesn’t just execute a workflow but participates in shaping it.

This raises questions that most organizations haven’t even begun to ask. What does the org chart look like when some of your most scalable “employees” aren’t human? What does management mean when you’re overseeing agents as well as people? What does talent development look like when the skills that matter most are the ones AI can’t replicate…empathy, creativity, ethical judgment, the ability to ask questions that haven’t been asked before?

As far out as they may seem, they are near-term planning questions. And if you’re not having them in your leadership meetings right now, you’re already behind. (Here’s a report I just published to get your started!)

The Human Experience is Still the Differentiator

This is the point I keep coming back to, and the one I wanted to make sure came through in the conversation with Allie and Dave: in a world where everyone has access to the same AI capabilities, the human experience becomes the ultimate competitive advantage.

AI can make your operations faster. It can make your processes more efficient. It can surface insights you might have missed. But it cannot, on its own, make your customers feel understood. It cannot feel empathy. It cannot build trust. It cannot create the kind of experience that makes someone choose you over every other option.

I used Amazon as an example during the conversation. Amazon’s automation is legendary. But what makes Amazon Amazon is not its efficiency, it’s its obsession with the customer experience. Every automated process exists in service of a human outcome.

Companies that use AI only to cut costs and accelerate throughput will find themselves competing on a very narrow playing field. Companies that use AI to reimagine the human experience, for customers and employees alike, will define the next era of competitive differentiation.

AI Is Not Just an Efficiency Play. It’s an Innovation Engine.

This was the throughline of our entire conversation, and it’s the thing I wish every CEO would internalize: AI is not just a tool for doing what you already do, faster and cheaper. It’s also a catalyst for doing things you’ve never been able to do at all.

The distinction I’ve drawn in my work between Iterative AI and Innovative AI matters here. Iterative AI removes friction and drudgery. It accelerates existing workflows. That’s valuable. But Innovative AI is where the real disruption lives. It unlocks new results, introduces new ways of creating value, and opens the door to entirely new business models.

Most organizations are stuck in Iterative mode. They’re using AI to optimize candles when the opportunity is to invent the light bulb.

Dave and I both pushed this point hard in the conversation. If you’re only thinking about AI in terms of cost savings and efficiency gains, you’re leaving the most transformative value on the table. The organizations that will lead the next decade are the ones using AI to challenge existing workflows, explore possibilities they couldn’t access before, and reimagine what their business could become.

Here’s Where I’d Like to Leave You

AI maturity regressed across the board. The technology is accelerating faster than our organizations, our cultures, and our leadership models can absorb. And most companies are responding by doing more of the same…more pilots, more tools, more incremental optimization. But this moment demands something fundamentally different.

It demands a shift in mindset (a mindshift). A shift from digital-first to AI-first. A shift from asking “How do I do this faster?” to “Why do we do this at all?” From automating the past to designing the future.

It demands tough conversations about workforce transformation, about organizational structure, about what work even means when AI agents are part of the team.

And it demands leaders who are willing to dream bigger than the status quo. A point we made in conversation fits perfectly here…the risk is not thinking too big, it’s thinking too small.

Great leaders design a better future that would not have otherwise happened.

Watch the full conversation with Dave, Allie, and me: Innovation and Industry Trends with Dave Wright and Brian Solis at Knowledge 2025.

Join Us at Knowledge 2026

And I’ll be back at Knowledge 2026, May 5–7 in Las Vegas, on stage with ServiceNow Chief Innovation Officer Dave Wright where we’ll explore our 2026 AI Index findings, “Your Guide to AI Maturity: Charting a Path to ROI.”

Session Description:
AI maturity isn’t a destination. It’s a leadership imperative. This hands-on workshop invites business leaders to collaborate with experts and peers to uncover what it takes to lead in AI. Through guided discussions about the Enterprise AI Maturity Index, attendees will walk away with a clear understanding of where they stand on the AI maturity curve, what Pacesetters are doing differently to realize 160% ROI, and actionable next steps to move from AI experimentation to business reinvention.

