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

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

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

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

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

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

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

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

The fork in the road most teams ignore

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

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

Iteration: improving yesterday to scale tomorrow.

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

Innovation: creating new value to unlock new opportunities tomorrow.

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

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

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

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

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

Why people resist change

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

Leaders love to talk about vision. Teams hear disruption.

Leaders love to talk about efficiency. Employees feel replaceability.

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

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

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

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

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

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

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

Because transformation doesn’t spread through logic alone.

It spreads through meaning.

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

Richie asked the question that is painfully accurate:

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

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

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

And then you wonder why you can’t innovate.

Innovation requires oxygen. Your calendar is often a vacuum.

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

The most powerful word in transformation is “no.”

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

No to the meeting that doesn’t need you.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The hidden tax of AI: workslop

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

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

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

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

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

A practical mindshift sprint you can run this week

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

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

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

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

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

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

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

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

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

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

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

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

If you take nothing else from this, take this:

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

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

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

Photo: Baker Hughes

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

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

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

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

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

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

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

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

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

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

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

Leadership moment

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

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

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

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

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Florence has a way of putting ambition in perspective. You’re surrounded by reminders that the future is something people choose to design, even when the tools, materials, and models of the day feel limiting. That’s what I kept thinking about as I prepared for the Baker Hughes Annual Meeting conference in January 2026. I’m proud to share that they 10-minute keynote address is now online and I’ve included it below

This is not Just a Future of Work Moment, It’s a ReinAIssance of Work Event

At the event, we explored a new era of “intelligent industry.” But I share this with you because this moment is bigger than any one sector. “Intelligence” is no longer confined to IT or executive dashboards. It’s moving into workflows, operations, supply chains, customer experiences, factories, field service, finance, healthcare, public services…everywhere outcomes are created. And, with intelligence, those outcomes can be automated and reimagined to deliver greater value not possible before generative AI and AI agents.

In an era of AI darwinism, an organization won’t be judged by how much AI it uses. It will be remembered by how intelligently it applies it.

AI Darwinism, the survival-of-the-most-adaptive era of business.AI Darwinism applies new selection pressure in business, created when intelligence accelerates the pace of change so dramatically that advantage shifts from size and speed to learning velocity and reinvention.

Baker Hughes describes the stakes through what it calls the “energy equation,” energy that is sustainable, efficient, and affordable. In the AI era, that equation becomes a board-level design imperative and constraint. Why? Because every company is suddenly in the energy business. In fact, energy consumption is expected to double by 2035. And as a result, every company must now build an operating model that runs on sustainable, efficient, and affordable energy: data centers, edge devices, smarter facilities, intelligent fleets, industrial automation, always-on agents, and the growing expectations of customers and employees for instant, high-quality outcomes.

In Florence, we heard projections that energy demand is rising fast, and that data center consumption is rising even faster. For example, data centers, largely driven by AI processing, are expected to double by 2030. Whether you lead in retail, banking, healthcare, manufacturing, logistics, or software, the implication is the same: scale begins now, and scale has a cost you can’t ignore.

But the biggest shift isn’t AI adoption or deciding which AI tools are best…though both are important. It’s that we finally have the chance, and parallel pressure, to redesign work itself. This work of reinventing work in this renAIssance, becomes the moat.

For the last couple of decades during the “digital transformation” years, many organizations didn’t transform so much as they digitized yesterday’s work. We layered technology on top of legacy workflows and called it progress. The risk today is doing the same thing with AI: automating old work instead of creating new capacity, new capabilities, and new value.

That’s why I framed this as a CTRL-ALT-DEL moment.

We have a choice in how we choose to reboot. A reboot doesn’t mean replacing people. And a reboot doesn’t have to mean that we come back online as a more efficient, leaner version of who we are and what we did yesterday. It means re-architecting the relationship between humans, AI agents, and physical AI so that:

we design work and how work flows (data too) across the organization to unlock new outcomes and value,agents can execute and coordinate tasks continuously,physical AI can extend capability into environments where humans shouldn’t (or can’t) go,and people move above the loop, setting intent, defining rules, supervising autonomy, and focusing on judgment, creativity, ethics, empathy, and innovation.

Someone has to define the boundaries. Someone has to decide what “good” looks like. Someone has to separate human work from automated work, break roles into tasks, and rebuild those tasks into an operating model that can learn and improve. That “someone” is leadership. That’s the mindshift!

So if you take one thing from this keynote, let it be this:

AI is not your strategy. Transformation toward greater resilience, relevance, and value creation is the strategy. And transformation demands that we upgrade technology and upgrade ourselves. To transform and innovate, we must be willing ti disrupt ourselves. It is the gift you give to yourself before someone else does.

