It had to be the algorithm.

After I received a note online from a friend who asked me why Dave Wright and I wrote the book Infinite “when no one is reading books anymore.” Shortly thereafter, I stumbled upon an article in The Atlantic that made me moue (had to look it up to describe the facial reaction I had to the headline), “The End of Reading is Here.”

Then the next day, I posted a video that showcased the marketing genius of Steve Jobs when he launched the iPod Nano in 2005. Then right after that, I saw an article in my feed, again published in The Atlantic, where the first line shared the story of Celeste Stange, a 29 year old, who recently purchased an iPod Nano for $69 on eBay as part of the emerging “analog movement.
After reading each article, I then decided to read them as if they were one…one counter-cultural movement that’s reclaiming friction, focus, and presence. And another that’s surrendering them to feeds, algorithms, and now AI.

I couldn’t help but think that one of these two very different futures may depend on which one we normalize. And without work, we might end up in a worse place. As my friend and colleague Diana Wu David recently reminded me, “well, I’ve always said we sort of suck at being humans.”

A Tale of Two Futures

On one side of the digital equation, we observe young people buying iPods, CD players, digital cameras, DVD players, first-generation iPhones, and even old Macintosh computers. They are intentionally replacing devices that can do everything with devices that do one thing, often slowly and even imperfectly. But that’s the point. As a single purpose device, it does what it needs to do without demanding their attention every eight seconds.

On the other side, The Atlantic explores whether we are entering a “postliterate age,” one in which fewer people possess the patience, desire, or cognitive stamina to read books and engage deeply with complex ideas. Think about it. In an era of AI, it’s so easy, even normalized, to take something complex and run it through ChatGPT or Claude to summarize it for you.
One movement is attempting to reclaim attention and to take back control.

The other shows what happens when we surrender it in the name of speed and scale.

Social media trained us to give away our attention. And now generative AI is raising the stakes, inviting us to give away the thinking itself.

The Analog Rebellion is not Really About Nostalgia, It’s About Agency

It’s easy to point to nostalgia when revisiting analog trends. Even I made the investment in a record player and vinyl. And I recently found my old iPod Nano 6th generation with a video screen that I was thinking about using again. I just don’t have wired earbuds lol! Oh, and while visiting my sister, she showed me that she still has the original iPhone. As I’m writing this, I think I’m going to ask her if she’d consider selling it to me.

The people “going analog” are not necessarily abandoning technology. Many of the devices they are rediscovering are in fact digital. The reference to analog, I guess, is intentionality. In theory, single-function devices keep you more in the moment. So, what people appear to be rejecting is not technology itself, but the conditions under which modern technology demands to be used.

The attraction of the old iPod is not merely its click wheel or its millennial aesthetic. It is that the device contains only the songs you deliberately placed there. There is no infinite library, no algorithmically generated playlist, no notification informing you that someone you barely remember has posted something you do not need to see.

In The Atlantic’s reporting, one woman described feeling paralyzed by the millions of songs available through streaming services. On her iPod Nano, she has only the music she chose. Others described buying CDs, waiting for them to arrive, and sitting down to listen to an album with intention. Interest in these older devices is substantial enough that eBay reported an average of 1,300 searches an hour for iPods last year, while prices for some models rose by more than 50%.

What looks like retro enthusiasm may actually be a quest for agency (a word that I am using more and more since the launch of ChatGPT in 2022).

People are rediscovering something modern product design spent years trying to eliminate…friction. Selecting an album, loading music onto a device, inserting a DVD, developing a photograph, or finding your way without GPS requires intent, focus, and effort. But that work creates participation. Participation creates memory. And memory helps transform consumption into experience.
We engineered friction out of our lives in the name of convenience and speed. Then we discovered that some of that friction was where life was happening.

This prompted me (get it!?) to think about that profound line from the series finale of the U.S. version of The Office when the character Andy Bernard observed, “I wish there was a way to know you’re in the good old days before you’ve actually left them.”

Convenience Became Connectivity, and Connectivity Became Captivity

There was a period when technology was useful without being omnipresent. As former Apple executive and “father of the iPod” Tony 

Fadell describes it in the article, devices were once “convenient, but not connected.” Your camera took pictures. Your music player played music. Your phone made calls.

Then those products converged into the “everything device.”

The smartphone became our camera, map, newspaper, television, office, shopping mall, social life, entertainment center, memory, and increasingly our emotional support system. Convenience and connectivity became inseparable. Every activity became an opportunity to capture attention, gather data, recommend something, or keep us engaged for just a little longer, and then a lot longer.

The problem with an everything device is that it eventually wants everything from us.

This was part of the journey I explored in Lifescale. At the time, I argued that we had not simply become distracted. Our behaviors were being deliberately shaped around interruption, validation, immediacy, and the endless scroll. Reclaiming our lives did not require abandoning technology. It required learning to use technology without allowing technology to use us, and then to scale our lives intentionally and directionally. In fact, I still use that book and wear the bracelet to live a more creative, productive, and happy life.

Going analog is, in many ways, Lifescaling made physical. It is an attempt to place boundaries around infinity, to turn our attention away from everything we could be consuming and toward what we have consciously chosen to experience.

The Postliterate Future Is Already Arriving

The parallel cultural movement is more troubling, and not just because I’m an author. It’s because I’m fighting to stay human.
In “The End of Reading Is Here,” author Rose Horowitch examines the possibility that the literate age may prove to be a relatively brief chapter in human history. Fewer than half of American adults reported reading a book of any kind in 2022, and only 38% said they had read a novel or short story. The proportion of Americans who read for pleasure on any given day fell from 28% in 2004 to 16% in 2023. The decline cuts across age, gender, and education. And here we are three years into generative AI, which makes me wonder where the stats sit today.

Reading isn’t a defense of the past or holding on to nostalgia. Long-form reading is a superpower. It boosts our cognitive stamina, strengthens critical thinking, enhances analytical skills, boosts empathy and social cognition, enhances our vocabulary and communication, and supercharges our creativity!

Reading also teaches how to remain present while thinking.

Deep reading combats distractions and lengthens our attention span and trains us to stay with an idea long enough for it to challenge us. It requires interpretation, imagination, patience, memory, and tolerance for ambiguity. It allows us to inhabit another person’s perspective without immediately responding, rating, sharing, or swiping away.

Digital culture rewards almost the opposite behavior. It compresses ideas into clips, headlines, summaries, reactions, and increasingly generated answers. We are becoming highly proficient at moving across information while becoming less practiced at moving through it.

We consume more while absorbing less. We encounter more voices while developing less of our own.

The decline of reading and the rise of analog culture aren’t separate trends. They are different responses to the same condition: a society that has optimized access to information while steadily eroding the human capacity to engage with it.

AI Is Moving Us from Attention Outsourcing to Cognitive Outsourcing

Social media competed for our attention. AI offers to complete the thought before we have one. And speaking of AI, we erode our authentic intelligence with every swipe, scroll, (and prompt).

People now turn to chatbots to draft emails, summarize information, prepare for conversations, make decisions, interpret emotions, generate ideas, and tell them what to say next. The Atlantic has documented heavy AI users who feel unable to begin meaningful work without consulting a chatbot, including one educator who realized his first instinct in solving a simple physical problem was to ask ChatGPT rather than think through it himself.

Let’s be clear. I’m not saying that AI is inherently harmful. Used intentionally, it can remove drudgery, democratize expertise, challenge assumptions, deepen analysis, and enable us to create things that were previously beyond our reach.

The danger though begins when assistance subconsciously becomes dependence.

AI produces polished answers so quickly that fluency can masquerade as understanding. We can appear more articulate while becoming less certain of what we actually believe. We can produce more ideas while exercising less imagination. We can respond faster while slowly losing the ability to sit with uncertainty long enough to develop an original point of view.

Philosopher Kwame Anthony Appiah describes the deepest risk as “constitutive de-skilling.” It’s the erosion of judgment, imagination, empathy, and our feel for meaning and proportion. Just what our society needs right now!

Before AI, digital tech was already interrupting our thinking. Now it can prevent our thinking in the first place. But hey, we can get more done, at speed at scale!

And the faster we move, the greater the risk of burnout. With the algorithm working in my favor, I then was presented with an article in Bloomberg that tracked the uptick in mental health leave. In fact, 67% of US employers report an increase in mental-health-related leaves of absence and accommodation requests over the past year, according to a survey by employment law specialists Littler Mendelson.

Think Outside of the Bot

We gave digital our attention. Are we about to give AI our minds?

Recently I shared some thoughts, and a nifty t-shirt concept! I’ll reshare some of the thinking here.

When we want people to be creative, we tell them to “think outside the box.” But unless we change the rules, create psychological safety, reward curiosity, and give people time to explore, we are not really changing the paradigm. We are merely redecorating the same box.

I feel the same way about AI.

We ask people to think bigger with it but then measure them by how much faster they complete yesterday’s work. We deploy AI to summarize, automate, respond, and produce more of what already exists. Then we wonder why it is not transforming the business, or the person using it.

This is why I have been advocating WWAID: What Would AI Do?

The question should come before the prompt. It challenges us to imagine possibilities we might otherwise miss.

But I am now embracing an additional mantra: Think outside of the bot.

And I’m not alone. The Economist launched a brilliant series of billboards shading AI, with one of them proclaiming just that, “think outside the bot.”

Thinking outside of the bot means bringing something original to the relationship before asking the machine to contribute. It means forming an opinion before requesting an answer. It means using AI to interrogate your assumptions rather than manufacture your convictions.

The aim for AI-human work is not to compete with AI at being a machine. It is to become more human because of what the machine makes possible.

What are We Teaching the People Around Us?

These choices affect more than our individual productivity.

Children develop judgment by making imperfect decisions. They build creativity through boredom, play, reading, conversation, and the slow frustration of trying to make something work. When every question is immediately answered, every assignment summarized, and every uncomfortable moment filled with content, we may be removing the very conditions through which a self is formed.

The same is true in our families. Presence is not simply occupying the same room while everyone attends to a different screen. It is the increasingly rare experience of giving another person attention without simultaneously offering it to a platform.

And it is true at work. Leaders cannot ask teams for breakthrough thinking while surrounding them with back-to-back meetings, constant notifications, productivity surveillance, and AI systems designed primarily to increase output. Creativity needs space. Curiosity needs permission. Original thinking needs enough time to become inconvenient.
Perhaps every family and every organization should ask:

What human capacity are we strengthening through technology—and what capacity are we quietly allowing to disappear?

The Future May Need to Become a Little Less Convenient

The people buying iPods are not retreating from the future. Ideally, they’re prototyping a healthier version of it.

They are reminding us that more choice doesn’t always create more freedom. It can fuel fatigue. More connection does not guarantee more belonging. More information does not necessarily produce more wisdom. And more intelligence on demand does not automatically make us more intelligent.

The real question is not whether we should choose analog or digital, or humans over AI. The question for us to deeply consider is whether we remain intentional enough to choose at all. That requires a different kind of AI, authentic intelligence.

What parts of your life should remain deliberately slow? What decisions should still require your judgment? What conversations should never be summarized? What should your children learn to struggle through? What should your team be expected to imagine before asking a machine? And what are you willing to make a little harder, quieter, slower, or more human, so that it remains truly yours?

Use the technology. Explore its possibilities. Let it stretch what you can see, create, and become.

Just remember to think before you prompt.

Oh, and think outside of the bot.

Yours authentically,
Brian

nfinite ∞ | Mindshift | Subscribe | Keynote Speaker

The post We Gave Technology Our Attention, Are We About to Give AI Our Minds? appeared first on Brian Solis.

At AI Week in Milan, I had the privilege of representing ServiceNow on a stage filled with people thinking seriously about the future of artificial intelligence. Rather than offer another tour of tools, trends, and predictions, I wanted to challenge the premise guiding much of today’s enterprise investment: that applying AI to the existing business is the same as transforming it.

It isn’t.

Most organizations are using AI to accelerate processes, automate tasks, increase output, and extract efficiencies from operating models designed for another era. Those gains are real, particularly when companies have the discipline to convert productivity into measurable value. But a faster version of yesterday’s company is not necessarily prepared for tomorrow. As scrutiny around AI returns intensifies, leaders will have to confront whether their investments are creating new value or simply reinforcing an AI-enabled status quo. The growing effects of AI on judgment, originality, attention, and the rising volume of synthetic mediocrity deserve their own conversation. For now, the more urgent question is whether we are using intelligence to rethink the business—or merely to make the old one run harder.

What if a company successfully deploys AI across the enterprise and still misses the future? What if it becomes more efficient without becoming more innovative, more productive without becoming more valuable, and more automated without ever becoming truly transformed?

AI is capable of helping organizations rethink how they operate, how they create value, how people work, and what customers experience. But technology cannot deliver that future on its own. Leaders must be bold. They must be willing to question the assumptions, processes, structures, and business models that made the current organization successful. Otherwise, AI will simply make the past run faster and more efficient. That’s fine and all, but that hardly makes any organization more competitive.

The risk for leaders is not thinking too big, but rather too small. They’re treating a once-in-a-generation platform for reinvention as another program for optimization and cost reduction.

In my keynote, I explore why the future of business transformation begins with an executive mindshift, how organizations can move beyond automating yesterday’s work, and what it will take to compete in a world shaped by augmented employees, AI agents, digital coworkers, and increasingly autonomous operations.

AI Is Being Applied to Yesterday’s Business

Digital transformation was supposed to reinvent organizations for a digital world. In reality, much of it digitized the work companies were already doing. Paper forms became online forms. Meetings moved to video platforms. Legacy applications migrated to the cloud. Existing processes became faster and more accessible, but the logic behind those processes often remained untouched.

AI could easily follow the same path.

Many of today’s most popular enterprise use cases focus on summarizing meetings, generating content, responding to customers, writing code, routing requests, reducing queues, deflecting cases, or completing repetitive tasks. These applications are useful and can produce meaningful returns. They can remove friction and give employees time to focus on work that requires judgment, creativity, empathy, and expertise.