If you’re ready to stop optimizing the past and start designing the future, we’ll see you there!

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The post Allie K. Miller in Conversation with ServiceNow Chief Innovation Officer and Head of Global Innovation Brian Solis appeared first on Brian Solis.

Maybe we got digital transformation wrong.

Not slightly wrong. Fundamentally wrong.

Instead of transforming, we digitized. We took legacy models, legacy processes, legacy thinking…and made them faster. We optimized the candle instead of inventing the light bulb.

That was the argument I made on stage at ServiceNow’s Knowledge 2025 in Las Vegas, in a keynote I called “Goodbye Digital Transformation, Hello AI-First Business Transformation.”

Here’s what the data tells us: 70% of digital transformations fall short of their objectives. 51% of organizations saw no increase in performance or profitability. And now with AI, nearly every company is investing, yet only 1% believe they’re at maturity. And 94% of C-suite execs say they’re unsatisfied with their AI solutions.

The problem isn’t the technology. It’s the mindset.

Most organizations are asking digital-first questions: How do I do this faster? How do I reduce effort? How do I optimize efficiency?

Those are the wrong questions.

An AI-first mindset asks different ones: Why do we do it this way at all? What if we reimagined the entire workflow? What becomes possible that wasn’t before?

The distinction I shared on stage: Iterative AI supercharges today’s work. It removes drudgery and accelerates what already exists. Innovative AI is something else entirely. It unlocks new results, new value, new business models.

The biggest barrier to scaling AI is not employees. It’s leaders who are not steering fast enough. The risk isn’t thinking too big. It’s thinking too small.

AI isn’t artificial intelligence. It’s augmented intelligence. And embracing an AI-first mindset means prioritizing possibilities not achievable without it…and outcomes AI couldn’t achieve without you.

Great leaders design a better future that would not have otherwise happened.

What’s your role in defining the future of your business?

Watch the full session here.

And I’ll be back at Knowledge 2026, May 5–7 in Las Vegas, this time on stage with ServiceNow Chief Innovation Officer Dave Wright. Our session is, “Your Guide to AI Maturity: Charting a Path to ROI.”

Session Description:
AI maturity isn’t a destination. It’s a leadership imperative. This hands-on workshop invites business leaders to collaborate with experts and peers to uncover what it takes to lead in AI. Through guided discussions about the Enterprise AI Maturity Index, attendees will walk away with a clear understanding of where they stand on the AI maturity curve, what Pacesetters are doing differently to realize 160% ROI, and actionable next steps to move from AI experimentation to business reinvention.

If you’re ready to stop optimizing the past and start designing the future, we’ll see you there!

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Fear is rising. Trust is falling. And the AI industry has no one to blame but itself.

For the last two years, the loudest voices in AI have sold the future through a mix of inevitability, disruption, and dread: AGI is near, jobs are going away, the economy may collapse, and somehow this is all supposed to inspire confidence. It doesn’t. It creates narrative debt. And now that debt is coming due, especially among the very people who are supposed to inherit this future.

It feels like in the headlines, feeds, and expert commentary, we’re being sold a false choice with AI. Either worship it or fear it.

I’m an optimist. And I believe that AI can help us unlock entirely new possibilities that make humans matter more in driving growth, better outcomes, and a more productive future. That’s why I’m not buying the sensationalism, nor should you.

What I believe is this: the AI we need in headlines and in our feeds has a message problem.

The Narrative is Backfiring

For the last two years, the public has been bombarded with a relentless narrative: AGI is near. Superintelligence is around the corner. White-collar jobs are in jeopardy. Students are graduating into a future that may not have a place for them. Then, somehow, everyone acts surprised when people feel anxious, skeptical, even angry about AI. The New Yorker recently captured the contradiction well: if you keep telling people your AI will upend their lives, take their jobs, and maybe threaten humanity, they’re going to believe you.