If you want the full thread, Florence, the energy equation, AI agents, physical AI, and why this is ultimately a human leadership moment, please watch the embedded YouTube video of the talk.

Remember, nothing interesting begins with knowing.

You can hit CTRL-ALT-DEL and reboot as a faster more efficient version of your business, now powered by AI. But in doing so, you do what everyone else is doing, while also becoming part of the AI status quo.

So the question is, how do you want to reboot your organization of the AI renAIssance?

One more thing…

While in Florence, I spent time with the Baker Hughes team. We recorded two short interviews that I’d like to share with you.

In this first clip, I zoom in on two non-negotiables for scaling AI without blowing up the future you’re trying to build: 1) governance and 2) humanity.

Governance is so much more than policy. It’s the trust operating system, the security, compliance, and guardrails that keep people and AI moving fast without breaking what can’t be repaired.

And humanity is the point: if we scale AI by shrinking human capacity, we don’t modernize the value engine. In fact, we sabotage it.

The north-star should be net new value creation. If you don’t define new value upfront, you’ll default to automation, efficiency, and cost cutting, and call it progress. And it is still progress, but it’s linear. What we’re really talking about is AI automation AND augmentation to drive exponential growth!

New leadership is what makes that outcome intentional.

Interview 2

In this follow-on clip, I unpack why physical AI (and world models) is the next major leap. It introduces an entirely new kind of workforce: intelligent devices and humanoids working alongside people. The move from pilot to production won’t be won by experimentation alone; it starts with vision. If you’re still struggling to define your AI vision, physical AI raises the stakes because now intelligence shows up in plants, fields, facilities, and real-world operations.

What makes this moment different is that physical AI is evolving beyond being trained only on language and multimodal data. Emerging world models can train systems on real-world scenarios, making pilots more precise, predictive, and safer by letting you design environments where physical AI can operate in known, tested conditions.

Do not use unimaginatively. Imagine with AI to answer the only strategic question that matters…what can we do tomorrow that we couldn’t do yesterday to create net new value?

This is the real opportunity of intelligent industry: augmentation…humans, agents, and physical AI performing together in ways that create exponential value.

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by Chris Killian, Home Care Innovation Forum

When Brian Solis, Chief of Global Innovation at ServiceNow, futurist, and bestselling author, took the stage at the Home Care Innovation Forum, he began his talk by delivering a sobering quote from legendary Silicon Valley investor Vinod Khosla.

“Most businesses have no clue what is about to hit them in the next 10 years when most rules of engagement change,” Khosla said. Drawing from his decades of studying technological revolutions and writing over 60 research reports on their impacts, Solis argued that artificial intelligence represents “probably the most disruptive technology we’ve seen in history.”

Yet most companies are approaching it with the same mindset that has limited success during digital transformation. At-home care, an industry notoriously reticent to integrate technology, can learn a lot from Solis’s expertise and foresight.

The Great AI Maturity Regression

ServiceNow’s latest AI Index revealed a startling trend: companies’ AI maturity scores actually decreased from 44 out of 100 last year to 35 this year, despite increased investment and attention around AI technologies.

The reason? Companies are treating AI like previous technology waves, using it to do yesterday’s work better, faster, and cheaper rather than tapping into its real potential going forward.

“We adapted yesterday’s processes, digitized yesterday’s systems and workflows in the name of modernization,” Solis said, “but all we were really doing was modernizing with technology, not reimagining work.”

The Iteration vs. Innovation Trap

Solis distinguished between two fundamental approaches: iteration (doing what we did yesterday better) vs. innovation (doing what we didn’t do yesterday to create new value). Most companies default to iteration because “corporations are designed not to innovate—they’re designed to scale efficiencies.”

This creates what he calls the “AI status quo” — implementing chatbots built on yesterday’s processes, automating existing workflows, and measuring success by traditional productivity metrics. Meanwhile, truly AI-native companies are “reimagining their org charts with AI in their org charts.”
“If you’re waiting for someone to tell you what to do, you’re on the wrong side of innovation,” he warned.

Solis cited Shopify’s controversial memo mandating that no human hires could be made “until you can demonstrate why artificial intelligence cannot do the job,” illustrating how some companies are fundamentally rethinking their operating models.

The Leadership Perception Gap

Perhaps most concerning is the disconnect between executives and employees regarding AI strategy. According to Solis’s research:

47% of employees feel their company’s AI approach is well-controlled and strategic (less than half)73% of executives feel the oppositeHalf of employees think their company has an AI strategy; 90% of executives believe they do

This mirrors the failed patterns of digital transformation, where perception gaps prevented meaningful change. Even more striking: CEOs privately admit they believe AI agents could provide better counsel than their boards, and 90% think AI can develop strategies equal to or better than their executive teams.