But they also begin with a familiar question: How can AI help us do what we already do more efficiently? And in even more narrow thinking, the question prioritizes automating work to displace workers.

When leaders focus exclusively on automating or accelerating existing work, they begin with the assumptions, constraints, and organizational structures of the past. They optimize the process before questioning why it exists. They automate a task before considering whether the task should be necessary in a world with AI. They improve a customer journey without asking whether an entirely different experience could now be designed.

Even though digital transformation digitized yesterday’s work vs. transforming it, AI gives us an opportunity to transform tomorrow. Actually, AI demands it and so will the market. Nonetheless someone still has to imagine what tomorrow could be. If you’re waiting for someone to tell you what to do or if you’re waiting to be disrupted, you’re on the wrong side of innovation…and history.

Transformation Requires a Mindshift

A mindshift is more than an openness to change. It is a profound change in perspective that helps us break free from old patterns and see possibilities that were previously hidden by our experience. And experience can be both an advantage and a limitation.

Leaders are often rewarded for knowing how their companies and industries work. They understand the operating model, the economics, the customers, the competitors, and the rules of engagement. But when those rules begin to change, expertise can unintentionally become a defense of the status quo.

The electric light bulb did not emerge from the continuous improvement of candles. It required someone to stop asking how to make a better candle and begin imagining a different way to create light.

AI demands the same break from linear thinking. The strategic question is no longer only how to make today’s business more productive. It is whether the current vision is ambitious enough for a world in which intelligence can be embedded into every process, interaction, product, service, and decision.

What could your company become if it were designed today around intelligence rather than retrofitted with it? What would the organization look like when employees are augmented by AI, agents coordinate work across functions, and digital coworkers execute within clearly defined boundaries? Which roles become more valuable, which skills become essential, and which assumptions about scale no longer apply?

These are not technology implementation questions. They are strategic questions about the future identity of the enterprise. Someone has to be accountable for designing it.

An AI-First Mindset Changes the Questions

Leaders need an AI mindset.

A digital mindset is often defined by “what” and “how.”

What can we digitize? How can we make this process faster? How can we increase performance, reduce effort, or operate more efficiently?

An AI-first mindset adds two more consequential questions: why and what if.

Why does this process take so long? Why are customers asked to repeat information the company already possesses? Why does one workflow move through several departments and disconnected systems? Why are talented employees spending so much time reconciling information, searching for answers, or navigating organizational complexity?

Then comes the question that opens the door to reinvention: What if we did not have to operate this way at all?

This is the idea behind WWAID: What Would AI Do?

WWAID is not about outsourcing thinking or judgment to machines. It is a pre-prompt mindset that challenges us to rethink the problem before we ask AI to solve it. It helps leaders step outside the limitations that shaped the original process and consider what might be possible when intelligence is available across systems, data, workflows, and decisions.

Before automating a task, ask whether AI could help eliminate the need for it. Before improving a workflow, ask whether the entire experience could be redesigned around the outcome. Before adding AI to a product, ask how intelligence might change the product’s purpose, value, or business model.

The most interesting opportunities emerge when AI is not simply used to replace human effort, but to augment human potential. In that sense, perhaps this is not only about artificial intelligence. It is about augmented intelligence: helping people think more expansively, make better decisions, exercise creativity, and achieve outcomes that neither humans nor machines could accomplish independently.

Every Company Needs Two AI Growth Curves

In Milan, I described two paths organizations need to pursue simultaneously.

The first is iterative AI. It improves existing services and work. It removes routine tasks and operational complexity, helping people work faster, make better decisions, and spend more time on higher-value activities. Iterative AI is essential because many companies are still burdened by fragmented systems, manual processes, technical debt, and workflows that have accumulated over decades.

The second is innovative AI. This is where organizations use intelligence to create new capabilities, experiences, products, services, and business models. Rather than improving the existing value chain, innovative AI can redefine how value is created and delivered.

Iterative AI supports efficiency and linear improvement. Innovative AI makes exponential growth and disruption possible.

Organizations need both curves.

Automation can generate savings, increase capacity, and improve productivity. But those gains should not disappear entirely into cost reduction. They should also be reinvested into people, experimentation, augmentation, and business reinvention. That right there challenges the traditional ROI model…automate work, take out costs accordingly. The cost of that ROI model though could be argued as one of competitiveness. The organization may become highly efficient at operating a model that is losing relevance.

The duality of AI is one of its most promising qualities. It can help companies reduce costs and improve the work of today while simultaneously creating the capacity to invent the work and growth of tomorrow. The leadership challenge is to prevent the first agenda from consuming the second.

From Automation to Augmentation to Agents

AI maturity is not achieved simply by deploying more tools. It represents an evolution in how intelligence participates in work.

Automation takes existing work and makes it more efficient, scalable, and consistent. Yet many organizations attempt to automate processes they have not sufficiently digitized, unified, or optimized. Automating a fragmented process does not repair it. It can simply help dysfunction fail faster.

Augmentation represents a more advanced relationship between people and AI. Here, AI becomes a cognitive exoskeleton, helping employees expand their expertise, improve decisions, and exercise greater creativity. The objective is not to remove humans from the loop indiscriminately, but to redesign the loop around what people and machines each do best.

Agentic AI takes this further. Agents can interpret context, interact with systems, coordinate tasks, adapt to changing conditions, and take action within defined parameters. Eventually, more workflows will operate autonomously with high-level human supervision, continuously evaluating, learning, and evolving.

This progression changes more than productivity. It changes how businesses are structured and how they scale.

Traditional organizations were designed around human boundaries. Work is divided into departments, passed between systems, and scheduled around working hours. But agents do not necessarily operate within those same limits. They can work continuously and coordinate across functional silos, creating the possibility of an always-on enterprise that responds in event time rather than calendar time.

When AI and agents offer potentially exponential scale, leaders must rethink the org chart, the operating model, and even the relationship between company size and market power. Established enterprises may gain new forms of leverage, but smaller businesses may also gain access to capabilities that were once reserved for organizations with enormous headcount, capital, and infrastructure.

The future organization will increasingly be redesigned around the collaboration of people and intelligent systems…starting today. It is foundational for a new breed of human-machine collaboration.

Innovation Demands That Leaders Disrupt Themselves

Many executive teams arevi waiting for AI to become more predictable. They want clearer best practices, more proven use cases, more mature platforms, settled regulations, and competitors they can safely follow.

But if you are waiting for someone to tell you exactly what to do, you are already on the wrong side of innovation.

By the time a new model becomes obvious, the organizations that defined it are already learning, improving, and compounding their advantage. Leaders do not need perfect foresight, but they do need the willingness to question what made the company successful and recognize when those same practices could constrain what it becomes next.

For innovation to succeed, you must be willing to disrupt yourself.

That does not mean abandoning the core business or chasing every new AI trend. It means creating two parallel agendas: one that improves today’s performance and another that explores what could replace, expand, or transcend today’s model. Dave Wright and I explore this as a bi-modal approach to AI strategy in our new book Infinite: How Visionary Leaders Transform Today’s Businesses into AI-Forward Companies.

Start with a meaningful customer, employee, or business outcome. Examine the entire workflow supporting it. Identify where friction, fragmentation, and legacy assumptions shape the experience. Then bring together the people who understand the business, the process, the data, the technology, and the human consequences to reimagine the flow from end to end.

Do not begin with the question, “Where can we add AI?”

Begin with, “What outcome should now be possible, and why are we not already delivering it?”

The Future Is Still a Leadership Decision

AI will continue to advance whether your organization is ready or not. Agents will become more capable, AI-native competitors will emerge, competitors will become AI-first, and customers and employees will begin to expect experiences that are intelligent, predictive, personalized, and increasingly autonomous.

The advantage will come from how leaders think about it, what they choose to do with it, and how quickly their organizations can learn.

In an era of disruption, there are still two things leaders can control: their mindset and their actions.

This keynote from Milan is an invitation to pause, watch, and have a different kind of executive conversation…not about AI, but about the organization you are becoming.

After watching, bring this question to your leadership team:

Are we using AI to improve the company we were, or to invent the company we need to become?

Great leaders do more than react to the future. They imagine a better future that would not have otherwise happened. And then they lead others in bringing it to life

Infinite ∞ | Mindshift | Subscribe | Keynote Speaker

The post The AI-Powered Status Quo: AI Transformation is a Test of Leadership appeared first on Brian Solis.

At first, those comparisons can sound dramatic, even inflated.

But they are not entirely wrong.

Fire did not change the world simply because it existed. Electricity did not transform civilization simply because someone discovered it. The wheel did not alter the trajectory of humanity simply because it rolled. These were not just inventions. They were enabling forces. Their potential and ultimatle power came from what people imagined, built, and reinvented because of them.

That is exactly where we are with AI. And yet, too many leaders are meeting this moment with painfully small ambitions.

They are using a civilization-shifting capability to write better emails, summarize longer meetings, generate more content, automate existing work, reduce costs, and increase efficiencies. Useful? Of course. Transformative? That’s another story.

This is the gap I wanted to confront in my keynote at Integrated Systems Europe.

The issue is whether we are bold enough to use it in ways that actually matter.

The Real Risk Is Not AI, It’s Incremental Thinking

Most organizations are still approaching AI through the lens of optimization.

How can we automate this task? How can we reduce that cost? How can we produce more with fewer people? How can we do what we already do, just faster?

These are understandable questions. They are also incomplete questions.

They keep companies anchored to the past while convincing themselves they are moving into the future.

AI does not become revolutionary when it helps us do familiar work more efficiently. It becomes revolutionary when it helps us see differently, think differently, decide differently, and create differently.

That’s proper invitation.

Not iteration, but reinvention.

Not just productivity, but possibility.

Not “How do we move faster?,” but “What becomes possible now that never was before?”

AI is not just improving execution. It can challenge imagination.

AI Is Exposing Leadership, not Just Technology

AI is doing something few technologies have done so quickly and so visibly.

It is exposing how leadership thinks.

When a company approaches AI only as a tool for efficiency, that tells you something. It tells you the organization is still trapped inside the logic of the current model. It tells you leaders are trying to protect the business they know rather than invent the business they will need. It tells you they are managing disruption instead of using disruption as a design material.

AI magnifies intent.

If your ambition is incremental, AI will help you scale incrementalism.

If your vision is constrained by legacy assumptions, AI will help you operationalize those assumptions more efficiently.

If your leadership team is only asking where AI can cut cost, then AI will become a cost-cutting instrument.

But if leadership asks where AI can unlock new value, create new experiences, reimagine workflows, expand human capability, and design entirely new categories of advantage, then AI becomes something else.

It becomes a growth engine…a reinvention engine…a leadership test.

And that is the part many executives still do not want to confront. AI is not only transforming markets. It is raising the bar for vision.

Most Companies are Using AI to Improve the Past

This is the trap, and it’s common.

Organizations tell themselves they are innovating because they are experimenting with AI. But in practice, many are simply optimizing legacy models with better software. They are using AI to make the old machine run smoother.

That isn’t transformation. Transformers will redesign the core of how value is created.

Leaders will ask bigger questions.

1. What if the workflow itself is dated or limited?
2. What if the customer journey should be reinvented, not improved?
3. What if teams should be organized around outcomes instead of functions?
4. What if products should become adaptive?
5. What if services should become predictive?
6. What if intelligence should not sit in dashboards, but move inside every decision, every process, every touchpoint?

This is what too many AI strategies still miss…the opportunity to look beyond yesterday and design tomorrow.

High Performers Use AI Differently

This is true for companies, and it is equally true for individuals.

The highest performers I know are not using AI to avoid thinking, they are using it to think better. These more ambitious thinkers are outperforming their other AI-enabled peers by 7x.

They are not outsourcing judgment. They are sharpening it. They are using AI to interrogate assumptions, test scenarios, expose blind spots, compare possibilities, pressure-test ideas, rehearse decisions, and expand the range of what they can see.

There are two very different futures emerging right now.

In one, people use AI to reduce effort and cost and increase efficiencies.

In the other, people use AI to expand capability.

One path leads to dependency.

The other leads to evolution.

That means learning how to pair machine intelligence with human imagination, judgment, empathy, taste, experience, and courage.

Competing Against AI is the Wrong Mindset

One of the most dangerous narratives in the market right now is that we are somehow in a race against AI.

That framing creates fear. It narrows vision. It pushes leaders toward defensive decisions. It treats AI as a threat to manage rather than a force to shape.

But the real challenge is learning how to compete with AI.

That is a completely different posture. It’s not competing against, but more effectively competing with AI.

When you compete against AI, you protect.

When you compete with AI, you invent.

When you compete against AI, you focus on automation and replacement.

When you compete with AI, you focus on augmentation.

When you compete against AI, you ask how to preserve relevance.

When you compete with AI, you ask how to create new relevance.

It changes strategy, which changes leadership.

It changes how work is designed, how value is measured, how talent is developed, and how growth is pursued.

Five Questions Every Leadership Team Should Ask Now

These are five questions every C-suite should be asking right now:

1. Where are we using AI to optimize the past instead of inventing the future?

Look beyond productivity gains. Identify where AI is simply making current work more efficient versus where it could help create new business value, new offerings, or new market advantage.

2. Which assumptions about work, value, and growth no longer deserve to survive?

Challenge the defaults. Many operating assumptions were designed for a pre-AI world. Some should be improved. Others should be retired.

3. Where can AI elevate human contribution rather than reduce it?

The strongest organizations will not be those that remove the most labor. They will be the ones that free people to do more strategic, creative, relational, and high-impact work.

4. What would we redesign if we built this business today with AI at the center?

Ask this honestly. Not as a thought exercise, but as a strategic lens. Products, journeys, org structures, decisions, service models, and operating rhythms all look different when intelligence is native to the system.

5. Are we leading AI as a technology initiative or as a reinvention agenda?

This may be the most important question of all. Because if AI sits only in IT, innovation stalls. If it lives inside leadership, transformation begins.

This Is a Leadership Moment, not Just a Technology Moment

Yes, AI is a technology revolution.

