via Dan Costa, Worth

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Please read the full article at Worth.

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

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

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

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

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

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

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

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

The post Mindshift Hits Amazon #1 for Business Management Books appeared first on Brian Solis.

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

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

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

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

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

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

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

Most organizations are treating AI as a productivity layer.

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

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

Automated Intelligence vs. Augmented Intelligence

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

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

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

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

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

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

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

cognitive dAIrwinism

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

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

That’s where a few emerging terms become useful.

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

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

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

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

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

When AI Starts Shaping the User

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

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

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

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

Think about that for a second.

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

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

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

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

AI Darwinism

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

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

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

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

Mindshift belongs in this conversation.

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

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

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

#WWAID

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

That is the idea behind WWAID.

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

This shift becomes easier to see in practice.

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

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

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

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

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

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

But do not stop there.

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

The real promise of generative AI is its duality.

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

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

That is the real choice in front of us.

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

AI Forward

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Amodei is sounding the alarm to get our attention.

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

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

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

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

Challenging the AI Jobs Narrative

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

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

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

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

The Gap Between AI Capability and Real-World Use

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Where does the impact concentrate first?

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

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

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

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

Where AI Is Hitting First

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The fork in the road most teams ignore

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

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

Iteration: improving yesterday to scale tomorrow.

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

Innovation: creating new value to unlock new opportunities tomorrow.

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

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

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

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

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

Why people resist change

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

Leaders love to talk about vision. Teams hear disruption.

Leaders love to talk about efficiency. Employees feel replaceability.

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

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

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

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

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

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

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

Because transformation doesn’t spread through logic alone.

It spreads through meaning.

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

Richie asked the question that is painfully accurate:

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

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

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

And then you wonder why you can’t innovate.

Innovation requires oxygen. Your calendar is often a vacuum.

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

The most powerful word in transformation is “no.”

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

No to the meeting that doesn’t need you.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The hidden tax of AI: workslop

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

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

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

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

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

A practical mindshift sprint you can run this week

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

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

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

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

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

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

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

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

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

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

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

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

If you take nothing else from this, take this:

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

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

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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 opposite
• Half 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 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 action
• Better service quality at scale
• More resilient operations
• New products that were previously uneconomic
• A 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:

1. We will not treat AI as a tool. We will treat it as a new operating model.
2. We will not run pilots forever. We will redesign workflows end-to-end.
3. We will not ban curiosity. We will build safe systems for it.
4. We will not measure AI by output volume. We will measure it by outcomes and learning velocity.
5. 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.

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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.

FAQs

What 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.

Brian Solis is a world-renowned futurist, keynote speaker, and author of over 60 industry-leading research publications and 8 best-selling books exploring disruptive trends, corporate innovation, business transformation, and consumer behavior.

Forbes has called him “one of the more creative and brilliant business minds of our time” and The Conference Board described Brian as “the futurist we all need now.”

Brian serves as the Head of Global Innovation at ServiceNow where he leads vision, strategy, and program innovation for the company’s global Innovation Centers. Brian also studies disruptive technologies, emergent trends, and market shifts to advise business executives on innovation and transformation strategies.

Brian continues to publish business and technology thought leadership in industry publications such as CIO, Forbes, and Worth, and has consistently been recognized as one of the world’s leading thinkers in innovation, business transformation, and leadership for over two decades.

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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 resilient
• innovate consistently
• move 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.

—

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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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Brian is proud to share that he has been invited for the second time to deliver the keynote at the Baker Hughes Annual Meeting in Florence, Italy.

For 27 years, the Annual Meeting has served as a landmark event for experts, thought leaders, and policymakers to convene, consider, and collaborate on the contemporary issues that drive energy and industry.

Brian’s keynote will focus on the following topic…

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Right before the break, I spent time with Jeff Barrett (It’s No Fluke/Shorty Awards). We explored why leaders keep making the same mistakes and why it’s a good idea for every leader to become an “everyday futurist” right now. Barrett’s takeaways from our conversation were published on Yahoo Creators. Our live conversation was just published on his podcast (Spotify).