Sam Altman recently made that contradiction impossible to miss. In one post, he wrote, “post-AGI, no one is going to work and the economy is going to collapse.” He also said that he was switching to polyphasic sleep because GPT-5.5 in Codex was so good he didn’t want to miss working with it. Put those two ideas together and you don’t get a coherent roadmap for society. You get cognitive and emotional whiplash: apocalypse on one hand, accelerationist adrenaline on the other.

And this is exactly where the narrative breaks. People are reacting to the story being told about AI by some of the people building it. And a growing number of people have had enough.

Gallup’s latest Gen Z research found that while 51% of Gen Z uses generative AI at least weekly, sentiment has moved sharply in the wrong direction. Excitement dropped 14 points to 22%. Hopefulness fell nine points to 18%. Anger rose to 31%. Anxiety held at 42%. Even more telling, 8 in 10 Gen Z respondents said using AI tools is likely to make it harder for them to learn in the future. Among employed Gen Z workers, 48% said the risks of AI in the workforce outweigh the benefits, and 69% said they trust work done without AI more than AI-assisted work. And, 44% of Gen Z workers admit to sabotaging their employers’ AI deployments as a form of rebellion.

The Federal Reserve Bank of New York says recent college graduates entered the end of 2025 with 5.7% unemployment and 42.5% underemployment, the highest underemployment level since 2020. So when young people ask, Where do I fit in this future? that is not fear talking. Unfortunately, it’s reality.

Predications are not Destiny

This is why I think the current AI debate needs to change for the better. We keep arguing capability as if capability alone determines destiny. It doesn’t.

Even inside the AI world, there is no consensus. Anthropic CEO Dario Amodei has argued that AI could disrupt 50% of entry-level white-collar jobs over the next one to five years. Yann LeCun pushed back, saying people should stop listening to AI builders on labor-market effects and start listening to economists instead. And Nobel-winning economist Daron Acemoglu has since argued that these sweeping predictions reflect “motivated reasoning” as much as insight, because labor outcomes depend not just on model capability, but on wages, organization design, job redesign, and whether new forms of work are created at all.

If the people creating the tools cannot agree on what happens next, then maybe we should stop treating every dramatic forecast as inevitable truth and start treating it as what it is: narrative. And right now, that narrative choice is eroding trust.

One reason is that people can see the contradiction in real time. On one side, the public is told not to panic. On the other, executives are openly exploring how AI might absorb more and more managerial and knowledge work.

One report, citing a Wall Street Journal scoop, says Mark Zuckerberg is building a CEO AI agent to help him retrieve information and do parts of his job faster as Meta flattens teams and pushes AI deeper into the organization. The same report says employee reviews are now partly tied to AI usage and describes a rogue internal AI incident that exposed sensitive data for nearly two hours. Whether you view that as innovation or inevitability, it sends a clear message: automation is no longer being aimed only at repetitive tasks. It is climbing the org chart.

So yes, AI has a trust problem. But trust is the downstream effect. The upstream cause feels like cognitive dissonance.

You cannot tell people AI will make them more productive while also telling them it may wipe out the bottom rung of the ladder. You cannot ask students to embrace AI as essential for their future while they watch employers flatten entry-level roles, automate knowledge work, and celebrate being able to do more with fewer people. You cannot effortlessly oscillate between “don’t worry” and “the economy might collapse” and expect the public to interpret that as visionary leadership.

Th Real Opportunity Here is Leadership

At this point, I can’t believe that this is strategy. In fact, I would say that is all starting to accumulate as narrative debt.

And beneath all the noise is a much more important story that leaders are missing.

OpenAI says the typical power user taps about 7x more “thinking power” than the typical user. They call this the “capability overhang:” the widening gap between what AI can do and what most people, companies, and countries are actually doing with it. To me, this is the real divide that matters. The future won’t split neatly between people who have AI and people who don’t. It will split between people who let AI do their thinking and people who use AI to elevate how they think, create, decide, and build.