But for business leaders, it is something more consequential.

It is a leadership moment.

It demands more than adoption roadmaps, governance frameworks, and pilot programs.

This moment calls for imagination. It calls for conviction.

It calls for executives who can see beyond efficiency to opportunity, beyond automation to elevation, beyond incrementalism to reinvention.

It calls for leaders who understand that the future will not be built by those who simply deploy AI first. It will be built by those who understand what AI makes possible and then have the courage to act on it.

That is what I hoped to leave behind at ISE and with you now.

Dream bigger.

Think bigger.

Lead bigger.

Watch the ISE Keynote

Infinite ∞ | Mindshift | Subscribe | Keynote Speaker

The post AI Is Eating the World. Are We Ready to Think Bigger? appeared first on Brian Solis.

Brian Solis, Head of Global Innovation at ServiceNow and one of the world’s leading digital futurists, recently joined Frank Cottle on the Future of Work show to explore how AI business transformation is redefining leadership, innovation, and the literal future of work.

Artificial intelligence has quickly become one of the defining forces influencing business, but many organizations are still approaching it as a cost-cutting exercise rather than an opportunity to create new value. In this episode of the Future of Work® Podcast, Frank Cottle welcomes Brian Solis, Head of Global Innovation at ServiceNow, to discuss why today’s AI moment represents far more than another technology cycle.

Brian explains why organizations stand at a crossroads. One path focuses on improving yesterday’s business through automation and efficiency. The other embraces AI as a catalyst for innovation, new business models, and entirely new forms of value creation. Throughout the conversation, Frank and Brian explore leadership, trust, workforce reinvention, AI augmentation, entrepreneurship, organizational culture, and the growing responsibility leaders have in guiding people through unprecedented technological change.

The discussion also examines why smaller organizations may be uniquely positioned to compete in the AI era, how AI can expand human capability instead of replacing it, and why curiosity, imagination, and courage will become defining leadership qualities. For executives, entrepreneurs, and workplace leaders preparing for what’s next, this episode provides a thoughtful roadmap for building organizations that grow through innovation rather than fear.

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More than a decade ago, I created this trailer for my book, X: The Experience When Business Meets Design. For reasons I can no longer remember, I never released it.

It became a lost trailer from the digital era. Yet the idea behind it may be even more important in the AI era.

At the time, customers were changing faster than the experiences companies designed for them. Digital, mobile, and social technologies were reshaping how people communicated, discovered, purchased, shared, and lived. But most businesses were responding by adapting existing experiences to new channels rather than reimagining them for a new world.

Websites became digital versions of stores. Mobile apps became smaller websites. Social media became another broadcasting channel.

The technology was new. The thinking behind it often was not.

Brand experience, customer experience, user experience, product experience, service experience, digital experience, and physical experience were still designed independently, often by different teams pursuing different objectives.

X argued that these were never truly separate experiences. They were all part of one experience as lived by the customer.

They needed to be designed collectively, cohesively, and purposefully around the human being moving through them.

A Timeless Idea from the Digital Era

I chose this moment with Steve Jobs because he articulated something every leader still needs to hear: start with the experience you want to create for people, then work backward to the technology.
That sounds obvious. It rarely is.

Companies naturally begin with what they make, what they sell, how they operate, and what their technology can do. Over time, the product, process, and business model become the lens through which leaders see the customer.

But customers do not experience an organizational chart. They do not care which department owns a touchpoint or which platform powers it. They experience one company, one brand, and one connected, or disconnected, journey.

This is the value of being a product company that is not led solely by the product, but by the said and unsaid, the seen and unseen, the pain and joy of what customers experience compared with what they could experience.

Customers know what they know. They know what sucks, and they know what is extraordinary. Those are the two experiences they remember.

Everything in the middle is forgettable.

The AI Era Is Repeating the Digital Era’s Mistake

The technology has changed. The pattern has not.

In the race to build with AI, too many organizations are once again beginning with the technology. They are asking what AI can automate, accelerate, generate, optimize, or eliminate.

Those questions may produce efficiency. They do not necessarily produce innovation.

The more important question is: What can people experience now that was never possible before?

AI should not simply be inserted into yesterday’s customer journey, product, service, workflow, or business model. That is the AI equivalent of shrinking a website to fit a mobile screen and calling it transformation.

The opportunity is to work backward from an experience worth creating.

What should become easier, more intuitive, more personal, more useful, more human, or even more magical? What frustration should disappear? What unmet need could finally be addressed? What could a person accomplish, feel, or become that they could not before?

That is where AI becomes more than another technology layer. It becomes an ingredient in meaningful experience innovation.

AI Cannot Replace the Source of Differentiation

AI can recognize patterns. It can synthesize information, generate possibilities, simulate scenarios, and help us build and learn at extraordinary speed.

But AI cannot build intuition.

AI cannot feel understanding.

AI cannot internalize empathy.

There are insights in talking to people, listening to them, observing them, and embracing empathy before vision and strategy. When you open your heart and mind, you begin to hear things others do not. You begin to see things others cannot.

The unsaid and the unseen…that is the differentiation.

AI can help you act on those insights, scale them, personalize them, and continuously improve them. But leaders and teams still have to care enough to discover them.

The opportunity is to build a company rich with the insight and inspiration needed to create value customers will feel immediately.

Then productize empathy. Operationalize it. Optimize it. Scale it with AI.

But never automate away the humanity that made the experience worth creating in the first place.

A Lost Trailer with a Message for the AI Era

I made this trailer before the current AI revolution. Yet it asks the question that still matters most:

Are we starting with the technology, or are we starting with the experience?

More than a decade later, the tools are exponentially more powerful. The possibilities are far greater. The responsibility to design purposefully is even more urgent.

Start with people. Imagine an experience worth remembering. Then use AI to make it possible.

Infinite ∞ | Mindshift | Subscribe | Keynote Speaker

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I had the privilege of keynoting UserTesting conferences in San Francisco and New York. It was my third and fourth UserTesting event respectively, which I guess makes this a situationship now.

The theme was experience innovation in an era of AI. The real message wasn’t really about AI. It was about people. It was about how human beings are changing faster than the experiences designed for them. It was about why, in this new era of increasingly ‘intelligent’ machines, the most important competitive advantage may be the most human one of all…empathy.

What follows is a summary of my presentation. The team over at UserTesting was also kind enough to publish the San Francisco session online and is free to view (I hope you love it!)

We are living in a moment when every company is trying to figure out how to use AI to move faster, produce more, spend less, automate more, and impress Wall Street along the way. And yes, AI will absolutely change how we work. But what I see in my work, is most organizations are limiting AI’s potential to do what they did yesterday, only faster, cheaper, and at scale tomorrow.

That’s not transformation though. That is operational déjà vu (I had to look up where the accents go) with more intelligent software.

The innovators do and will use AI differently. Yes. They use it to automate the parts of yesterday that desvere a place in the evolved enterprise. They also use it to do what they could not do yesterday. They use it to discover what they did not know was possible. They use it to augment, not automate or replace, human creativity, intuition, and capacity.

AI = Augmented Intelligence

I asked the audience to think about AI not just as artificial intelligence, and certainly not just as generative automation, but as “augmented intelligence.” AI can summarize. AI can synthesize. AI can identify patterns. AI can speed up insight. But AI cannot care. AI cannot sit with a customer’s frustration and feel the heat rising in its own chest. AI cannot watch a user (aka a human being) struggle with a workflow and suddenly feel responsible for making it better.

Remember…

AI cannot build intuition.

AI cannot feel understanding.

AI cannot internalize empathy.

And intuition, understanding, and empathy is where true experience design begins.

This is why X: The Experience When Business Meets Design feels even more relevant today than when it published in 2015. Back then, I was trying to make the case that experience wasn’t a department, a metric, a journey map, or a design sprint. Experience was something much more human. It was a memory. A visceral memory. Something you feel. Something you recall. Something you share.

And there are really only two types of experiences people remember: the ones that are wonderful and the ones that, well, suck.

Everything in between is forgotten. And yet, that’s exactly what we design for…transactional moments. Unfortunately, most businesses design for the middle. Not intentionally, of course. I don’t believe teams sit in conference rooms saying, “How can we make this forgettable?” Although, to be honest, some customer journeys do make you wonder.

Forgettable is what happens when companies optimize for transactions instead of feelings. It happens when they measure completion rates but not emotion or memories. It happens when they design for internal processes instead of human moments. It happens when executives believe they’re delivering “great experiences” while customers quietly, and sometimes not so quietly, disagree.

The Customer’s Experience Begins with an Apostrophe

We are not our customers.

That was one of the central ideas in the keynote, and it may be more important now than ever. We say “customer experience,” “user experience,” “brand experience,” and “employee experience” as if the phrase itself makes us customer-centered. It doesn’t. So I asked the audience to add one small thing.

An apostrophe S, “’s.”

It’s not customer experience.

It’s the customer’s experience.

It’s the user’s experience.

It’s the employee’s experience.

The experience is theirs. And it’s yours to design, not leave to chance.

That tiny grammatical shift changes everything. It moves the center of gravity from us to them. It reminds us that the experience does not belong to the company. It belongs to the person living it.

And that person has changed.

Modern Consumers are Digital Narcissists

The modern customer is not just digital-first anymore. They are mobile-first, app-shaped, AI-curious, algorithmically trained, emotionally overloaded, and increasingly impatient. I call them Generation Novel, not because of age, but because of shared interests, behaviors, empowerment, and impatience. They are the product of the attention economy, the introvert economy, the AI economy, and a world that has made convenience and personalization feel like a birthright.

We did not set out to become digital narcissists. It just happened. Originally, I referred to us as “accidental narcissists” because every device, every app, every feed, every notification quietly taught us that we are the most important person in the world.

Just ask any influencer or creator.

Our phones learned us. Apps anticipated us. Algorithms programmed us. Maps reassured us. Delivery apps showed us the little car creeping toward our house so we wouldn’t contact customer support demanding a refund because our tacos were four minutes late.

That map wasn’t just a feature. It was empathy disguised as UI.

Years ago, when working around companies like Uber, the real competitor wasn’t always another ride-sharing company. The real competitor was impatience. How long is too long before someone opens another app? That is the experience question. That is the user psychology question. That is the business question.

The same thing happens with drone delivery. The first time a drone drops something at your house, it feels like science fiction. By the second or third time, if it’s late, you’re looking at your watch. “Excuse me, my autonomous flying robot is 90 seconds behind schedule.”

That’s the world we live in.

People move from magic to entitlement much faster than most organizations move from committee to pilot.

This is why experience design has to evolve. Customers don’t compare you only to your direct competitors. They compare you to the best, most intuitive, most personalized, most invisible, most extraordinary experiences in their lives.

TikTok changes how people expect content to move. DoorDash changes how people expect transparency to feel. Apple changes how people expect objects to behave. Disney Parks change how people expect physical environments to create wonder. And now AI is changing how people expect everything to respond, predict, assist, and adapt.

You Compete with AI

You compete with AI.

Read that again.

You don’t just compete with people or companies using AI. You compete with the expectations AI creates. You compete with every experience that teaches your customers that life should be more relevant, immediate, intuitive, personal, predictive, and maybe even a little magical.

But magic doesn’t happen by accident.

In X, I wrote about experience as intentional design. Not decoration. Experience is the intentional shaping of moments that become memories.

Now, you can compete with AI, not against it, but with it…augmented with AI to do what you couldn’t do yesterday to unlock experiences not possible or imaginable before.

The Call for an Experience, not Just Brand, Style Guide

At UserTesting, I asked a simple question: Do you have an experience style guide?

I’m not talking about a brand style guide…not a UI kit…not a design system. Those are important, but they don’t go far enough. I mean a guide that defines how people should feel in each moment of truth. What should they feel when they first land on your site or when they walk in the front door? What should they feel when they open your app or when they interact with your AI or when they navigate your IVR or when a representative greets them? What should they feel when something goes wrong? What should they feel when they need help? What should they feel after they leave?

What should they remember?

What should they tell someone else?

Most organizations have customer journey maps. Fewer define moments of truth. Fewer still have designed feelings for those moments. Yet, as Cesare Pavese famously wrote, “We do not remember days, we remember moments.”

So why do we keep prioritizing the forgettable moments?

Design for IGNITE MOMENTS

This is where I introduced the idea of ignite moments. With the mobile revolution, I worked with Google to define micro-moments. Ignite moments build on that lineage. Micro-moments helped us understand intent in a mobile-first world. Ignite moments help us design meaning in an AI-first, emotionally fatigued world.

In addition to becoming an accidental narcissist, people today are stressed. They are anxious. They are overwhelmed. They are rethinking success, happiness, identity, work, relationships, and the future. They are staying in more. They are planning less. Even dating apps now feel like part-time jobs. Imagine looking for love and needing a dashboard, a profile strategy, a messaging funnel, and emotional recovery time.

What does this have to do with design?

Everything.

Because people bring all of that into the experiences they have with you. They don’t arrive as clean personas or synthetic beings. They arrive as human beings. Distracted. Hopeful. Impatient. Skeptical. Sometimes exhausted. And when your experience adds friction, confusion, or indifference, they feel it more than you realize.

Ignite moments are designed to say, “I see you,” and, “I got you.”

They are thoughtful and intentional. They convey care. They reduce anxiety. They create clarity. They add warmth. They help someone feel special, because someone designed the the moment to prove it.

This is why I believe we need more digital warmth.

Design for Digital Warmth

I used vinyl as an analogy in the keynote. Anyone who loves vinyl will happily corner you at a party and explain why it sounds better. And, in fairness, there is a warmth to analog sound that compressed files often lack. That’s what digital experiences are missing. Warmth. Texture. Feeling. Humanity.

We have engineered too many experiences to be efficient, to convert, and too few to be felt or memorable and shareable.

Digital warmth is the sixth love language. I’m sure Gary Chapman will be thrilled to learn that I’ve taken the liberty of updating his work for the digital and AI economy.

But it matters.