Every January I convince myself this is the year I will finally “think more strategically.” I buy a new notebook. I reorganize my apps. I promise to read more books and scroll less. Audible counts. By mid-February, I’m back to reacting to emails, chasing headlines, and convincing myself that being busy counts as progress. It’s not a personal failure. It’s a leadership pattern.

According to futurist Brian Solis, it’s exactly why so many smart, experienced leaders feel like they’re constantly behind, even when they’re doing everything right. The biggest risk facing organizations today is not disruption itself. It is the habit of reacting to disruption without ever changing the mindset that led to being disrupted in the first place. That distinction matters more now than at any other moment in modern business history. I interviewed Solis, the 9x best-selling author and Head of Global Innovation for Service Now, this week for the podcastabout his latest book, Mindshift.

Over the past decade, leaders have navigated political instability, a global pandemic, forced digital transformation, supply chain shocks, and the rapid acceleration of artificial intelligence. Each time, organizations scrambled to adapt, often framing the moment as temporary, a new normal, a bridge back to business as usual.

But the disruptions did not slow down. They stacked.

What Solis argues, and what many leaders are now feeling, is that today’s business climate no longer rewards reactive leadership. It rewards those who can see signals early, interpret what they mean for human behavior, and make thoughtful decisions before change becomes unavoidable.

That is where the idea of becoming an “everyday futurist” or a “digital anthropologist” makes sense.

The Problem With Expertise

One of the hardest truths for experienced leaders to confront is that the skills and instincts that helped them succeed can eventually work against them.

Career success reinforces patterns. If a particular way of thinking has delivered results, promotions, and credibility, it becomes difficult to question it. Over time, leaders operate inside familiar guardrails. Decisions are filtered through past wins, existing metrics, and institutional expectations.

Solis describes this as a cognitive trap. Leaders do not fail because they are uninformed. They fail because they are operating with blind spots they do not recognize. Research cited in his work shows that while nearly everyone believes they are self-aware, only a small fraction consistently practice it.

This is why many organizations experience disruption as shock rather than evolution. Signals appear early. New behaviors emerge. Technologies mature quietly. But without curiosity and openness, those signals are dismissed as noise, fads, or problems for later.

Ignorance can feel efficient until it isn’t.

Why a Mindshift Is Necessary Now

What makes the current moment different is not any single technology or trend. It is the pace and depth of change happening simultaneously.

Artificial intelligence alone illustrates the challenge. Generative AI did not arrive unexpectedly. It has been developing for years. Yet its mainstream adoption triggered another wave of reactive behavior, with leaders scrambling to deploy tools without fully understanding how work, decision-making, or value creation might fundamentally change.

This pattern is familiar. COVID forced overnight digital transformation. Remote work scaled faster than any strategic roadmap predicted. Social platforms reshaped media, commerce, and politics before many institutions understood the implications.

The common thread is not technological surprise. It is leadership inertia.

A mindshift, as Solis defines it, is not about predicting the future perfectly. It is about changing how leaders relate to uncertainty itself. Instead of asking how to return to normal, everyday futurists ask what is emerging and how it might alter behavior, expectations, and opportunity.

That shift is no longer optional. It is foundational.

Becoming a Digital Anthropologist in Your Own Work

The phrase “digital anthropologist” sounds academic, but in practice it is deeply pragmatic.

Anthropology is the study of how humans behave within systems. Applied digitally, it means paying attention to how people adapt to new tools, platforms, and environments. Not just what technology can do, but how it actually gets used. Solis began applying this lens decades ago, studying how emerging technologies reshaped consumer behavior, workplace norms, and social dynamics. The goal was not prediction for prediction’s sake. It was relevance.

Everyday futurists do the same thing at a smaller, repeatable scale. They notice how customers, employees, and communities change their habits. They observe friction, workarounds, and unintended consequences. They ask better questions.