Here is an invitation for leadership to swoop in…

The real job of leaders right now is to lower the temperature and raise the standard. It is to replace vague hype with a believable path. It is to invest in skills, redesign work, create new on-ramps for early-career talent, and show people how AI can make them more capable, more creative, and more valuable, not more disposable. AI automation is one part, but that’s just a cost center story…and it’s an AI status quo story. But AI augmentation + automation unlocks new value, creating a story of cost takeout and human + AI value creation.

People don’t just want to better understand the future, they want to understand their part in it.

If leaders cannot answer that with clarity, conviction, and humanity, the backlash we’re starting to see now will look basic in hindsight.

The winners in this era will be the ones who make the future feel believable and aspirational. They will build systems where AI expands human potential instead of shrinking human possibility. They will replace fear with fluency, hype with judgment, and inevitability with intention.

If you are a CEO, your job is to explain what AI means for your people, how work will change, how learning will evolve, what new roles will emerge, and why human judgment will matter even more than roles void of human thinking, creativity, and imagination. Show them the roadmap. Show them the reskilling path. Show them how AI can make them more capable, more creative, and more valuable, not more disposable.

That’s leadership.

The future of AI is not something that just happens to us. The future of AI happens because of us.

That is the advantage.

Not automation. Augmentation.

Not doomsday prophecy. Vision and purpose.

Not smarter machines alone. A smarter, more human future because with AI.

The future of AI does not need more prophets. The future needs leaders people can trust.

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The most important AI shift in business right now is not happening in the tech stack. It’s happening in the mindset of leadership. As executives lean more heavily on AI to guide decisions, a bigger question is coming into focus: is AI expanding judgment, or slowly replacing it?

A March 2026 report highlighted by The Register, citing Confluent’s survey of 200 UK private-sector leaders, found that 62% of leaders use AI to make the majority of their decisions. Not some decisions. The majority. And it gets more unsettling from there: 70% say they second-guess themselves when AI disagrees, 46% say they rely on AI more than colleagues, and 65% say decision-making has become less collaborative since adopting AI.

AI has become the new executive influencer.

AI is no longer simply supporting judgment. In too many cases, it is starting to replace the friction that makes judgment valuable in the first place: debate, collaboration, instinct, experience, context, accountability. And that should set off alarms in every boardroom.

The danger in this next phase of AI is the danger is that leaders will stop noticing when they’ve deferred to it, instead relying on it in the same way they do with earned, trusted advisors.

Pattern Recognition

Why is this happening now?

Speed has become its own form of pressure. Confluent found that 92% of respondents say the speed of decision-making has increased over the last three years, and 82% say they are forced to choose between making a fast decision and making an informed one.

In this case, WTF, doesn’t stand for “what’s the future.”

When decision velocity becomes the operating norm, AI starts to feel like an always-on prophet, but can perform more like a false prophet. It offers the illusion of certainty at machine speed. It gives executives something that looks like objectivity, sounds like confidence, but fast is not the same as wise. Nor is it a definitive pillar of leadership, yet, definitely not in the same way strategic advisors and board members have earned their positions.

Imagine Walt Disney or Steve Jobs leaning on AI to validate their ideas and acquiescing if it disagreed!?

On that end, we’re now seeing the same pattern elsewhere. SAP’s 2025 research found that 44% of U.S. C-suite executives would reverse a decision they were already planning to make based on AI input, 38% would trust AI to make business decisions on their behalf, and 74% place more confidence in AI advice than in family and friends. That last number is more than a WTF reaction. It is telling. What it tells us is that is being operationalized and normalized as a trusted advisor, without earning that position…yet.

At the same time, the performance gap is widening between companies that are merely using AI and those that are actually transforming through it. PwC’s April 2026 AI Performance Study found that 74% of AI’s economic value is being captured by just 20% of companies. In other words, most organizations are running to be part of the AI rush, but only a minority are converting activity into measurable advantage. And even less are converting investment into ROI.

That should reframe the conversation for every CEO and board.

The issue is no longer adoption or fluency.