The old golden rule says, “Treat others the way you want to be treated.” But the idea of digital empathy, to truly design meaningful ignite moments, says, “Treat others the way they want to be treated.”

That changes the purpose and the work. It changes the research. It changes user testing. It changes what you look for when you watch the videos, study the patterns, and listen to what people are really saying underneath the words.

The Golden Rule of Digital Empathy

The goal is not just to get to insights faster.

I love speed. Every executive loves speed. Every team is under pressure to move faster. AI can absolutely help us get to insights faster. But don’t let speed strip away the very thing that makes insight valuable. Watch the videos. See the hesitation. Hear the sigh. Notice the workaround. Feel the frustration. Build intuition. Build understanding. Build empathy.

The best ideas don’t come only from looking backward at data. They come from imagining people’s future needs.

That’s the difference between optimization and innovation.

Optimization asks, “How do we make this process faster?”

Innovation asks, “Should this process exist at all?”

Optimization made websites responsive in a mobile world.

Innovation made TikTok feel native to the phone.

Optimization made ketchup bottles squeezable.

Innovation asked why we were still attacking glass bottles like they owed us money.

Yes, I talked about ketchup. The original glass ketchup bottle was designed in the 1800s and it was and still is a terrible delivery vessel. We all developed rituals to get ketchup out of it. The palm slap. The knife. The shake. The prayer. Then came squeezable bottles. Better, but still imperfect. Then came the upside-down gravity bottle. Suddenly, someone had empathy for the condiment experience.

@jordan_the_stallion8

Stich with @Adam Cowart #fypシ

♬ original sound – Jordan_The_Stallion8

That is innovation!

Everything can be reimagined when you feel the friction.

Everything is up for reimagination now…even parking signs…especially parking signs. Anyone who has tried to decipher a city parking sign while cars are honking behind them understands that design is not an aesthetic exercise. In New York, if you stop too long to read a sign, your life may be physically threatened. That’s not a customer journey. That is not an ignite moment. That’s a hostage situation!

Yet so much of the world is designed this way. Technically complete, accurate, and functional, but emotionally hostile.

This is why human-centered design must be more than a workshop. It must become a leadership practice. It must become how companies make decisions. It must become how teams define value. It must become how organizations connect brand promise to operational reality.

Design for People, not for the Spreadsheet or Dashboard

Brand is not what you say it is. Brand is the culmination of the experiences people have and share.

That was true when Yelp first gave customers a public voice and business owners reacted as if the villagers had stormed the castle. “How dare you tell people about the horrible experience I bestowed upon you!”

It is even more true now.

Customers don’t need your permission to define your brand. They define it every day through reviews, screenshots, group chats, social posts, TikToks, and increasingly, through AI agents that may one day summarize whether you are worth someone’s time.

Remember, there are only two experiences people have and remember…experiences that suck and experiences that are extraordinary. And, in both extremes, that is what people share. Which are you designing for?

The brand is the memory expressed to others.

The Concentric Circles of Experience Innovation

BX, CX, UX, and service experience are not separate planets. They are concentric circles of experience innovation. Brand experience asks how someone feels before, during, and after engagement. Customer experience brings the promise to life across the journey. User experience shapes interaction across products, services, devices, and touchpoints. Service experience orchestrates the roles, props, systems, and processes that deliver it all.

Together, they determine whether someone feels seen or sold to.

Also Measure for Experiences and Outcomes

This is why experience has to be measured differently.

We know ROI. We worship ROI. We tattoo ROI on quarterly planning decks. But experience needs a champion. It needs a metric. It needs something more meaningful. At the conference we explored the concept of RoX: Return on Experience. RoX captures more than a moment, a product, or a service interaction. It measures how someone feels overall throughout their relationship with you, and how those feelings connect to outcomes. It measures against the stated experience you said you would design and deliver in ignite moments, how they’re felt in those moments, what they sparked (or ignited) as a result, and how they’re shared.

Experience is not separate from growth. It is the source of growth.

When people feel good, they stay. When they stay, they buy again. When they buy again, they tell others. When they tell others, they create advocacy. When they trust you, they give you permission to innovate with them.

The opposite is also true. When companies ignore experience, the “I” in ROI changes meaning.

It becomes return on ignorance.

It’s the cost of not knowing your customer. It’s the cost of treating those ignite moments, those moments of truth, as cost centers. So, what is the cost of designing for assumptions? What is the cost of friction? What is the cost of indifference? What is the cost of forgettable moments? What is the cost of a customer who leaves quietly because nothing was bad enough to complain about, but nothing was good enough to remember?

That is the silent killer of growth and innovation.

This is why experience leaders must change the narrative inside the organization. Designers, researchers, product leaders, marketers, and CX teams have to speak the language of business without losing the language of humanity. Think like a venture capitalist. Think like an investment banker. Make the case for experience not as a cost center, but as a growth engine. Connect UX and CX to acceleration, loyalty, premium pricing, advocacy, retention, and innovation.

But don’t stop there. Humanize the customer.

Bring executives into their world. Show them the moments that matter. Let them feel the friction. Let them watch the struggle. Let them hear the language customers use. Then translate that empathy into economic value.

That is how experience earns its seat at the table.

Change the Narrative: How to Elevate Experience to the C-Suite

In the AI era, having a seat at the table becomes even more important. As more teams use AI to generate content, build interfaces, synthesize research, and automate engagement, sameness will flood the market. AI will make average easier. It will make mediocrity scalable. It will make “good enough” dangerously abundant.

While helpful, synthetic persona engagement offers no opportunity for empathy.

Human-centered experience design is how you escape the sea of sameness.

This is the renewed relevance of X. The book was never just about design. It was about business meeting design at the intersection of behavior, emotion, technology, and memory. It was about the idea that experience is not what happens when someone clicks. It is what happens when someone feels.

When I wrote X, I even designed the book itself as an experience. I asked what a book might look like for someone whose brain had been rewired by mobile devices. What if a printed book had to behave a little more like an app? What if readers needed to jump in and out, move nonlinearly, follow color cues, and experience the ideas visually, emotionally, and spatially?

That was the point. The medium had to embody the message.

Today, that message matters even more.

AI is going to accelerate everything. But acceleration without empathy only gets you to the wrong destination faster. Automation without imagination only scales yesterday. Intelligence without humanity becomes forgettable.

Remember Why You Started

So maybe this isn’t about artificial intelligence at all.

Maybe it’s about remembering what purpose intelligence serves here…and I don’t mean artificial intelligence. I mean human and emotional intelligence (EQ).

It is for understanding. It is for creating. It is for caring. It is for seeing what others miss. It is for designing something better because you felt the problem deeply enough to no longer tolerate it.

If you’re waiting for someone to tell you what to do, you’re on the wrong side of innovation.

Start with people.

Watch them. Listen to them. Learn from them. Ask what they value now. Ask what makes them anxious. Ask what gives them relief. Ask what makes them feel special. Ask what they avoid. Ask how they define success, happiness, premium, trust, and worth. Ask what they wish worked differently. Ask what they have stopped expecting because disappointment trained them to lower their standards. Observe them. Feel where they struggle or hit friction or feel under-appreciated or overlooked. See where they hit deadends.

Then raise those standards for them. Innovate.

Design the ignite moments. Add digital warmth. Build trust. Deliver wow. Make the experience native, personal, intuitive, and meaningful…because I promise you, someone on the other side will feel it. And they will remember. And they will share it.

Experience Design is How You Make People Feel and What It is They Remember

In the end, Maya Angelou said it best:

“People may forget what you said.

People will forget what you did.

But they will never forget how you made them feel.”

That is experience.

That is design.

That is innovation.

And in an age of AI, it may be the most human advantage we have left.

I’ll leave you with this…

Infinite ∞ | Mindshift | Subscribe | Keynote Speaker

The post In the Era of AI, Human Experience is the Killer App appeared first on Brian Solis.

We proudly launched ServiceNow Futures this year. We are a future-forward team of futurists, innovation officers, and futures engineers inside ServiceNow. Our mission is to help customers “see further and act sooner.”

As part of our charter, we translate emerging signals and trends into actionable insights for C-Suites. Our research, ourthinking, our customer-facing work leads to executive-level frameworks, reports, webinars and workshops, everything business and technology leaders need to guide the decisions they need to make now for a world that doesn’t exist yet…to proactively shape their future.

Today, I’m proud to share the release of my latest Futures Report, “AI Cultures and Agentic Darwinism: Why survival of the fittest starts with a leadership mindshift.” You can download it immediately (no email gate or paywall).

So, let’s dive into it!

As part of our charter, we translate emerging signals and trends into actionable insights for C-Suites. Our research, ourthinking, our customer-facing work leads to executive-level frameworks, reports, webinars and workshops, everything business and technology leaders need to guide the decisions they need to make now for a world that doesn’t exist yet…to proactively shape their future.

Today, I’m proud to share the release of my latest Futures Report, “AI Cultures and Agentic Darwinism: Why survival of the fittest starts with a leadership mindshift.” You can download it immediately (no email gate or paywall).

So, let’s dive into it!

The Four AI Cultures Defining the Future at Different Speeds

Something is happening beneath the surface of every AI strategy.

On paper, many companies look like they’re moving in the same direction. They have AI roadmaps. Pilots. Copilots. Centers of excellence. Governance in motion. Productivity targets. Executive mandates. But underneath all that activity, organizations are beginning to diverge. In fact, for three years now, we’ve tracked the maturity of organizations as they evolve with AI as part of our AI Index report. That’s a separate story, but I would love to share how we developed and scaled one of the industry’s first comprehensive AI maturity models!

But getting back to it, some companies are using AI to make today’s business faster, cheaper, and more efficient. Others are using AI to rethink how the business should work in the first place. Some are asking AI to help employees complete old tasks. Others are redesigning workflows so humans and agents can create entirely new forms of value together. And some are reinventing what a company could be with AI at the core…from the beginning.

This is creating a quiet separation leaders can no longer afford to miss.

The most dangerous mistake in this moment is assuming that all AI adoption is progress. While positive, it isn’t. Activity can look like acceleration while the organization remains culturally stationary. A company can deploy tools everywhere and still be optimizing a business model that is losing relevance. It can generate efficiency and still fail to create momentum or new value. It can produce productivity gains and still fall behind competitors that are reinventing the work itself.

AI Darwinism Starts With Culture

This is where AI Darwinism begins.

As AI agents gain the capacity to reason, decide, act, and learn across workflows, the market starts to reward a different kind of company. It’s not necessarily the one with the biggest AI budget…not the one with the most pilots…not even the one with the most sophisticated technology.

The advantage shifts to the organization with the culture to adapt.

Culture determines what leaders believe AI is for. It determines which questions teams ask, which risks they tolerate, which investments get funded, which workflows get redesigned, and which possibilities are dismissed before they are ever explored. Culture is the invisible operating system behind every AI strategy.

That is why, in our new ServiceNow Futures report, AI Cultures and Agentic Darwinism, developed with advisory from my old friend and former Altimeter colleague, now General Partner at Blitzscaling Ventures, Jeremiah Owyang, we studied AI not just as a technology trend, but as an evolutionary force.

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AI has a trust problem because they understand the future they were asked to accept.

For the last three-to-four years, AI was introduced less as a platform for human potential and more as a force of inevitability: jobs would disappear, entry-level opportunities would shrink, organizations would flatten, humans would be removed from the loop, and efficiency would become the new north star. That story did not write itself. It was shaped across boardrooms, classrooms, campaign stages, media headlines, investor calls, and frontier labs.

CEOs framed cuts as transformation. Investors rewarded efficiency over reinvention. Educators policed AI as cheating before teaching students how to think with it. Policymakers debated control before building safeguards and capability. Frontier leaders warned of disruption without designing a more human path forward.

So when people resist, distrust, boo, organize, and question whether this future includes them, we should not mistake that for ignorance. It is feedback. The AI trust crisis cannot be solved with better messaging, narrative, or PR. It requires a new kind of leadership, one that stops using AI to make yesterday cheaper and starts using it to make people, work, and growth more human.

When the Future Gets Booed

Of course graduates boo commencement speakers.

At the University of Arizona, former Google CEO Eric Schmidt was met with boos when he raised AI and its effects during a commencement address. He was speaking to a generation that has heard, again and again, that the future they are entering may have less room for them.

At the University of Central Florida, Gloria Caulfield told arts, humanities, communications, and media graduates that “artificial intelligence is the next industrial revolution.” Students booed. One graduate explained the response plainly: “I don’t think that kids are having a hard time accepting it because we know that AI exists. I think we’re just having a hard time acknowledging that it’s taking away job opportunities from us.”

Axios called it a new graduation ritual: booing AI. The same report noted a Glendale Community College moment where an AI system skipped students’ names and the president blamed the technology.

Then came Ronny Chieng at Harvard. In a Class Day speech, he exclaimed, “F AI,” and told graduates their mission was to “destroy AI.” The crowd roared its approval. But beneath the joke was a deeper truth. Chieng wasn’t rejecting medical breakthroughs or scientific progress. He was pushing back on a world where AI is used to smooth away the messy, meaningful, human work of creativity, learning, and effort.

The Signal Beneath the Backlash; Trust is Falling

The pushback is not anti-technology. It is anti-erasure and anti-irrelevance.

And the data says the same thing.

Pew Research Center research found that AI usage is rising, but trust is falling. About half of U.S. adults now use AI chatbots, and 38% of employed adults say they use chatbots for work tasks. ChatGPT alone has reached 44% of U.S. adults.

Yet the same study found that Americans are deeply skeptical of AI’s broader impact. Forty percent say AI will have a negative impact on society over the next 20 years, compared with just 16% who expect a positive impact. For their own lives, 31% expect a negative effect versus 23% positive.

The concern cuts even deeper. Sixty-three percent of Americans say AI is advancing too quickly. Seventy-one percent believe the increased use of AI will make their personal information less secure. Sixty-seven percent have little to no confidence in the U.S. government to regulate AI effectively, and about six in ten are not confident that U.S. companies will develop and use AI responsibly.

It’s hard not to feel that if you don’t already.