From Chatbots to Digital Employees

The progression from AI tools to AI agents to what Solis calls “digital employees” represents a fundamental shift in how work gets organized. Unlike simple automation, these systems can perform complex, cross-functional workflows spanning multiple departments—something that requires breaking down organizational silos.

“An agent has to be essentially hired the way you’d hire an employee,” Solis explained. “They have to be trained, onboarded, managed for performance, and eventually offboarded when they’re replaced.”

Only 30% of companies are thinking cross-functionally about AI implementation, yet this approach delivers the biggest organizational impact and fastest ROI. The challenge isn’t technical, it’s organizational. “The hardest part isn’t the implementation or vendor selection,” Solis noted. “The hardest part is having conversations with people you don’t normally work with to create interest and buy-in.”

Real-World Transformation: The IKEA Example

Solis shared how IKEA’s AI chatbot successfully resolved 57% of customer inquiries, leading to typical discussions about reducing headcount. But instead of stopping there, IKEA asked different questions: What were those calls about? What about the 43% that couldn’t be resolved?

They discovered the unsolvable calls were interior design consultations. Rather than just cutting costs, IKEA reskilled their customer service agents as interior designers, creating a new business service that generated €1 billion in its first year.

“Save money over here, generate exponential growth over there,” Solis said. “It’s a different way of thinking.”

Perhaps most importantly, Solis argued that AI transformation can’t be delegated to IT departments or innovation committees alone. “If there ever was a time for leaders to come from anywhere within the organization, it’s now,” he said.

The choice facing every organization is clear: continue using AI to optimize yesterday’s business model, or use it to reimagine what business could become. As Solis put it, “Do you want to wait for disruption to happen, or do you want to be part of it happening?”

Watch his full talk below 

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The post Beyond the AI Revolution: Why Many Companies Live in the Past [Keynote] appeared first on Brian Solis.

The AI community is buzzing around an essay published by Matt Shumer , “Something big is happening.” In it, Shumer describes a moment when AI capabilities are far outpacing society. He argues that most individuals aren’t keeping up with the speed of the change, comparing this point in time to the “this seems overblown” phase before COVID-closures and impacts hit worldwide. His takeaway is that disruption “is happening right now,” even if it sounds crazy.

Shumer’s point is that we’re on the cusp where AI stops behaving like a tool you supervise step-by-step and starts behaving like a system that can take initiative, test its own work, iterate, and ship. His central claim is that the “this seems overblown” phase is ending, fast. In his view, we’ve hit an inflection point where the newest AI systems have shifted from “assistants” to semi-autonomous workers that can take a goal in plain English, build a complete product, test it, and iterate with minimal human involvement. The next step, is fully autonomous AI workers.

And the essay’s most viral line is the one that should bother every executive, board member: “GPT-5.3-Codex is our first model that was instrumental in creating itself.”

Said another way, AI is building the next AI.

That sentence is a headline in of itself.

So here’s a question: Who inside your organization is building the next version of your company?

AI is not only accelerating AI development, it is accelerating advantage and competitive moats. It is compressing learning cycles. It is rewarding the people and companies who can translate capability into outcomes. And it is leaving everyone else with an expensive set of pilots, under-utilized AI model licenses, a stack of policies, and a growing sense of unease they cannot quite name.

That unease is starting to take shape now. I call it AI Darwinism, the survival-of-the-most-adaptive era of business. AI Darwinism applies new selection pressure in business, created when intelligence accelerates the pace of change so dramatically that advantage shifts from size and speed to learning velocity and reinvention. This is true for organizations and people. And, it rewards organizations that evolve their operating model by combining human judgment and creativity with machine agency, redesigning work to move faster in learning, not just faster in execution.

AI Darwinism is the survival-of-the-most-adaptive era of business. It applies new selection pressure in business, created when intelligence accelerates the pace of change so dramatically that advantage shifts from size and speed to learning velocity and reinvention. And, it rewards organizations that evolve their operating model by combining human judgment and creativity with machine agency, redesigning work to move faster in learning, not just faster in execution.

I’ve seen this before. In the early 2000s, I studied the rise of digital Darwinism, which explored how businesses were and weren’t navigating the digital revolution. Now, it’s happening all over again, this time at potentially exponential scales.

NY Times Tech Writer and co-host of the Hard Fork podcastKevin Roose captured the concept of AI Darwinism in a recent post: “I [sic] follow AI adoption pretty closely, and i have never seen such a yawning inside/outside gap.” He continued, “people in SF are putting multi-agent claudeswarms in charge of their lives, consulting chatbots before every decision, wireheading to a degree only sci-fi writers dared to imagine.  people elsewhere are still trying to get approval to use Copilot in Teams, if they’re using AI at all.”