Why are people using this tool differently than expected? What behavior is being rewarded by this system? What feels easier now that didn’t before?

These questions turn abstract trends into usable insight.

Curiosity Is a Leadership Skill

One of Solis’s core arguments is that curiosity is not a personality trait. It is a practice.

Most leaders are trained to project certainty. Saying “I don’t know” can feel risky, especially in environments driven by short-term performance metrics. But curiosity requires admitting uncertainty before it becomes unavoidable. In Solis’s framework, the first step of a mindshift is learning how to receive. That means creating mental space to notice signals instead of filtering them out. Without openness, trends pass unnoticed. With it, leaders begin to perceive meaning.

From there, leaders can organize what they are seeing. Not every new development deserves equal attention. Some are fads. Others are foundational. The discipline is learning to tell the difference. This process does not eliminate uncertainty. It replaces anxiety with agency.

One of the most practical challenges leaders face is time. Immediate goals crowd out long-term thinking. Urgency replaces importance.

Solis acknowledges this tension directly. Even futurists miss signals when they are consumed by execution. The difference is not perfection, but intentionality. Everyday futurists make thinking part of the job, not a luxury. They allocate time to explore ideas without immediate ROI. They maintain a “parking lot” for trends that are interesting but not yet actionable. They revisit assumptions regularly. This habit prevents both overreaction and complacency. It allows leaders to act with context rather than panic.

The New Year Reset That Actually Matters

As organizations enter another year of planning cycles, the temptation is to focus on tools, tactics, and timelines. But the deeper reset is cognitive. The most important question leaders can ask is not what they plan to do differently, but how they plan to think differently.

Becoming an everyday futurist does not require a title, a crystal ball, or perfect foresight. It requires humility, curiosity, and the willingness to challenge one’s own assumptions. In a world where disruption is constant, mindset is the only sustainable advantage.

The future is not something that happens to leaders. It is something they either engage with thoughtfully or encounter unprepared.

And that choice begins with a mindshift.

E296 Brian Solis: Why a Mindshift is Needed

Forbes has called Brian Solis “one of the more creative and brilliant business minds of our time.” ZDNet heralded him as “one of the 21st century business world’s leading thinkers,” and Entrepreneur Magazine described him as “a world’s top superforecaster.”
Check out his latest bestseller, ⁠Mindshift: Transform Leadership, Drive Innovation and Reshape The Future⁠.

Brian is a world-renowned digital futurist, 9x best-selling author, and international keynote speaker. He has published over 70 widely read research reports that explore the future of business and industries, disruptive technologies, and shifts in markets and consumer behaviors.

Brian serves as the Head of Global Innovation at ServiceNow. In his role, Brian leads a global network of Innovation Officers and Futurists who study emerging technologies and micro and macro trends, business transformation patterns, and customer insights to understand important shifts affecting organizations and the markets they serve.
The Innovation team produces original research and thought leadership and delivers presentations and workshops that help leaders anticipate shifts, capitalize on trends, and drive resilience and long-term growth.

He is published in industry publications such as Harvard Business Review, Fast Company, CIO, Forbes, and Worth. And he has consistently been recognized as one of the world’s leading thinkers in innovation, business transformation, and leadership for almost three decades.

The post Yahoo Creators: Brian Solis on Becoming an Everyday Futurist and Why a Mindshift Is the Most Important Reset Leaders Can Make Right Now appeared first on Brian Solis.

The biggest risk in 2026 is leaders using 2016 mindsets to lead…but mistaking it as a 2026 mindset.

I’ve been meaning to share my interview with University of Toronto – Rotman School of Management Magazine.

This is a “Novel Economy,” where disruption is constant, compounding, and *always* novel (AI, climate, geopolitics, quantum, spatial intelligence… all evolving, emerging, and colliding). And when leaders talk about a “new normal,” it’s often a tell: they’re trying to steer back to business as usual instead of translating change into opportunity.

That’s why I wrote Mindshift.