Adoption Without Consent Becomes Resistance

The Wall Street Journal framed the backlash as an “American rebellion against AI,” connecting booed commencement speakers, blocked data centers, plummeting poll numbers, protests, political fights, energy concerns, job fears, and growing community resistance. Its reporting noted that voters in Festus, Missouri, ousted four city council members shortly after they approved a $6 billion data center, that roughly 360,000 Americans were in Facebook groups opposing data centers, and that local opposition blocked or delayed at least 48 data center projects valued at about $156 billion last year.

This is what happens when leaders sell inevitability instead of agency at a speed no one was ready for.

This is what happens when the public hears, over and over again, that AI will be used to reduce headcount, automate jobs, cut costs, and accelerate productivity, but rarely hears a credible plan to create new value, new roles, new mobility, and new futures.

You reap what you sow.

And for too long, the AI story has been sown in fear.

You Reap the Narrative You Sow

A growing number of companies have publicly cited AI efficiencies as a rationale for layoffs. Business Insider recently tracked 16 companies that have announced AI-related staff cuts or restructurings, while also noting the possibility that some companies may be using AI as a convenient explanation for cuts they would have made anyway (or at least to cover the costs of AI tech and infrastructure, not because AI could take over jobs.)

AI does not walk into a company and eliminate a job.

Leaders do.

Boards do.

Investors do.

AI can perform tasks. It can summarize, analyze, classify, generate, recommend, simulate, reason, and increasingly act within boundaries. But a job is more than a bundle of tasks. A job is context, judgment, accountability, relationship, craft, care, ethics, taste, presence, and trust. A job is also agency, identity, mobility, contribution, belonging, and hope.

When leaders over rely on AI investments and reduce work to tasks, people become costs.

When people become costs, the only ROI for AI becomes cutting costs.

You’ve heard the saying, “when you’re a hammer, everything looks like a nail.” When it comes to AI automation, that phrase can be adapted, “When you’re an axe, everything looks like headcount.”

This is why trust collapses. And this is when leaders fail. Failures mount when leaders use an exponential technology with an elimination mindset.

The Old Playbook is Still the Problem

Executives, managers, teachers, parents, mentors, we need to become the leaders we are waiting for.

You can’t automate your way to innovation.

You can’t cut your way to growth.

Yet so much of the AI conversation remains trapped in yesterday’s business logic: reduce costs, increase efficiency, do more with less, focus on the quarter, win over the street. While it looks good on a spreadsheet or an executive dashboard, it does not build belief or instill trust over the long term. It does not inspire people to learn. It does not invite or empower workers, students, customers, or communities into the future.

It does however, create resistance. And that’s where we are.

Researchers are seeing early evidence that AI is starting to hurt entry-level jobs, especially in roles where AI can perform the same tasks junior employees used to do. Stanford Digital Economy Lab researchers found results “consistent with the hypothesis that generative AI has begun to affect entry-level employment.”

AI is not affecting all jobs equally. It is putting the most pressure on the first rung of the career ladder, especially in work where companies can use AI to do what junior employees used to learn by doing. If leaders automate the entry level without redesigning how people learn, grow, and contribute, they do not just cut costs. They break the talent pipeline.

Cengage describes the emerging experience gap painfully and honestly clear: entry-level jobs have long been the training ground where people build judgment, communication, professional confidence, and practical skills. But as AI absorbs routine early-career tasks, employers increasingly expect junior workers to perform more analytical and judgment-based work earlier, sometimes asking them to evaluate AI outputs before they have learned the underlying work themselves.

At the same time, employers are rapidly shifting expectations. NACE reports that more than one-third of entry-level jobs now require AI skills, nearly triple the share from fall 2025, and 28% of employers say they are seeking early-career talent who can use AI in their work.

No Wonder People are Angry (and Scared)!

So imagine being a student.

Your professors told you not to use AI because it was cheating. Employers now expect you to use AI because it is essential. Executives tell you AI will create opportunity, while headlines tell you companies are cutting jobs because of AI. Then commencement speakers tell you to embrace the revolution.

The boos are feedback.

They are saying: You have not earned our trust. And this isn’t only a student problem. It is an institutional problem.

AI Should Expand Thinking and Capacity with People

Futurism recently highlighted a small UK executive survey that found 62% of executive respondents said they use AI to make “most decisions,” while 46% said they rely on advice from AI more than from their own colleagues.

The piece also referenced research from Carnegie Mellon and Microsoft suggesting that knowledge workers who trust generative AI’s accuracy may show lower propensity for critical thought. I mean…WTF (what’s the future!?)

The sample is limited and the tone is pointed, but the irony is important: some of the same leaders asking workers to trust AI may already be outsourcing too much of their own thinking to it.

That isn’t leadership. This is cognitive surrender and AI abdication.

AI should expand our thinking, not replace it. It should challenge assumptions, not launder them. It should help leaders explore more possibilities, not avoid responsibility for the choices they make.

And that takes…leadership. From above…from the side…from within.

People are Still the Strategy

Jensen Huang made an important distinction on Memos to the President. He argued that AI automates specific tasks, such as coding or reading scans, but does not replace the human purpose of a job: innovation and problem-solving. In his framing, AI can increase demand for software engineers and radiologists by supercharging productivity, not by erasing the need for human expertise.

That is the narrative we should have been building all along.

AI is not the strategy. People are still the strategy.

AI is a capability. For leaders who lead it, it’s a force multiplier, a new layer of intelligence and agency that can help people do what was not possible yesterday. But this is only true if leaders choose augmentation over automation…only if they redesign work before they redesign the workforce…only if they build new ladders before they remove the first rung.

This is where the mindshift has to begin.

From Finite Automation to Infinite Possibility

A finite company uses AI to make yesterday cheaper.

An infinite company uses AI to make tomorrow possible.

www.infinitecompany.ai

The difference is not technology. It is leadership. And it’s leadership at every level…as parents, teachers, mentors, managers, leaders.

So, it’s not just “finite leaders,” it’s everyone who hold back progress, whether they realize it or not.

What’s the opposite in this case? It’s infinite…

An infinite company does not start by asking, “How many people can we replace?” It asks, “What new value can we create?” It asks, “What work should humans no longer have to do?” It asks, “What can people become capable of when routine work is offloaded and human imagination is amplified?” It asks, “What outcomes were impossible before AI that are now within reach?”

That is how we change the conversation from fear to agency.

And we need to change it now.

How Leaders Can Earn Back Trust

First, leaders have to stop using AI as a euphemism for layoffs.

If AI is part of a restructuring, say exactly how. Explain what work is changing, what value is being created, what skills are needed next, what investments are being made in people, and how displaced capacity will be reinvested. Do not hide behind inevitability. If the choice is cost reduction, own it. If the strategy is growth, prove it.

Second, redesign work around outcomes, not org charts. Think automation + augmentation. You have to redesign work before you redesign the workforce.

When AI automates work, it replaces tasks that entry-level workers often handle: drafting, coding basic features, research, customer support, analysis, admin work, etc. In those areas, employment for younger or less-experienced workers appears to be declining.

When AI augments work, it helps people do their jobs better rather than replacing the work outright. In those cases, the negative employment effect is weaker or less obvious.

The org chart tells us who reports to whom. It does not tell us how value is created. Leaders need work charts that map outcomes, tasks, handoffs, data, decisions, human judgment, AI agents, risk controls, and feedback loops. AI transformation is not about sprinkling tools across departments. It is about reimagining how work flows.

Third, rebuild the entry-level ladder.

If AI absorbs the routine work that once trained people, then organizations need new apprenticeships. Early-career workers should learn how to collaborate with AI, interrogate its outputs, understand the underlying work, develop judgment, and contribute to real outcomes faster. Do not eliminate the training ground. Reinvent it.

Fourth, make agency and augmentation measurable.

Every AI initiative should answer four questions: What human capacity did we unlock? What customer or employee outcome improved? What new capability did we build? What did we reinvest in people, products, services, or growth? Cost savings alone is not transformation. It is accounting.

Fifth, teach students how to think with AI, not surrender thinking to it.

Education cannot treat AI only as a cheating problem. Students need to learn how to question AI, use it as a collaborator, detect errors, challenge bias, build original ideas, and protect their own critical thinking. The future does not belong to students who outsource their minds. It belongs to those who augment them.

Sixth, include more voices in AI decisions.

Workers. Students. Customers. Artists. Teachers. Communities. Local officials. Privacy experts. Labor leaders. People who will live with the consequences. When AI is designed in closed rooms and deployed into open society, backlash is not a bug. It is the system responding.

Finally, leaders have to earn trust in public.

Prove it in public.

It’s ok if you don’t have the answers. There is no playbook. The problems compound when you try to use yesterday’s playbook to navigate an uncharted future. You have to ask different questions and do the work to answer them.

That’s leadership. So, lead.

Show the jobs created. Show the roles redesigned. Show the people reskilled. Show the guardrails. Show the impact. Show the harms prevented. Show how AI is helping humans become more capable, not just making organizations more efficient.

This is the leadership audition of our time. No pressure!

The leaders of tomorrow will not be remembered for how quickly they cut.They will be remembered for how courageously they imagined.

They will be remembered for turning fear into fluency, fluency into capability, capability into creativity, and creativity into new value.

The AI backlash is real. But it is not the end of the story. It is an invitation to write a better one.

Remember, people are not asking us to stop the future. But they are asking to be included in it. They are asking for a future where AI does not diminish human potential, instead, it expands it. It’s a future where students are not told their degrees are obsolete before they even begin, a future where workers are not treated as costs to be optimized away, a future where leaders do not confuse automation with innovation or efficiency with progress.

This is our moment to wonder again.

Ask better questions.

Design better systems.

Build better companies.

Become better leaders.

Let’s stop asking people to accept a future they were never invited to help shape.

Let’s build it together.

Infinite ∞ | Mindshift | Subscribe | Keynote Speaker

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What a wonderful experience! I had the opportunity to keynote Western Digital’s “Built to Win” Sales Leadership conference in beautiful San Diego. The message to explore “how to win” with AI as a force multiplier not an automator. Everyone received a copy of Mindshift too! What an unforgettable experience!

  Book Brian to keynote your next event!

Read Infinite: How Visionary Leaders Transform Today’s Businesses into AI-Forward Companies.

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Brent Leary and I have been friends for many, many years. So whenever we have the chance to sit down for a conversation, it never feels like an “interview.” It feels like two longtime friends exploring what’s next, asking bigger questions, and challenging each other to think beyond the obvious.

In this conversation, Brent and I explored ideas from “Infinite,” the new book Dave Wright and I wrote to help leaders rethink what enterprise organizations can become in an AI-forward world.

The question is no longer, “How do we use AI to become more efficient?”

That’s important, of course. But it’s also defensive.

The bigger question is: How do we use AI to create new value, new capacity, and new possibilities for growth?

That is the difference between a finite company and an infinite company.

Finite companies use AI to optimize yesterday’s work. They automate legacy processes, reduce costs, and improve productivity within the same operating models.

Infinite companies use AI to reimagine how work gets done. They reverse-engineer workflows for 2030 and beyond. They move from org charts to work charts. They think about people and agents working together in new ways to drive exponential outcomes.

In our conversation, Brent and I explored several ideas shaping this next chapter of enterprise reinvention:

• Why companies must move from playing defense with AI to playing offense
• Why you cannot automate your way to innovation
• How cognitive Darwinism changes the way we think about work, learning, and competitiveness
• Why leaders need to ask whether today’s workflows are worthy of 2030
• How AI-native companies think differently about ROI, risk, and exponential growth
• Why the “SaaS apocalypse” is really a warning about future tech debt and AI slopware
• How technology is moving closer to business operations, not farther away from them
• Why HR and IT will need to work together to design the future of people, agents, roles, and workflows

One of the most important shifts we discussed is that innovation does not mean simply applying new technology to old processes. Innovation means creating new value.

That requires a different mindset, a different set of questions, and a different kind of leadership.

This is also where my work in digital anthropology and futurism comes together. Technology does not just change systems. It changes behavior. It changes expectations. It changes how people work, how customers engage, and how organizations compete.

AI is accelerating all of that.

And yet, many companies are still looking at AI through the lens of productivity gains, cost reduction, and short-term efficiency. That is understandable, but it is not enough.

As Brent and I discussed, the companies that thrive in this next era will be the ones that use AI to increase capability, capacity, and imagination. They will rethink roles. They will break down work into tasks and outcomes. They will design new human-agent ratios. They will ask what work should become, not just how today’s work can move faster.

That is the foundation of the infinite company.

An infinite company is not just AI-enabled.

It is AI-forward.

It is designed to learn, adapt, reinvent, and grow continuously.

Thank you, Brent, for the friendship, the thoughtful conversation, and the many years of pushing the future of business forward with curiosity and heart.

Please watch the conversation here

Infinite | Mindshift | Subscribe | Keynote Speaker

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Brian Solis shared a cautionary tale in Forbes about what happens when executives replace people with AI vs. empowering them with AI. His article explores the stories of Ford and Klarna and what CEOs should do instead of replacing people with AI.

Summary

Ford initially replaced quality inspectors with AI, leading to significant quality issues and a realization that AI alone couldn’t replicate crucial human judgment. The company subsequently rehired 350 experienced technical specialists, or “gray beards,” to mentor staff and enhance AI systems, acknowledging AI’s effectiveness depends on the knowledge it’s trained with. This strategic pivot resulted in Ford topping the J.D. Power Initial Quality Study. Similarly, Klarna, after touting AI efficiency, reinvested in human customer service, recognizing AI brings speed but humans provide essential empathy. The overarching lesson is that AI should amplify human expertise and redesign work for value creation, fostering human-agent collaboration rather than simply replacing personnel.

Read the full article at Forbes.

The post Forbes: Ford Offers a Powerful Lesson and Story About Replacing People with AI and Learning from It appeared first on Brian Solis.

You can’t cut your way to growth. You can’t automate your way to innovation.

AI is forcing every organization to reveal what it really believes about people.