I’ve been warning about for three years, even when the audience wanted GenAI news and demos and prompt tutorials instead: the disruption is not AI. The disruption is the divide it creates and the chasm it expands.

Vinod Khosla’s AI divide, translated for leaders

Vinod Khosla issued an ominous warning in 2024. “I am awe struck at the rate of progress of AI on all fronts,” he observed.

“Today’s expectations of capability a year from now will look silly and yet most businesses have no clue what is about to hit them in the next ten years when most rules of engagement will change.”

He continued, “It’s time to rethink/transform every business in the next decade.”

Vinod Khosla keeps returning to ablunt idea: AI will drive massive productivity and abundance, and it will also create violent discontinuities for jobs and incumbents. He’s explicit about both the upside and the turbulence.

Here’s the part executives should underline, stick on the wall, and charge leadership teams to explore opportunities with urgency. Khosla describes a near-term corporate glow-up: “increasing productivity, reducing costs… increasing abundance generally.”  Then he warns that the same flywheel can trigger sweeping displacement.

That’s the AI divide in one frame: those who turn capability into compounding advantage, and those who wait for permission until the market moves without them.

The data says the capability gap is already real

Shumer uses lived experience. Research shows the specifics.

We keep talking about AI adoption as if the story is access. OpenAI’s latest research, “Ending the Capability Overhang“, argues the real story is underuse: a growing “capability overhang,” the gap between what frontier AI can do and the value most people, businesses, and countries are actually capturing at scale.

The most important takeaway from OpenAI’s research is not that models are getting smarter. We already know that. The headline is that capability is compounding faster than society and businesses are absorbing it, and that gap is becoming the new competitive divide. In OpenAI’s framing, a “capability overhang” is the widening distance between what frontier AI can do and how fully those capabilities are being adopted and integrated into real work.

In other words, the divide is not who has the most AI. It is who has the agency to apply it deeply, across real work, and to reimagine work, day after day.

The winners will be the organizations, and the people, that know how to use AI deeply, repeatedly, and responsibly. OpenAI shows this overhang is already massive, even among people with the same access.

The “typical power user” relies on about 7x more advanced “thinking capabilities” than the typical user.

That represents and AI maturity gap. It means some people are using AI for complex, multi-step work and higher-value outcomes, while others are still treating it like a search box with better manners.

Just Google “Clawdbot” to see all of the wild examples of people deploying AI agents to run their life!

Shumer’s piece describes lived experience on the inside of the curve. OpenAI is describing what happens when that curve meets the real world: capability expands, but value creation concentrates in the hands of people and organizations who know how to work with it. This is why the “AI is building the next AI” line matters. It matters because when intelligence compounds, the advantage goes to those who compound learning.

The AI gap is not a future problem. It is a present competitive condition.

What we’re missing…

Shumer is right about velocity. He’s right that public perception is lagging. He’s right that people who tried AI two years ago are evaluating a fossil record.

But the essay mostly frames the moment as a personal wake-up call about labor displacement and individual adaptation. That is important. We also need to discuss leadership, not inspirational leadership, but operational leadership.

The real constraint is not model capability. Research shows that. It is vision, AI fluency, and managerial capability.

OpenAI’s “overhang” research essentially says this out loud, adoption alone is not enough. Effective use is the differentiator.

Roose is describing the cultural version of that.

Khosla is describing the macroeconomic version of that.

AI does not replace leaders. It replaces leaders who cannot see what is happening.

If your people are “behind,” it is rarely a talent problem. It is usually a permission or empowerment problem. Leadership needs a mindshift.

This is where expert leaders matter. They can recognize the moment, tell the truth about it, and re-architect the company around it.

 

Using a compass to reframe the future of work and labor

Yes, AI is now building the next AI. The hopeful implication is not “we’re doomed.” The hopeful implication is that we can finally move from automation projects to reinvention programs.

If you are a CEO, CIO, COO, CHRO, CDO, or on a board, the possibility is not “AI makes employees faster.” It is:

Faster cycle time from insight to decision to actionBetter service quality at scaleMore resilient operationsNew products that were previously uneconomicA workforce that spends less time on repetition and more time on learning, scaling, judgment, creativity, relationship-building, and domain imagination

But possibility does not deploy itself.

What business and technology leaders need to do now

You want your teams to feel the’re growing, not losing ground to AI. Give them a leadership upgrade, not just the tools.