A few takeaways you can apply immediately:

1) Reframe disruption (and opportunity).
The question shouldn’t be “How do we protect what we built?” It’s “What is this disruption inviting us to rethink, redesign, and reimagine?”

2) Build trend literacy across 3 layers:
• Microtrends = early signals in niche communities
• Macrotrends = broader movements across industries
• Megatrends = seismic forces that reshape society

The opportunity is in connecting the dots between them.

3) Self-awareness is a business advantage.
A self-aware mind is a shiftable (and growable) mind. It helps you escape “default thinking” (like reflexively cutting costs or automating away people) and see growth paths others miss.

4) Innovation = experience + speed + reinvestment.
The disruptors I’ve studied obsess over frictionless experiences *and* fast, decisive execution. Momentum matters.

5) Use the 6-stage mindshifting framework:
1) Receive, 2) Perceive, 3) Weave, 4) Conceive, 5) Believe, 6) Achieve.
Signals become strategy. Strategy becomes action.

If you’re navigating AI and uncertainty right now, start here…be curious, listen deeply, and ask better questions before rushing to solutions.

Read the full Rotman Q&A + then go deeper with Mindshift!

Please read my Q&A with Rotman here.

The post Rotman School of Management: What it takes to thrive in ‘the Novel Economy’ – Q&A with Brian Solis appeared first on Brian Solis.

A nice start to 2026! “X: The Experience When Business Meets Design” named is a reference book in shaping the future of brand, marketing, and experience via Infonegocios Miami!

Expertos y Libros de Referencia:

• Seth Godin (“This is Marketing”)

• Simon Sinek (“Start with Why”)

• Brian Solis (“X: The Experience When Business Meets Design”)

• Ann Handley (“Everybody Writes”)

• Case studies: Red Bull, Tesla, Patagonia, Glossier, Netflix

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3 questions…3 minutes. That’s the premise of this conversation.

Innovation is having a moment, and not just in the “new tech, new tools” way. What I’m seeing (and writing about) more than ever is a deeper shift: how leaders are rethinking transformation through the lens of people, purpose, and possibility. That’s the foundation for my latest book, Mindshift: Transform Leadership, Drive Innovation, and Reshape the Future.

That’s why I enjoyed joining Katja Weber on the latest episode of Deutsche Telekom’s 3×3 Insights, where we explored what it really takes to lead in an era defined by constant change and accelerating AI.

In this conversation, we went beyond headlines and hype to talk about the human side of what’s next, and what it demands from all of us.

What We Covered in This Episode

The future of digital transformation (and why it’s not a tech project)

Digital transformation is often framed as modernization: migrate systems, automate workflows, apply AI, move faster. But transformation isn’t an IT initiative, it’s a leadership choice.

In our conversation, we discussed how organizations can navigate change without simply reacting to it, and how they can build cultures that foster innovation as a practice, not a slogan.

AI, human experience, and the balance we can’t ignore

AI is reshaping the way work gets done, and it will continue to do so. But what’s easy to overlook is the emotional and experiential impact of that shift on teams, customers, and communities.

We talked about how leaders can find the balance between automation and empathy, and why the next chapter of AI won’t just be about what technology can do, but what it should do in service of people.

Resilience and agility in uncertain times

Uncertainty is no longer a phase. It’s the operating environment.

We explored what it means to build enterprises that are resilient and agile, not by chasing the next trend, but by aligning decisions with purpose and designing organizations that can adapt without losing their center.

“Innovation isn’t just about technology, it’s about reimagining what’s possible for people.”

That idea is at the heart of what I believe: true transformation starts with mindset, not machinery. Technology can amplify impact, but it’s human creativity, conviction, and resilience that sustain it.

A Question for You

What do you think you should know today that you don’t know yet?

Drop your thoughts in the comments! I’d love to hear what you’re exploring, questioning, or learning right now.

The post Innovation Meets Inspiration: 3×3 Insights Interviews Brian Solis about the importance of a Mindshift Right Now appeared first on Brian Solis.