Do leaders see AI as a way to shrink the company into a more efficient version of yesterday? Or do they see it as a way to expand the organization’s capacity to learn, imagine, decide, create, and grow?

That is the fork in the road.

I call this moment AI Darwinism: the new competitive reality where advantage goes to the most adaptive, not the most automated. The winners will not be the companies that replace people fastest. They will be the companies that learn faster, augment better, and use human + artificial intelligence to create new value before the market forces them to.

That is why the real ROI of AI is no longer just Return on Investment. It is Return on Intelligence.

Return on Investment asks: What did we spend, and what did we save?

Return on Intelligence asks: What did we learn? What did we unlock? What new capacity did we create? What new value can we now deliver that was impossible, impractical, or invisible before?

You cannot automate your way to innovation and growth.

Automation can make yesterday more efficient.

Augmentation is how we invent what comes next.

Automation is Useful, Automation as Strategy is a Trap

Let’s get this out of the way: automation is not the enemy. It is necessary. It is just not absolute.

Some work should absolutely be automated. Manual drudgery should be reduced. As ServiceNow CEO Bill McDermett says, we need to take the soul crushing work out of our day-to-day potential.

Employees should not spend their best cognitive hours copying, pasting, reconciling systems, searching for information, formatting decks, summarizing meetings, or navigating workflows that should have been redesigned years ago. Customers should not wait in a queue for answers a system can responsibly deliver in seconds. AI can and should remove friction from work.

I think we can all agree here.

But automation becomes dangerous when it graduates from tactic to strategy.

That is when the boardroom conversation narrows, and as a result, the strategy and what good looks like narrows. The imagination collapses into a spreadsheet. AI becomes a cost-cutting mechanism: fewer people, fewer hours, fewer costs, fewer steps, fewer “inefficiencies.” The organization looks more productive on paper, but it may also become less capable, and more so, less competitive in practice.

Harvard Business Review recently framed the choice directly: CEOs are deciding whether their AI strategy is primarily about improving the bottom line through automation and headcount reduction, or growing the top line through augmentation and innovation. The authors argue that automation may produce faster early gains, but augmentation has greater long-term potential because it builds capability, trust, learning, and new productive capacity.

Yet, the 2025 Indeed Workforce Insights Report, which surveyed ~80,000 workers across eight countries, found that the time saved with AI was largely spread across “more of same tasks.” Any potential for augmentation, for example, innovation/creative work and relationship management, didn’t even break the top five use cases.

As the others of the Havard Business Review observed,” It’s easier for executives to imagine using AI to streamline what people already do than to reimagine how it might be used to produce entirely new value.”

That is the strategic conversation leaders should be having.

Not “How many jobs can AI replace?” But “How much more value can our people create because AI exists?”

Not “How do we use AI to do the same work with fewer people?” But “What work should exist now that intelligence is becoming abundant?”

That is the difference between using AI to optimize the past and using AI to invent the future. The challenge is that executives may not know the difference.

Jensen Huang’s Lesson: Tasks Are Not Jobs

Jensen Huang offered one of the clearest executive explanations of this distinction on SCSP’s Memos to the President podcast.

He warned against the kind of fear-based AI rhetoric that tells young people not to become radiologists, software engineers, or knowledge workers because AI will allegedly erase those professions. His point was that if society convinces people not to enter fields where we will actually need more human capability, not less, that pessimism becomes self-defeating.

The task of a job and the purpose of a job are related, but they are not the same.

If you define a software engineer as someone who types code, then yes, AI looks like a replacement technology. If you define a software engineer as someone who solves problems, designs systems, translates possibility into functionality, and turns imagination into infrastructure, then AI becomes a force multiplier.

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

Huang used the example of code. If we assume the economy only needs a fixed amount of software, then AI writing code seems to imply fewer engineers. But Huang challenged the premise itself. Why assume we only need the same amount of software? Why assume the world only needs one billion lines of code when we could need a trillion…across healthcare, science, manufacturing, retail, energy, education, and entirely new domains of human possibility?

At NVIDIA, Huang said AI now does much of the coding, yet the company is hiring more engineers than ever. The reason is not paradoxical. Well, it may seem counterintuitive in this

It is the point. When AI takes on more tasks, the ceiling for ambition rises. More problems become addressable. More experiments become feasible. More ideas become buildable.

That is Return on Intelligence.

The goal is not to preserve or even optimize every task. The goal is to elevate the purpose of the work.

The Hidden Cost of a Replacement Signal

AI strategy is not just an operating decision. It is a signal.

Employees are watching how leadership talks about AI. They are listening for whether AI is being introduced to help them become more valuable or to make them more disposable. They are reading between the lines of every pilot, every reorg, every “efficiency initiative,” every platitude that promises AI will “free people up” without explaining what they will be freed to do.

HBR’s research found that only 44% of surveyed employees said their organization had formally announced AI plans. Overall, 62% believed their organization was using AI to augment employee capabilities, while 34% believed AI was being used to automate work and reduce costs. The gap grows more revealing by level: 81% of senior leaders believed their organization was all-in on augmentation, while only 53% of individual contributors perceived augmentation and 40% suspected automation.

The perception gap is breathtaking. To be honest, it’s business as usual. And, I call it the iteration gap. It’s the gap between maximizing the past and imagining the future. Coming from Silicon Valley, the past wasn’t even in the conversation. We weren’t ever asking, what are we doing that can be better tomorrow? We were always saying, not really asking, “WTF!?” aka “What’s the future…!?” It was always a directive.

We weren’t ever asking, what are we doing that can be better tomorrow? We were always saying, not really asking, “WTF!?” aka “What’s the future…!?” It was always a directive.

Leaders may think they are driving transformation, but they are instead using AI to optimize the past…without first assessing whether or not the past was worthy of bringing forward to the future. It’s the difference between iteration and innovation. Iteration makes yesterday less expensive, faster, more efficient. Innovation is doing what you didn’t do yesterday, to create new value.

In that same vein, automation is using AI to optimize yesterday. Augmentation is collaborating with AI to do what was not possible without AI and what AI couldn’t do without people. Simple said, using AI to do yesterday’s work is not augmentation. But in both cases, iteration and automation, can appear as innovation and augmentation because you’re using new technology to work in new ways. If the workflow and outcome are optimized, it’s not innovation or augmentation. And to be clear, that’s cool. You need iteration. You need automation. You just can’t innovate or create new value without using AI or any technology and/or mindset to explore the unknown and unlock what wasn’t visible or possible before.

If you do not communicate this vision or intent and purpose or goals, you do not achieve anything other than a better yesterday. Then there’s the narrative that AI is a replacement engine, that people matter less than AI’s capacity to replace headcount, salaries, and all other employee costs. For the record, that is an unproductive, and basic, narrative. Come on. Are we talking status quo here or leadership?

When employees believe AI is being deployed to replace them, trust erodes. Adoption becomes a metric. People may comply, but they do not commit. They use AI because they are told to, not because they believe it according to a vision. But they believe it will help them grow. HBR describes this as the difference between employees becoming “pilots” of AI and becoming passengers along for the ride. When people are passengers, engagement remains shallow, guidance is unclear, and AI output can devolve into “workslop,” low-effort, low-quality AI-generated work that adds noise instead of value.

This is one of the great overlooked risks in enterprise AI. Poor AI strategy does not just produce bad outputs. It produces defensive humans. And defensive humans do not transform companies.

The same HBR article points to three dynamics that CEOs and boards need to hear…

First, the threat of layoffs undermines well-being, which in turn affects productivity, retention, and talent attraction. Second, poorly integrated AI workflows create confusion, shallow adoption, and more workslop. Third, cost-cutting AI strategies can hollow out junior roles and future innovation.

A company can look efficient in the short term while becoming less capable over time.

The Leadership Mandate: Turn Automation Savings into Augmentation Capital

This is where AI strategy has to graduate from pilots, dashboards, and efficiency conversations into a new operating discipline.

If automation creates savings, time, money, resources, leaders have to decide where those savings go. Do they fall straight to the bottom line? Do they disappear into the machinery of more meetings, more output, more dashboards, more pressure, and fewer people? Or are they intentionally reinvested into new capabilities, new customer value, new roles, new skills, new services, and new business models?

Think of it this way…if you invest in automation, which frees up people’s time, where will the ROI of AI come from? The easy answer is to cut costs. In a world where AI automation threatens to become the new status quo, companies can hinder competitiveness. Augmentation, on the other hand, represents an opportunity to reinvest resources into new competitiveness that creates new value?

Are you taking away jobs or are you creating them?

That is the question every CEO, board, CIO, CFO, COO, and CHRO should be asking now. Because the true measure of AI is not just what it removes from the business. It is what the business becomes capable of because AI is now part of the work.

This is the missing metric in most AI strategies: reinvested capacity.

Companies are already tracking hours saved, tickets deflected, cycle time reduced, costs avoided, code generated, content produced, and tasks automated. Those numbers matter. But they mostly tell us what AI took out of the system. They do not tell us what intelligence, creativity, judgment, trust, and value were put back in.

That is the difference between efficiency and innovation, productivity and progress.

A company can save 100,000 hours and become no more innovative. It can generate more content and say less. It can move faster and still move in the wrong direction. It can automate customer service and still never understand the customer. It can eliminate junior work and accidentally eliminate the apprenticeship layer where future leaders learn judgment, context, taste, and accountability.

This is the part of the AI conversation that remains dangerously underdeveloped or under. We are measuring activity because activity is easy to count. But in AI Darwinism, activity is not the advantage…it is adaptability.

Harvard Business Review’s research shows why augmentation is not just a nicer version of automation. It is a different performance path. Automation tends to create faster, more visible gains because it substitutes AI for well-defined work. Augmentation requires deeper upfront investment in workflow redesign, capability-building, data infrastructure, management practices, and human-AI coordination. But that investment is what shifts the organization’s productive frontier.

It’s the difference between automating the past and inventing the future.

That is the strategic discipline leaders need now. Do not just ask, “How much time did AI save?” Ask, “What can we do with the time?”

Did we use it to get closer to customers?

Did we use it to experiment with new offerings?

Did we use it to improve decisions?

Did we use it to reskill people into higher-value roles?

Did we use it to redesign workflows around outcomes rather than departments?

Did we use it to make work more meaningful, or simply more demanding?

IKEA gives us an important operating example of what reframing looks like. When the company deployed it’s AI chatbot Billie to serve customers and deflect escalations, it was so successful, that it handled 47% of initial engagements. The company could have fallen into an automation trap. Deflecting almost 50% of service inquires demands a review of resources. After all, it represented 3.2 million interactions and nearly €13 million in savings. That alone would have been enough for most companies to declare victory and move on.

So the question becomes, do you cut headcount for short-term ROI gains? It’s easy to stop there. But IKEAs move went beyond efficiency. IKEA turned that efficiency into capacity.

The company did not just ask, “How many inquiries can AI handle?” It asked, implicitly, “What can our people now do that creates more value?” Ingka Group says 8,500 call-center co-workers were reskilled into areas such as remote interior design, digital retail sales, relationship-building, and complex customer inquiries; Reuters also reported that Ingka’s remote interior design channel generated €1.3 billion, or 3.3% of total revenue, with a target to reach 10% by 2028.

That is Return on Intelligence in practice.

Human + AI Collaboration = Augmented Capacity

The AI chatbot was not the transformation. The transformation was the redeployment of human capability into work that was more consultative, more relational, more creative, and more commercially meaningful.

This leadership toward augmentation: automate the work that no longer deserves human limitation, then move people toward the work where human capability matters more.

That sounds simple, but it requires a very different kind of executive vision and courage. It requires leaders to stop treating technology and people as costs trapped inside workflows and start treating them as capacity waiting to be amplified. It requires leaders to ask whether the current operating model deserves optimization or reinvention. It requires leaders to redesign work before they reduce people. It requires the CFO to see savings as investment capital, the CIO to see AI as workflow architecture, the CHRO to see skills, roles, and trust as strategic infrastructure, and the CEO to make the intent unmistakable.

Remember, employees are being todld to adopt AI. They’re trained to increase AI fluency, and that fluency is measured. So they are not just adopting AI, there are also interpreting it.

They’re asking: Is this here to help me grow, or to make me disappear? Am I being invited to become a pilot of the future, or am I a passenger in someone else’s cost-reduction plan? Is leadership using AI to build a better company, or just a smaller one?

Trust is now part of the AI stack.

HBR found that when employees believe AI is being introduced to make their work better, adoption rises through curiosity and agency rather than compliance. The organization cultivates pilots over passengers. The opposite path is just as instructive: when AI is associated with replacement, layoffs, and forced adoption, well-being declines, workslop rises, attrition increases, employer brand suffers, and leadership pipelines erode.

This is why “AI adoption” is the wrong finish line. Adoption without belief is compliance. Compliance without trust becomes status quo. Status quo at scale becomes a loss of competitiveness. And no company can transform with a workforce that is quietly defending itself from its own strategy.

The alternative is to make augmentation visible.

Like IKEA, show employees where AI is helping people move into better roles. Show how automation savings are funding training, experimentation, customer innovation, and new services. Show how exceptions are being studied as signals. Show how frontline teams are helping redesign work. Show how junior employees will still build judgment in an AI-shaped workplace. Show how leadership is protecting the learning architecture of the company even as tasks change.

This is especially important for early-career talent. If AI drafts the first analysis, writes the first version, summarizes the meeting, generates the code, answers the routine customer question, or prepares the report, then leaders have to redesign how people learn. Apprenticeship has to be built into the new workflow.

The next generation of talent should not be trained to compete with AI at the task level. They should be trained to think with AI, challenge AI, supervise AI, improve AI, and use AI to get closer to problems that matter. They need to learn judgment, context, taste, ethics, empathy, and systems thinking in new ways. If leaders fail here, they may not feel the damage immediately. But years from now, they will wonder why the organization has fewer people capable of leading through ambiguity.

This is the danger of short-term automation thinking…you’re essentially mortgaging the future.

Improve the Past While Visualizing a New Future

Automate and optimize what deserves to live in the future, not because it’s the way that things have always been done. Explore where to augment work to compete for the future, today.