1) Name the shift inside your company

If you want your teams to feel like they’re growing, not losing ground to AI, do not hand them a tool and call it transformation. Start a movementThis moment is not about adopting AI, it is about ending the capability overhang inside your company. It is about closing the gap between what is now possible and what your people are empowered to deliver. It is about turning fear into fluency, confusion into cadence, and experimentation into an operating model.

This is the leadership work. This is the bridge.

The Overhang Movement is a leadership pledge.

1) Declare the shift, out loud, in plain language.

Most organizations are stuck because nobody has named what is happening. So, name it.

We are moving from AI that answers to AI that acts, from copilots to agents, from productivity hacks to workflow reinvention, from “Can we use it?” to “What outcomes can we now deliver that we could not deliver before?”

If you cannot name the shift, and people can’t articulate it, you cannot lead it.

2) Make AI fluency a leadership standard, not an employee experiment

This is where movements win or die.

If executives outsource understanding, the organization will confuse activity for progress. Leaders must be first to practice what they expect, not performative prompting. Real use. Real workflows. Real decisions.

Set a new expectation. Every leader must run one meaningful workflow with AI weekly and share what they learned. Make curiosity measurable. Make learning contagious.

The future will not be led by the most confident leaders. It will be led by the most committed learners.

3) Redesign work around outcomes, not tasks

Tools speed up tasks while movements redesign reality.

Pick one journey your customers can feel, one process your employees resent, one area where delay costs real money and trust. Then rebuild it end to end with humans and machines in the loop.

Do not optimize the old. Replace the old.

AI ROI is not saved minutes. It is saved months, which then allow for new value exploration and creation.

4) Turn governance into an accelerator

Give people permission with boundaries. Create the policy envelope, the audit trail, the evaluation discipline, the accountability. Make governance operational and empowering.

5) Build a learning loop that compounds

Movements run on rhythm. Create a weekly cadence that replaces status updates with learning reviews. What worked. What broke. What was shipped. What we learned. What we will change.

Then scale by reuse. Lift the pattern, not just the tool. Reuse agents, reuse guardrails, reuse telemetry.

If you do not build a learning loop, you are not transforming.

6) Make the divide visible, then make it unacceptable

The overhang thrives in silence, so measure depth and outcomes:

How many workflows are being redesigned end to end?How many teams are using AI for multi-step work, not single answers?How fast do insights become decisions, and decisions become actions?Where are people blocked by fear, policy ambiguity, or lack of training?

 

What you do not measure becomes your blind spot. What you normalize becomes your culture. You’re building a culture of learning and unlearning, and the safety and permission to ask questions and explore.

7) Lead with hope, backed by competence

People are afraid of being left behind or replaced by AI. Your job is to swap anxiety with agency. Show them the path. Train them. Equip them. Protect them. Celebrate progress. Make reinvention something people join, not something that happens to them.

Hope is more than a feeling. Hope is a plan people can follow.

AI Darwinism is here, but so is your choice

Kevin Roose is right to call out the “inside/outside gap” because it’s now visible, cultural, and compounding.

Shumer is living the agency shift inside an AI Native org and sees the divide from the front lines.

Vinod Khosla is right to hold two truths at once. AI can unleash productivity and abundance, and it can also widen inequality and displace work at a pace leaders are not prepared to manage.

And OpenAI is right to quantify the capability overhang, the widening distance between what these systems can do and what most people and organizations are actually doing with them.

AI Darwinism is not about the strongest companies winning. It’s about the most adaptive.

In nature, survival does not go to the biggest or the most experienced. It goes to the ones that learn faster, evolve sooner, and change their behavior before the environment forces them to. That is the AI era in one sentence.

AI is rewriting the conditions of competition in real time.

This is where “AI is building the next AI” becomes a leadership mirror.

If intelligence is now compounding itself, then the pace of disruption will not be linear. It will be exponential.

The question is whether your organization will be better because you learned how to build with AI as it evolves, govern it, and redesign work around it.

Here’s the leadership pledge I want you to take into your next executive meeting:

We will not treat AI as a tool. We will treat it as a new operating model.We will not run pilots forever. We will redesign workflows end-to-end.We will not ban curiosity. We will build safe systems for it.We will not measure AI by output volume. We will measure it by outcomes and learning velocity.We will not let the AI divide become a talent crisis. We will lead the transition.

In the age of AI Darwinism, your greatest competitive advantage is the organization you become with AI.

The future won’t reward the companies thatadopt AI.

It will reward the ones that adapt because of it.

And this is where the hopeful story lives.

The opportunity for leaders is to turn that into hope at scale.

Mind the gap. Close the gap.