The issue is whether you are building an AI-forward company or just layering AI over yesterday’s leadership model. If AI is being used to accelerate the same old decision structures, the same old incentives, the same old org chart logic, then you are simply digitizing managerial habit. And habit does not win the future.

As the old saying goes, you’re not measured by your intentions or goals, you’re measured by actions and habits.

The Leadership Mandate

Leadership has to grow up.

McKinsey found that 92% of companies plan to increase AI investment over the next three years, yet only 1% describe themselves as mature in deployment. It’s WTFAOA (the last part means all over again). It tells us that capital and trust are moving faster than operating models, and tools are spreading faster than leadership readiness.

So the mandate now is not to ask, “How do we use AI more?”

It is to ask better questions.

Where should AI inform decisions, and where must humans remain unmistakably accountable?

What decisions can be accelerated, and which ones require tension, interpretation, and lived experience?

How do we redesign collaboration so AI enhances collective intelligence instead of quietly eroding it?

What governance, data discipline, and leadership behaviors must exist before AI is allowed to shape strategic direction at scale?

These are not technical questions. They are proper leadership questions.

The companies pulling ahead are aiming AI at growth, reinvention, and operating redesign, while building the governance and trust required to scale it responsibly. That is exactly what PwC found separates the leaders from the laggards.

If you’ve been to any of my keynotes, you’ll hear me say that AI is a test of leadership.

Leaders need to learn how to think better in an environment where machine confidence can easily masquerade as strategic clarity.

The winners in this next era will not be the organizations that automate decisions the fastest.

They will be the luminaries that know how to scale intelligence without surrendering judgment.

That is the work now. That is the mandate.

—

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When major CEOs begin framing succession around AI, it signals something larger than a changing of the guard. It suggests that the leadership model itself is being redefined in real time. This is not about becoming more efficient with new tools. It is about whether today’s leaders can redesign work, decision-making, and value creation for a world shaped by intelligence at scale.

Recently, two iconic CEOs announced that they’re stepping aside and called for a new genre of leader in an era of AI. Coca-Cola’s James Quincey said the company now needs “someone with the energy to pursue a completely new transformation of the enterprise.” Former Walmart CEO Doug McMillon was just as direct: “I could start this next big set of transformations with AI, but I couldn’t finish it.” Coca-Cola’s board named Henrique Braun CEO effective March 31, 2026, and Walmart’s board named John Furner CEO effective February 1, 2026.

These were not struggling CEOs being pushed out by poor performance. These were accomplished operators, each with a record of real transformation behind them, looking at what comes next and recognizing that AI requires total business reinvention. It is a mindshift, a change in the physics of leadership itself. When leaders of that caliber start framing succession around AI-readiness, the conversation stops being about technology adoption and shifts to how a legacy enterprise, and the people steering it, can sustain momentum while also driving reinvention.

Pattern Recognition

This isn’t for everyone.

Most CEOs will approach AI an investment in efficiency and scale, pushing for ROI out of the gate. Faster service. Cost takeout. More automation and optimization. Fewer handoffs. Better dashboards.

All of that has value.

But on its own, that mindset locks AI inside yesterday’s operating model. It treats intelligence as a productivity layer instead of a reinvention layer.

And that is precisely where the returns begin to flatten.

PwC’s 2026 Global CEO Survey found that only 12% of CEOs say AI has delivered both cost and revenue benefits, while 56% say they have seen no significant financial benefit so far. PwC also found that nearly 74% of AI’s economic value is being captured by just 20% of

companies. What separates that top tier is not simply more experimentation. The leaders are more likely to use AI to pursue growth, reinvent business models, redesign workflows, and increase decisions made without human intervention while strengthening governance and trust.

Legacy companies are applying AI to tasks.

AI reinventors are redesigning systems.

The Reinvention Gap

McKinsey’s latest research found that companies realizing the most value from AI do not aim only at efficiency; they pair efficiency with growth and innovation, and they redesign workflows to get there. In a separate April 2026 discussion on the “agentic organization,” McKinsey argued that the real challenge is not the technology but redesigning workflows, leadership, and culture for an agentic world, adding that 75% of roles need fundamental reshaping now.