That’s the mindshift.

For example, starting with pain points can trap people in a “loss minimization mindset,” where teams look for time lost, money lost, and friction reduced. The better frame is to use AI to reason, reframe, and reinvent work itself.

In our Business Transformation Mindset report, Dave Wright, Alexis Walker, and I explored distinction between the iteration mindset and the transformation mindset.

The iteration mindset asks how to do today’s work faster, cheaper, with less effort, and with greater efficiency.

The transformation mindset asks why we operate this way, what problems remain unsolved, what data and workflows are missing, and what could transform the business rather than simply fix it.

This is the bridge from automation to reinvention.

The goal is not to build a giant inventory of AI use cases that solidify status quo. That is useful, but insufficient. The work is to build a portfolio of new value cases: places where AI helps reveal unmet demand, redesign workflows, expand judgment, accelerate learning, improve customer outcomes, and create capacity that did not exist before.

This is also why Return on Intelligence needs to sit next to Return on Investment.

Return on Investment asks whether AI produced financial efficiency. Return on Intelligence asks whether AI made the organization more adaptive, more creative, more decisive, more trusted, and more valuable over time.

A useful way to think about it is:

ROI∞ = (Realized Value × Learning Velocity) ÷ (Time-to-Outcome × Resistance to Change)

We introduce this model in my new book with Dave Wright, “Infinite: How Visionary Leaders Transform Today’s Businesses into AI-Forward Companies.”

Realized value matters because AI is everywhere. Pilots, demos, copilots, agents, dashboards, and innovation labs can create the appearance of progress without changing the economics or experience of the business.

Learning velocity matters because AI Darwinism rewards organizations that learn faster than their environments change. The advantage is not simply adopting a model. It is building a company that can sense new signals, test new assumptions, absorb feedback, and improve continuously.

Time-to-outcome matters because speed without direction is just motion. AI should compress the distance between noticing a problem, understanding it, deciding what to do, acting on it, and learning from the result.

Resistance to change matters because culture can neutralize technology. Legacy incentives, fear, fragmented systems, poor data quality, unclear governance, and organizational politics can drag down Return on Intelligence faster than any model can lift it.

The AI Layoff Trap and Self-Inflicted Disruption

In their paper published in April 2026, Brett Hemenway Falk and Gerry Tsoukals explore the “AI Layoff Trap,” which can debilitate a company’s competitiveness by doing the very thing businesses have done throughout history, cut costs through automation to become more leaner and more efficient. The authors argue that “if AI displaces human workers faster than the economy can reabsorb them, it risks eroding the very consumer demand firms depend on.”

The paper’s model shows that firms can rationally over-automate because each company captures the full cost savings of replacing its own workers while bearing only a fraction of the demand destruction caused by lost wages. The result can become an automation arms race that harms workers and firm owners alike.

The paper’s “Red Queen” effect makes the warning even sharper. Better AI does not automatically solve the trap. In the model, higher AI productivity can widen the over-automation wedge because each firm perceives a market-share gain from automating beyond rivals, even though those gains cancel out when everyone does the same thing.

Think about it.

The more powerful AI becomes, the more dangerous unimaginative leadership becomes.

AI Darwinism will not punish companies for automating. It will punish companies for automating without imagination.

The winners will automate, yes. But they will also augment. They will use AI to remove friction and then reinvest the gains into intelligence. They will use agents to handle routine work and then elevate humans into higher-value judgment. They will use automation to expose unmet demand. They will use AI not simply to answer questions, but to help leaders ask better ones.

Take the time to ask different and better questions…if you don’t do it now, when will you have time to do it later?

What do our customers need that our current model cannot deliver?

What work exists only because our systems are fragmented?

What assumptions are embedded in this process?

What would we build if we were starting today?

What human capabilities become more important as AI becomes more capable?

What new value can we create now that time, intelligence, and agency are becoming more abundant?

That is why this moment is so consequential. AI is not just changing how work gets done. It is revealing the imagination, or the lack of imagination, inside the companies deploying it.

Some organizations will use AI to become faster versions of their past. They will celebrate efficiency, reduce costs, and call it transformation. Others will use AI to confront the deeper question: what should this company become now?

Automation will yield impressive savings.

Augmentation will shape the future.

That is the difference between return on investment and Return on Intelligence. And that is why you cannot automate your way to innovation and growth.

Automation can clear the runway.

Augmentation is how you learn to fly.

Infinite | Mindshift | Subscribe | Keynote Speaker

The post In an Era of AI Darwinism, ROI Now Represents Return on Intelligence appeared first on Brian Solis.

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

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

The Enterprise AI Transformation Gap

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

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

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

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

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

Let me tell you about a scenario I’ve seen play out in organizations across industries. A company invests heavily in a modern data stack. They build dashboards. They deploy predictive analytics. They launch an AI copilot that summarizes support tickets, drafts responses, and flags anomalies. Productivity improves. The board is impressed.

But then, difficult questions surface: Did cycle times fundamentally change? Did headcount models allow for growth and value creation? Did the operating model actually evolve?

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

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

The Agent Sprawl Problem Not Enough People Are Talking About

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

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

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

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

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

Why Enterprise AI Needs a Unified Platform

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

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

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

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

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

How a Unified Enterprise AI Platform Creates Value

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

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

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

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

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

The Leadership Imperative

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

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

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

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

Infinite | Mindshift | Subscribe | Keynote Speaker

The post The Future Economy: From Automation to Reinvention: Why Leaders Defining the Next Decade Are Prioritizing AI Platforms and People appeared first on Brian Solis.

Via Bloomberg

Ford rehired over 350 veteran engineers referred to internally as “gray beards” after an aggressive AI adoption strategy backfired.

Ford had been increasingly relying on AI-driven inspection systems to streamline production and address quality control issues, however the firm acknowledged that AI lacked the nuanced judgement when it came to complex problems.

“We had been relying more and more on automated quality systems and not getting the desired results,” said Kumar Galhotra, Ford’s chief operating officer.

“We brought back technical specialists and they hunt for failure points before a part ever reaches the plant floor,” he continued.

Ford won’t abandon AI, but instead will partner AI with human oversight and experience.

“Mistakenly, we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that that would produce a high-quality product,” said Charles Poon, Ford’s vice president of vehicle hardware engineering.

After rehiring proven engineers, Ford is experiencing a marked improvement in its quality standards.

According to the latest J.D. Power Initial Quality Survey, Ford ranked top among mainstream brands. This is the first time it has achieved that milestone in 16 years.

In Infinite, Dave Wright and I explore how human + AI workflows can not only improve yesterday’s standards, but also raise them.

Infinite | Mindshift | Subscribe | Keynote Speaker

The post Ford Values Human Expertise Over Machines appeared first on Brian Solis.

via Vicki Salemi, Tribune Content Agency, published in Yahoo! Tech

DEAR READER: In the age of AI, what does it mean to become “infinite?”

Inquiring minds need to know, so we checked with Brian Solis, head of global innovation at ServiceNow and coauthor of “Infinite: How Visionary Leaders Transform Today’s Businesses into AI-Forward Companies.”

“Becoming ‘infinite’ is about employing an intentional mindset where AI, in its current and evolving forms, will automate and augment work perpetually, to free up time and resources to allow constant expansion and reinvention,” said Solis. “Infinite companies automate what should move forward and augment people and work to deliver exponential outcomes and value. Augmentation is a defining instrument of infinite leadership because it expands human possibility: helping people do what was once impossible without AI, and helping AI create value that is only possible through people.”

Solis said there are specific stages when people identify with and adopt AI. “Some people are ‘AI Followers.’ They wait for proven ROI and best practices before moving. They let others take the risks. They prioritize stability and compliance over speed and possibility,” he said. “Others are ‘AI Forward,’ thinking about integrating AI alongside humans deliberately. They are genuinely exploring what work looks like when humans and AI agents collaborate, but the human is always in the loop by design.”

As for the next stage? Solis said it’s defined as “AI First,” whereby AI should be the default solution for an increasing number of tasks, with human intervention as the exception rather than the rule. They don’t aim to eliminate human input entirely. They aim to free human time so humans can provide judgment, creativity and strategic oversight on the things that genuinely require it.”

The final stage is “AI Native.” The product or service in this stage wouldn’t exist if AI wasn’t part of the equation, according to Solis. “AI is core, intrinsic, irremovable. It does not require the whole company to operate in this way, but it would allow people to operate in an AI Native way when defining new offerings or reimagining how existing or net new work flows, one workflow at a time.”

Read Infinite: How Visionary Leaders Transform Today’s Businesses into AI-Forward Companies

Book Website

The post Yahoo Tech: To infinity and beyond! How AI is reshaping the workplace appeared first on Brian Solis.

The moment this box arrived, I immediately remembered the moment when this book was just an idea shared in an email. It’s all now very real and I wanted to share this moment (and box break) with you!

Today, I’m proud, grateful, and honestly a little emotional to share that my new book with Dave Wright, Chief Innovation at ServiceNow, Infinite: How Visionary Leaders Transform Today’s Businesses into AI-Forward Companies, is now available. We would love your support and invite you to join us on this journey!

This book began with a question that kept following us into every executive conversation, every AI workshop, every boardroom discussion, and every moment of uncertainty leaders are facing right now:

What does it really mean to build a company for an age when intelligence is no longer scarce?

There are so many books, courses, talks, and articles that explore intelligence as a tool, as a co-pilot, as a solution for automation, cost-cutting and efficiency gains.

We set out to share our work in how AI is also a catalyst to rethink the company itself.

This moment is bigger than tech. It is asking us to reconsider how work flows, how value is created, how people and agents collaborate, how leaders measure progress, and how organizations evolve when change will no longer arrive in waves, but instead continuously.

That is why we wrote Infinite.

It is a book for leaders who feel the tension between the urgency of this moment and the responsibility to lead what comes next with humanity, imagination, and courage.

An Infinite Company is infinite because it is designed for boundless capability and possibility. It learns. It adapts. It grows smarter with every decision. It uses AI to elevate human value.

We believe AI should be used to reimagine work, to free people from the repetitive and the routine, to create new capacity, to unlock new forms of creativity, judgment, empathy, and invention, to help leaders build organizations that are more efficient and more plugged-in to what is possible.

This is a moment to stop optimizing the past and start designing what comes next. It is a moment to ask better questions:

• Are we using AI to improve yesterday, or invent tomorrow?
• Are we building faster versions of old companies, or new companies worthy of this moment?
• Are we treating AI as a feature, or as a foundation for business reinvention?
• Are we making people more replaceable, or more capable?

From here on out, the future will be designed.

And Infinite is here to help leaders design it. And that’s you!

Thank you for being part of this journey…for reading, sharing, challenging, and encouraging the ideas that eventually became this book.

We hope it helps you see this moment differently. We hope it gives you language for what you already feel is changing. Most of all, we hope it gives you the confidence and those around you to lead what comes next.

The Infinite future begins with us, right now.

With gratitude,

Brian

Infinite | Mindshift | Subscribe | Keynote Speaker

The post Infinite, My Latest Book, is Now Available and I Would Love for You To Read It 🥳 appeared first on Brian Solis.

The Tech Humanist Show

Listen:

Spotify

Apple Podcasts

Youtube

Are leaders thinking big enough—and human enough—in the AI era?

Explore how AI and technology shape the human experience with Kate O’Neill and guest Brian Solis, Head of Global Innovation at ServiceNow. Discover the concept of cognitive Darwinism, AI transformation stories, leadership in the AI era, and how to drive growth while staying human-centric.

Topics Covered:

AI augmentation vs. automation
Cognitive Darwinism and self-awareness
Capacity and capability overhang in AI adoption
Transformation as a human story
Purposeful iteration vs. intentional innovation
Return on intelligence vs. return on ignorance
Reskilling and workforce transformation case studies (IKEA & Walmart)
Human-centric leadership and psychological safety
Personal relationship with technology & digital attention
Mind shifts required for future-ready leadership

Episode Chapters:

00:04 Introduction & Guest Welcome
01:00 Transformation as a Human Story
02:24 The Human Story Leaders Miss in the AI Era
03:06 AI’s Anti-Human Trajectory & Cognitive Darwinism
04:28 AI Tax and Brain Fry
05:49 AIQ: Artificial vs. Augmented Intelligence Quotient
09:16 Agentic AI & Process Reinvention
11:11 Grand Strategy and Leadership Mindsets
15:55 Mind Shifts and Self-Awareness
17:18 Book Inspiration and Becoming a Leader of the Moment
20:13 Unlearning Disruption Myths in Enterprise
25:16 Innovation: Creating New Value
26:59 Evaluating AI Use: Efficiency vs. Net New Value
31:13 Psychological Safety and Human-Centric Leadership
32:28 IKEA & Walmart: Augmentation and Reskilling Case Studies
38:00 Personal Relationship with Technology & Life Scale
41:48 Closing Thoughts: Questions for Embracing Change
43:05 Episode Wrap-Up and Farewells

The post The Tech Humanist Show: AI Augmentation vs. Automation appeared first on Brian Solis.

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My latest article in Forbes explores what CEOs shared with me about AI business transformation during a full day workshop. 

AI is now firmly on CEO and board agendas. But many leadership teams are still asking the wrong questions. Recently, I led several workshops with roughly 100 CEOs and Chief Operators at a leadership conference in Arizona.

The topic was AI. But the real conversation was leadership.

These were honest conversations with operators who are under daily pressure to grow revenue, protect margins, improve customer experience, support stretched teams, open better organizations, retain talent and make faster decisions in a more complex operating environment.

What became clear is that CEOs feel the pressure. Their boards are asking about it. Their teams are experimenting with it. Vendors are pitching it. Competitors are talking about it. The headlines only make everything more confusing.

But beneath the urgency is something much more revealing, but often kept quiet.

Many CEOs know AI is going to change their business, but they do not yet know how to lead through it. The recent CEO departures at Coca-Cola and Walmart signaled as much.