Read Mindshift | Book Brian as a Speaker | Subscribe to Brian’s Newsletter

 

 

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In 2012, during his time as partner and principal analyst at Altimeter, Brian Solis coined Generation-C (Gen-C), a psychographic profile of ‘connected’ customers who align by digital behaviors vs. age groups. His books, research, and keynote presentations helped leaders understand how to reimagine brand, marketing, customer experiences, employee experiences, and product innovation around this incredibly influential generation.

His work was recently revisited in the article, “Gen C: Understanding the Connected Generation and Their Digital Impact.” Generation-C is more important today than ever.

In today’s hyper-connected world, the term “Gen C” has surfaced with more complexity than traditional generational labels. It doesn’t neatly align with birth years like Millennials or Gen Z. Instead, it captures a mindset—of people constantly plugged into digital life, shaping culture through connection and content. Unpacking Gen C isn’t just about demographics; it’s about behaviors, influences, and expectations driven by near-constant connectivity.

What Defines Gen C? A Psychographic Profile, Not an Age Bracket

Traditional generational segments—Baby Boomers, Gen X, Millennials—rely on birth year ranges. Gen C breaks that mold. Coined by digital analysts such as Brian Solis and embraced by marketing thinkers like Forbes and Nielsen, Gen C refers to a psychographic group defined by lifestyle and attitude. It’s not about age, but being always-on, digitally savvy, and behavior-driven .

This means someone in their teens and someone in their thirties may both be Gen C if they share this digital-first outlook. That mindset includes a reliance on social media, multitasking among devices, and a proclivity for real-time content creation and consumption .

Overview

Gen C represents not an age group, but a digitally native mindset—fluid, influential, and always connected. Their behavior rewrites expectations: they value authenticity, prioritize seamless experiences, and transform passive consumption into active participation. Brands, institutions, and media must pivot from demographic assumptions and embrace this connected generation’s demand for personalization, community, and immediacy.

Facing them with outdated digital strategies is a misstep. Instead, organizations can engage by listening, co-creating, and delivering value that feels personal and trusted. That’s where true influence lies.

FAQsWhat exactly is Gen C?

Gen C stands for the “connected generation”—a mindset-defined group of individuals who are always online, digitally savvy, and driven by behavior rather than birth cohorts. It’s characterized by constant connectivity and content creation.

How does Gen C differ from Millennials or Gen Z?

Unlike Millennials or Gen Z, Gen C isn’t tied to specific birth years. It spans across traditional generations, focusing on digital habits and attitudes—how people connect, create, and consume content.

Why should marketers focus on Gen C?

Gen C holds disproportionate influence in digital spheres—watching videos, engaging on social platforms, and shaping trends. Understanding this group helps marketers craft more authentic, personalized, and community-driven strategies.

What do Gen C consumers expect from brands?

They look for mobile-first, convenient interactions, transparency, and personalization. Brands that facilitate community involvement and build trust resonate more effectively.

Can Gen C be found in different age groups?

Absolutely. Anyone exhibiting an always-connected mindset—creating content, curating their digital presence, engaging socially—could be considered part of Gen C, regardless of age.

How can wider society adapt to Gen C’s influence?

By recognizing their power as connectors and content creators. Institutions should prioritize digital-first strategies, social collaboration, and voice that aligns with authenticity and experience.

 

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Global Gurus has named Brian Solis as a top futurist for 2026. The award also celebrates Brian’s book, “Mindshift: Transform Leadership, Drive Innovation, and Reshape the Future.” The book helps leaders become everyday futurists to anticipate and shape the future vs. reacting to it.

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Thank you, Brendan McGurgan. What a privilege to spend time with you.

What if the greatest challenge facing leaders today isn’t technology itself — but how we think about it?

In this episode of ScaleX Insider, Brendan McGurgan is joined by world-renowned digital anthropologist, futurist, and author Brian Solis for a powerful conversation on mindset, leadership, and navigating growth in an era defined by AI, acceleration, and constant disruption.

Brian shares why meaningful scale begins not with tools or tactics, but with how leaders think, perceive, and make decisions. Drawing from his bestselling books Life Scale and Mind Shift, he explains how founders and executives can move from reaction to intention, from overwhelm to clarity, and from noise to meaningful progress.

This episode explores how leaders can develop the mental frameworks required to grow sustainably, lead with confidence, and embrace innovation without losing focus or purpose.

Episode Highlights

Scaling Begins with Mindset
Why true scale starts with how leaders think, not how fast they grow.

Life Scale vs Life Speed
How constant motion creates the illusion of progress — and why slowing down leads to better outcomes.

The Cost of Distraction
How algorithms, social media, and constant input quietly derail focus, clarity, and long-term vision.