That is why this moment asks something new of CEOs. You are either scaling yesterday or you’re optimizing the best parts of yesterday while reimagining your business for tomorrow.

The CEO can no longer be the executive sponsor of AI. That was yesterday’s job. The CEO now has to become the architect of an AI-forward company. That means setting ambition beyond efficiency, identifying where intelligence can create new value, deciding where autonomy belongs and where human judgment must remain decisive, and aligning the operating model so AI does not sit in disconnected pilots across functions.

McKinsey’s research on business leadership in AI transformation makes the point…no amount of upskilling will overcome an ineffective operating model, and incentives and performance systems have to align to the transformation roadmap.

In other words, leadership in the AI era is not about using AI to run the same machine.

It is about redesigning the machine with AI to do what wasn’t possible yesterday.

What CEOs Need Now

For CEOs, AI business reinvention now requires five things.

First, a new vision and ambition. AI cannot be confined to incremental productivity gains. The mandate is growth, reinvention, and new value creation. The companies pulling ahead are the ones using AI to reshape business models and pursue opportunities beyond their traditional category boundaries.

Second, workflow redesign, not task automation. We’re talking about real end-to-end reimagination of how work moves, where decisions happen, and how humans and agents coordinate. McKinsey found that value emerges when entire workflows are reimagined, not when a single task is done marginally better and faster.

Third, governed autonomy. The best AI performers are increasing decisions made without human intervention, but they are doing it with stronger governance, responsible AI frameworks, and higher employee trust.

Fourth, leadership redesign. Senior leaders need new muscles: judgment, creativity, aspiration, resilience, and the ability to work in teams that increasingly include both humans and AI agents. Those traits are becoming more valuable.

Fifth, workforce reinvention. McKinsey estimates that by 2030, about $2.9 trillion of economic value could be unlocked in the United States if organizations prepare their people and redesign workflows around people, agents, and robots working together…not around cost takeout, but around new forms of coordinated capability.

The Boardroom Mandate

That has profound implications for the board.

Boards have already started paying more attention. NACD reports that more than 62% of directors now set aside agenda time for full-board AI discussions. But the headline directors should sit with longer is the point of this article.

CEOs may not be up to the task of AI business reinvention.

It takes vision, strategy, and backing.

NACD’s guidance is explicit that boards need a shared understanding of AI’s strategic relevance, clear board and committee roles, and real scrutiny around strategy, capital allocation, and risk.

Boards need to stop asking, “what is the ROI of AI?” and start asking, “Where are we redesigning the business because intelligence has changed the economics of value creation?”

They need to stop applauding pilots simply cutting costs and start interrogating whether those pilots are connected to workflow redesign, decision rights, governance, and new revenue creation.

They need to stop evaluating succession through a legacy lens of operating excellence alone and start asking whether the next generation of leaders can run an enterprise where humans and intelligent agents work together across end-to-end workflows.

The New Leadership Brief

The leadership brief is changing.

For boards, the mandate is just as clear.

AI-native leadership is the new mandate.

Capital allocation and support has to move from pilot budgets to persistent investment in transformation.

Oversight has to cover trust, governance, and decision rights, not just experimentation.

And performance reviews need to ask whether management is creating learning velocity, redesigning work, and generating new forms of enterprise value, not merely reporting efficiency wins.

You cannot use AI to do what you have always done and expect to outperform companies that are using AI to become what you have not yet imagined.

The Message

The real lesson in these CEO transitions is the mind shift.

Quincey and McMillon did not merely hand over the reins. They acknowledged, in their own words, that the terrain ahead is different enough to require a different kind of leadership energy, pace, and horizon. Boards should hear that clearly. So should every CEO still treating AI like a better engine inside the same plane they’re trying to modernize while in flight.

The companies that win this next era will not be the ones that automate the past most efficiently. Nope. They will be the ones that redesign the future first…starting today.

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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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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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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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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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