That is not something most CEOs are going to say out loud in a boardroom or at an all-hands meeting (unless they’re already on their way out). CEOs are expected to project clarity. They are expected to have conviction. They are expected to know where the business is going.

But in a trusted setting, the more honest version surfaced: “I know this matters. I know we need to move. But I’m not sure where to start, and I’m not sure my organization is ready.”

Executives don’t know what they don’t know. This was by far the most interesting part of each workshop. Leaders would share that they are the CEO because they know what to do. But they admitted that they don’t what to do right now. That scares them. And they’re not sharing that honesty with their teams. Anyone who appears as an AI expert is heard, regardless of their credentials or caliber of ideas.

Companies that survive and thrive will be the ones whose leaders are willing to rethink how the enterprise creates value.

Most CEOs Are Still Thinking Too Small

Any time AI came up, most leaders immediately went to the obvious places: customer service chatbots, productivity tools, automation, reporting, marketing content and operational efficiency.

Those are all valid starting points. They are also too narrow. The dominant mindset still is: “Where can we bolt AI onto the business?” Much less common is the more important question: “How should the business work differently now that AI exists?”

If AI is treated as a bolt-on, it will mostly produce incremental gains. Faster emails. Better summaries. More efficient customer service. Some cost savings. Some productivity improvements.

But if AI is treated as a catalyst, it forces a much deeper conversation about work, workflows, operating models, decision rights, talent, guest experience and growth.

Most companies are trying to use AI to optimize existing workflows. They aiming to reduce costs. But very few are asking whether those workflows should exist in their current form or whether there are new outcomes to be achieved not possible before AI.

Not having these conversations lead to missed opportunities.

The more advanced leaders in the room were already beginning to see AI differently. They were talking about AI not only as an efficiency lever, but as a growth catalyst. They named opportunities in customer experience, revenue generation, service models, locale, labor planning, team experience, training and better decision-making.

That is where the conversation needs to go. Not just, “How do we save time or money as we optimize yesterday?”

But, “What can we now do that we could not do before?”

The Real Bottleneck Is Imagination

IT came up a lot in the workshops, often as a roadblock as IT leaders were said to prioritize protecting their positions instead of getting closer to the business. Data came up constantly. Governance, risk, privacy and security were top of mind.

These are real issues. Most companies have fragmented systems, disconnected data, unclear ownership and legitimate concerns around how AI tools are being used. Those problems cannot be ignored.

But the deeper bottleneck is not technical. It is imaginative. And imagination is at war against those pushing agendas that make AI the new status quo.

Most leadership teams have not yet developed a shared picture of what AI could make possible across the organization. In the recent Microsoft Work Trends Index report, research found that only 1 in 4 AI users (26%) say their leadership is clearly and consistently aligned on AI. As a result, the conversation defaults to tools. The company starts with use cases before it has a point of view.

That is backwards. A leadership team should start with asking:

“Where is our business constrained by old assumptions?”

“Where are we too slow?”

“Where are we too manual?”

“Where are we overly dependent on tribal knowledge?”

“Where are decisions being made with incomplete information?”

“Where are teams spending time on work that does not create differentiated value?”

‘Where are guests experiencing friction?”

“Where are operators not getting the support they need?”

Those questions open up a different kind of AI conversation. They move AI from a technology agenda to a business agenda.

How imagination takes shape is also tied to embracing a beginner’s mind.

CEOs Are Learning That Fluency Cannot Be Delegated

One of the clearest themes from the workshops was that AI fluency is becoming a leadership priority.

That does not mean every CEO needs to become technical. It does mean CEOs and senior teams need enough understanding to ask better questions, challenge shallow ideas, recognize real opportunities and make informed decisions about risk.

AI cannot live only with IT. It cannot live only with digital. It cannot live only with innovation teams or a few enthusiastic power users. Of course, those functions matter. But AI strategy has to be led by the business.

The risk is that, in the absence of senior-level fluency, whoever sounds most confident becomes the de facto AI expert. That could be a vendor. It could be a consultant. It could be an internal enthusiast. It could be someone with technical expertise but limited understanding of the company’s operating model.

Confidence is not the same as judgment. CEOs and leadership teams do not need to know everything about AI, but they do need to know enough to lead.

The ‘AI Tax’ Is Already Showing Up

There was another theme in the room that deserves more attention.

In many companies, employees are already using AI to improve personal productivity. That sounds positive, and in many cases it is. But the results are uneven.

A lot of organizations are now seeing more volume, but not always more value. More content, but not always better thinking. More drafts, more summaries, more decks, more emails, but also more generic work, more rework and more quality control issues.

This is the AI tax.

I read this quote in a recent article on Futurism, “I have never knowingly finished reading an email signed by a human but written by AI.”

AI generated content is turning people off.

It is the hidden cost of unmanaged adoption. It shows up as shallow analysis, off-brand communication, hallucinated facts, duplicated effort, and the quiet frustration of managers who now have to clean up AI-assisted work that was never thoughtfully reviewed.

But the answer is not to ban experimentation. That would be a mistake. The answer is to raise the floor, set standards, set norms at a higher-bar.

Organizations need to teach people not only how to use AI, but how to use it well and what good vs. great looks like. And on that note, leaders need to call out what slop looks like and why it’s not acceptable. They need to demonstrate how to prompt with context., how to pressure-test output, hw to protect sensitive data, how to preserve brand and personal voice, and how to know when human judgment matters most.

AI fluency is not just about access to tools. It is about quality of thinking and output.

Fear Is In The Room

Every CEO understands there is fear around AI and jobs.

Employees are wondering what this means for their roles, their teams, their relevance and their future. Some are excited. Some are anxious. Many are both.

Leaders feel this tension. They know they need to move. They also know that if AI is framed only as a cost-cutting exercise, the organization will resist it.

That is why change management came up repeatedly.

But “change management” may be too soft or “out of date” a phrase for what is required. This is not simply about communications, training, or adoption plans. This is about trust, vision, and leadership.

I don’t know anyone who enjoys “change” or “management.”

Employees need to understand the company’s intent, vision, direction…a future motivating state. And they need to believe they play a role in building that future.

Is AI here to eliminate people? To empower them? To change what great performance looks like? To create capacity for growth? To improve the customer’s experience? To make the business more resilient?

If leaders do not define the narrative, employees will create their own.

And in the absence of trust, the default narrative will be fear.

The Best Leaders Are Willing To Become Beginners Again

One of the most striking moments in the workshops was hearing several CEOs arrive at the same conclusion on their own: they need to approach AI with a beginner’s mind. Remember why you started.

That is easy to say and hard to do.

CEOs are rewarded for experience, pattern recognition, conviction, and decisiveness. They are usually in the room because they have seen a lot, solved a lot, and developed good instincts over time.

AI challenges that posture.

It is moving too quickly for any leader to pretend they have it all figured out. The old patterns still matter, but they may not be enough. In some cases, they may even get in the way.

A beginner’s mind does not mean abandoning judgment. It means being honest about where judgment needs to be rebuilt.

The leaders who navigate this well will not be the ones who perform certainty. They will be the ones who create the conditions for their organizations to learn faster than the market around them. And this mindset is where imagination comes to life.

What CEOs And Leadership Teams Should Do Next

The path forward is not to launch random pilots or chase every new tool. It is to build a leadership agenda for AI.

First, stop starting with use cases. Start with the work.

Look across the enterprise and identify where work is slow, repetitive, inconsistent, overly manual, or trapped in silos. Then ask how that work could be redesigned with AI. The goal is not to add AI to bad processes. The goal is to rethink the process.

Second, define the company’s AI ambition.

Every leadership team should be able to answer a simple question: What do we want AI to make true about our company?

Do we want to become more efficient? More predictive? More guest-obsessed? Better at supporting operators? Faster at opening restaurants? More personalized in how we serve customers? Better at growing revenue? A better place to work?

Without a clear ambition, AI becomes a collection of disconnected experiments. With a clear ambition, it becomes a strategic agenda.

Third, build executive fluency.

The CEO and leadership team need dedicated time to understand what AI can do, where it fails, what risks it creates, and how it may reshape the business. This cannot be a one-time inspiration session. It has to become part of how the senior team thinks about strategy, operations, talent, and growth.

Fourth, treat governance as an enabler.

Risk is real. Governance matters. But the purpose of governance should not be to slow the organization down. It should help people move faster with confidence.

Teams need clarity on what tools are approved, what data can be used, where human review is required, how brand standards are protected, and what behaviors are off limits.

No rules creates risk. Too many rules creates paralysis. Good governance creates trust.

Fifth, confront the data reality.

Many companies are not ready for the AI they say they want because their data is too fragmented. Guest data, labor data, transaction data, training data, operational data, marketing data, real estate data, and financial data often sit in disconnected systems.

Perfect data is not required to begin. But leadership teams need to be honest about which data domains matter most and where better integration would create the greatest leverage.

AI will not magically create enterprise intelligence from organizational fragmentation.

Sixth, redesign work before redesigning headcount.

If AI is introduced primarily as a labor reduction tool, employees will protect themselves. If it is introduced as a way to create capacity, improve work, and unlock growth, the conversation changes.

One of the most productive moments in the workshops came when I asked leaders what they would do with resources freed up by AI.

That question shifted the energy in the room.

Instead of talking only about efficiency, leaders began talking about growth. Better guest experiences. Stronger teams. New services. More support for operators. Faster innovation. Better decisions.

That is a more meaningful frame.

Freed-up capacity should not automatically become removed capacity. In the best companies, it becomes growth capacity. Reinvest efficiency gains back into the business and people.

Finally, build a culture of disciplined experimentation.

The companies best positioned for AI are not necessarily the ones with the most tools. They are the ones where people feel empowered to try new things, ask better questions, challenge old assumptions, and learn from failure.

But experimentation needs discipline. Otherwise, it becomes noise.

Leaders need to define what good AI-assisted work looks like. They need to create safe spaces for testing. They need to share what is working. They need to call out low-quality output. They need to make learning visible.

AI adoption cannot be a free-for-all. It has to become an organizational capability.

The Leadership Moment

The biggest takeaway from my time with these CEOs is that they are not behind because they lack interest. They are behind because AI requires a more honest leadership conversation than most companies are currently having.

This is not just about tools. It is not just about automation. It is not just about chatbots, productivity, or cost savings and efficiency gains.

It is about whether leaders are willing to admit that the old playbook may not be enough or even right.

For many CEOs, the hardest part of AI will be creating the space to learn before they feel ready, to ask questions they are not used to asking, and to lead through ambiguity without pretending it does not exist.

That is the conversation CEOs may not be having out loud. But it is the conversation they need to have with their leadership teams now.

• Where are we thinking too small?
• What do we not yet understand?
• What work would we redesign if we were building the company today?
• How do we turn efficiency into growth?
• And what kind of leadership does this moment require from us?

The CEOs who answer those questions honestly will do more than adopt AI. They will use it to build a different kind of company.

_________

Read Infinite | Read Mindshift | Subscribe to my Substack | Keynote Speaker

The post Forbes: The AI Conversation CEOs Are Not Having Out Loud appeared first on Brian Solis.

Via Chrys Brent Deiparine, International Business Times

Executives foresee AI-driven job cuts, raising concerns about job security and the future of entry-level positions.

Artificial intelligence is no longer a future workplace disruption. For many executives, it is already becoming a workforce strategy.

A new Mercer survey of 825 C-suite leaders found that 99% of CEOs expect AI to result in at least some headcount reduction within the next two years, underscoring how quickly automation is moving from a productivity tool to a driver of workforce restructuring.

The findings arrive as workers are already showing signs of growing anxiety about their job prospects. Mercer’s 2026 Global Talent Trends report found employee ‘thriving’ has fallen from 66% in 2024 to 44% in 2026, while 40% of workers now fear losing their jobs because of AI.

For employees, the concern is not simply whether jobs disappear. It is whether they can clearly demonstrate value in a labour market where routine tasks are increasingly being automated. That is the risk personal finance expert Suze Orman says many workers underestimate.

The Mercer findings suggest corporate leaders are becoming increasingly confident that AI can replace or reduce certain categories of work.

That shift is already showing up in employment data. According to Challenger, Gray & Christmas, employers announced more than 97,000 job cuts in May 2026, the highest monthly total since 2020. Roughly 40% of employers cited AI as a primary factor behind those reductions.

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via Vicki Salemi, Tribune Content Agency

DEAR READER: In the age of AI, what does it mean to become “infinite?”

Inquiring minds need to know, so we checked with Brian Solis, head of global innovation at ServiceNow and coauthor of “Infinite: How Visionary Leaders Transform Today’s Businesses into AI-Forward Companies.”

“Becoming ‘infinite’ is about employing an intentional mindset where AI, in its current and evolving forms, will automate and augment work perpetually, to free up time and resources to allow constant expansion and reinvention,” said Solis. “Infinite companies automate what should move forward and augment people and work to deliver exponential outcomes and value. Augmentation is a defining instrument of infinite leadership because it expands human possibility: helping people do what was once impossible without AI, and helping AI create value that is only possible through people.”

Solis said there are specific stages when people identify with and adopt AI. “Some people are ‘AI Followers.’ They wait for proven ROI and best practices before moving. They let others take the risks. They prioritize stability and compliance over speed and possibility,” he said. “Others are ‘AI Forward,’ thinking about integrating AI alongside humans deliberately. They are genuinely exploring what work looks like when humans and AI agents collaborate, but the human is always in the loop by design.”

As for the next stage? Solis said it’s defined as “AI First,” whereby AI should be the default solution for an increasing number of tasks, with human intervention as the exception rather than the rule. They don’t aim to eliminate human input entirely. They aim to free human time so humans can provide judgment, creativity and strategic oversight on the things that genuinely require it.”

The final stage is “AI Native.” The product or service in this stage wouldn’t exist if AI wasn’t part of the equation, according to Solis. “AI is core, intrinsic, irremovable. It does not require the whole company to operate in this way, but it would allow people to operate in an AI Native way when defining new offerings or reimagining how existing or net new work flows, one workflow at a time.”

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