The Six Stages of Mind Shift

Brian breaks down the framework behind his work:

•Receive – Creating space to observe what matters
•Perceive – Understanding patterns instead of reacting to noise
•Weave – Connecting insights into meaning
•Conceive – Imagining new possibilities
•Believe – Developing conviction and agency
•Become – Acting with clarity and purpose

Signal vs Noise in the Age of AI

Why most leaders feel overwhelmed by AI — and how to identify what actually matters.

AI as an Augmentation Tool

How AI enhances thinking, experimentation, and execution rather than replacing human judgment.

The Power of a Beginner’s Mind

Why curiosity and openness are now essential leadership traits.

From Reaction to Intention

How stepping back enables better decisions, stronger strategy, and sustainable growth.

Key Takeaways

•Scaling begins with mindset, not technology
•Most leaders are reacting instead of responding intentionally
•AI is a multiplier, not a solution
•Focus comes from subtraction, not addition
•Clarity enables better decisions and stronger leadership
•Curiosity is a competitive advantage
•Frameworks turn complexity into clarity

About Brian Solis

Brian Solis is a world-renowned digital anthropologist, futurist, and Head of Global Innovation at ServiceNow.
He advises senior leaders around the world on innovation, digital transformation, and the future of work. Brian has published more than 60 research reports and several bestselling books, including Life Scale and Mind Shift, exploring how technology shapes business, behavior, and society.

Often referred to as “The CEO Whisperer,” Brian works closely with executives across industries to help them rethink leadership, customer experience, and AI-driven transformation.
He is a regular contributor to Forbes, Harvard Business Review, CIO, and Worth, and was named one of the original LinkedIn Top 500 Influencers, alongside Bill Gates, Richard Branson, and Arianna Huffington. His work is followed by over 800,000 people worldwide.

Connect with Brian Solis

Website | LinkedIn | Books | Speaking

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There’s a familiar pattern that plays out in C-Suites and boardrooms in every industry around the world.

Something big happens…an economic shock, a geopolitical event, a climate disaster, a technological leap, like ChatGPT or tomorrow’s big quantum event, and for a moment, everything feels…different. Urgent. Unavoidable. “We need to do something!” becomes the prevailing mantra.

And then, almost quietly, we drift back into business as usual.

Not because leaders don’t care.
Not because they’re not paying attention.
But because the comfort of legacy thinking is powerful…and because it’s rewarded and profitable (until it isn’t)

The problem is, we don’t live in a business-as-usual world anymore.

We live in an era of perpetual disruption.

I had the opportunity to share my thoughts in an article for Chief Executive, “The ‘Mindshift’ CEOs Can’t Ignore.” It’s a lighthouse for leaders who know something’s different, but don’t yet realize how to move differently. Though the rules of leadership are changing faster than most operating models can keep up, the the path forward is hidden by cultures of quarter-to-quarter performance.

But it doesn’t change the reality that we’ve officially crossed into a new business reality: For now, AI isn’t a trend or tool. It’s a new baseline for thinking differently about why and how we work.

And that changes everything…at least it should.

Disruption isn’t the threat. Old thinking is.

What’s happening right now isn’t just “more change.”

It’s compounding change.

Geopolitics collides with supply chains.
Climate events reshape continuity planning.
Markets whip faster than strategy cycles.
And now generative AI accelerates decision-making, reinvents workflows, and rewires expectations at every level of the organization.

This is why so many transformation efforts stall:

Leaders try to solve next-era problems using last-era instincts.

Or said differently…

You can’t run tomorrow’s business with yesterday’s logic.

To quote Mr. Marshall Goldsmith, “what got you here, wont’ get you there.”

That’s where mindshift comes in.

A mindshift is a deliberate change in how you think, lead, and act, so you can intentionally shape change instead of spending your days reacting to it.

Mindshift turns disruption into advantage

Most executives experience disruption as pressure.

Something to defend against.
Something to mitigate.
Something to “get through.”

But the leaders who win next won’t just survive disruption.

They’ll use it.

Because disruption reveals opportunity that stability hides.

A mindshift reframes volatility as a leadership advantage and gives you a practical blueprint for building a company that can do three rare things at once:

stay resilientinnovate consistentlymove forward confidently…even without perfect clarity

That’s the work.

Not just forecasting the future. Designing for it.

The CEO blueprint for a mindshift (especially in an AI-first era)

Mindshift leadership isn’t abstract. It’s practiced. It’s built into how you show up.

Here are the behaviors that separate future-ready leaders from everyone else trying to keep up.

1) Lead with beginner’s mind (even when you’re the expert)

Success has a sneaky side effect: it hardens beliefs.

We start to treat past wins like permanent truths.
We start protecting “how things work here.”
We start optimizing systems, even if they’re holding performance and potential back.

A beginner’s mind is the discipline of keeping an open mind.

What if, instead of optimizing or automating the past, we asked…

“If we started today, how would we design this?”

It sounds like one deceptively simple question. But it’s a a question that challenges strategy and it challenges identity.

And that’s where reinvention begins.

2) Make curiosity a leadership strategy (not a personality trait)

Curiosity is not a soft skill. It’s a critical skill.

In an AI-first world, curiosity is signal detection and imagination.

It’s how leaders catch weak signals before competitors feel them.

It’s how you see patterns before they become pressure or worse, disruption.

It’s how you develop the capacity for awe — the kind of openness that keeps you from becoming rigid at the exact moment you need to evolve.

This is also why CEOs can’t delegate AI literacy.

You can’t lead what you don’t understand.
You can’t shape what you don’t explore.

AI can’t be something you “support.” It has to be something you experience…then you can lead.

3) Build psychological safety on purpose

Innovation doesn’t thrive in fear. It just doesn’t. And iteration isn’t the same as innovation. As the old saying goes, the lightbulb isn’t the result of the continuous improvement of candles. The same is true for streaming. Netflix isn’t the result of the coninuous improvement of VHS tapes or DVDs.

And AI transformation , actual business reinvention, requires breaking free from conventional trajectories. That takes curiosity, experimentation, questions, mistakes, debate, challenge, and learning in public.

People need space to say:

“I don’t know yet.”

“This doesn’t make sense.”

“We’re missing a risk.”

“What if we tried something different?”

Psychological safety a system…part of your culture.

And when you build it intentionally, you get the one thing every organization says it wants and very few can actually create:

truth at speed and scale.

4) Lead the change visibly (or don’t expect anyone else to)

Most transformations fail because leaders communicate change…and then keep behaving the same. Or they’ll hire outside consultancies to lead change management, but innovation, and people, really, don’t thrive in change or management environments.

Mindshift leadership needs to be visible because a mindshift starts with you. People have to see and believe that youchanged and what’s in it for them to follow.

It’s modeling the behaviors you want others to adopt:

experimenting openly

learning out loud

retiring outdated processes (and outdated mindsets)

showing what adaptation looks like in real time

If you want a culture of innovation, your team has to see you practice it.

5) Reward growth mindset behaviors, not just outcomes

If your incentives only reward certainty and perfection, your culture will avoid the very experimentation it needs to survive.

The organizations that become future-ready reward:

unlearning and learning

smart experimentation

progress and iteration

initiative and ownership

and intentional momentum.

6) Redefine success beyond quarterly metrics

Quarterly performance matters. But resilience is a performance signal too.

Adaptability is a performance signal.

Innovation capacity is a performance signal.

If you only measure what’s easy to track, you’ll miss what matters most when markets shift.

Mindshift expands the definition of “winning” to include the qualities that make growth sustainable in the next era.

7) Scale the mindshift across teams

The mistake too many leaders make is that they try to treat transformation as a project. But transformation cannot survive as a leadership initiative.
It has to become a cultural capability.

You have to set the vision…the future motivating state. And people have to see themselves in the future you’re building. That’s why there are several chapters dedicated to the art and science of real storytelling.

When people feel connected to the story, the journey, and the outcome…

They stop waiting for change.

They start creating it.

Why now is the time to read this

We’re not facing a single wave of change. We’re facing stacked disruption with more waves on the horizon.

Right now, CEOs are making a choice, whether they realize it or not:

Will you keep reacting to change? Or will you develop the mindset to shape it?

That’s what this is really about.

A mindshift is a CEO-level Ctrl+Alt+Delete.

It’s a reset of leadership itself.

How do you want to reboot?

If you’re ready to lead what’s next…

Please read my Chief Executive article: “The ‘Mindshift’ CEOs Can’t Ignore” and share it with C-Suites, boards, and leaders who influence them.

Then go deeper with the book: Mindshift: Transform Leadership, Drive Innovation, and Reshape the Future and please share copies with leaders at every level.

The future isn’t waiting. And if you’re waiting to see what others do first or for the use cases that others prove out, you’re on the wrong side of innovation.

The leaders who will thrive learn to prepare for what’s coming, and they prepare to create what comes next.

—

Read Mindshift | Book Brian as a Speaker | Subscribe to Brian’s Newsletter

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I had the opportunity to work with Amazon Web Services (AWS), Martin Ristov, and Ann Culver on a video that explores how ServiceNow and AWS power intelligent AI transformation.

By connecting workflows, data, and people, organizations can deploy customized AI solutions through the Now Assist platform using multiple foundation models while maintaining security and compliance. Learn how this partnership enables responsible AI practices and drives tailored outcomes without managing complex ML infrastructure.

Please watch!

Shot at AWS in San Francisco

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