I’ve spent much of my career studying how technology changes people and how those changes eventually reshape markets, organizations, and culture (or how it needs to). AI is accelerating and stressing that cycle in ways and speeds I have not seen before. This is why I keep one question close by. Are we using this moment to rethink what is possible, or merely to make what already exists faster, cheaper, or more efficient?

It’s something to think about…

When the future gets literally booed, we need to talk about it. Why? Because it reflects culturally where we lack leadership at work, university/school, and at home.

NSFW

Now, with that said…

I recently joined leaders from the American Supply Association in Newport, Rhode Island, for a conversation about artificial intelligence, the future of work, and the changing role of leadership. Although the presentation was designed for executives from supply, distribution, manufacturing, and construction, the discussion quickly moved beyond any one industry. The questions in the room were the same questions confronting leaders everywhere: What does AI actually mean for our businesses? How quickly should we move? What happens to our people? Where do we begin? How do we measure success (and what does it look like?). And perhaps most honestly, what are we supposed to know when no one has a complete playbook?

I have spent much of my career studying how technology changes behavior and how those behavioral shifts eventually reshape markets, organizations, and culture. AI is different from previous waves of innovation because it is not confined to one device, platform, department, or customer experience. It is influencing how people search, write, learn, decide, communicate, create, shop, manage, and work. It is moving into every function at once, while its capabilities continue to improve at a pace most organizations are not designed to absorb let alone scale.

That does not mean the future is predetermined. Technology may introduce new capabilities, but people determine what those capabilities become. The most consequential question facing leaders is whether we will use AI primarily to optimize what we did yesterday or to imagine what could or should exist tomorrow.

Technology Changes People, and People Change Markets

Every major technology begins as something obscure, unnecessary, or even threatening. Over time, it becomes useful, then convenient, then expected. Eventually, the behavior it introduced becomes so normal that we forget how quickly or dramatically we changed.

For example, I told the sequential steps from the Internet to Ubers and Lyfts to Waymos and the autonomous flying vehicles that come next.

In 1999, parents warned their children not to meet strangers from the internet and certainly not to get into their cars. Less than two decades later, millions of people were summoning strangers from the internet and climbing into their vehicles without hesitation. Today, in cities such as San Francisco, Los Angeles, and Austin, people can summon a car with no driver at all. What once sounded reckless became innovative, then convenient, and finally ordinary.

https://www.youtube.com/watch?v=wEWs1VYXTS0

The same progression is unfolding with AI. People are already giving intelligent systems access to their calendars, preferences, communications, documents, and decisions. AI agents are beginning to research, schedule, compare, recommend, purchase, and act on behalf of users. Some of these behaviors still feel experimental, but so did mobile banking, social media, streaming, and ride-sharing when they first appeared.

This matters for business because technology does more than improve convenience. It resets expectations. Once customers experience an intelligent, personalized, immediate response in one part of their lives, they begin to expect that level of service everywhere. They do not care whether your company operates with legacy systems, fragmented data, old approval structures, or entrenched processes. They simply know that another company made something feel easier.

Your customers are not comparing your business only with your closest competitor. They are comparing it with the best experience they have anywhere. Their expectations are being shaped by Amazon, Apple, Uber, Netflix, ChatGPT, and every other platform that teaches them what speed, personalization, simplicity, and intelligence can feel like.

That is why AI is not only a technology initiative. It is a customer-experience issue, a workforce issue, an operating-model issue, and a leadership issue.

AI Anxiety Is Often a Reflection of Leadership

Many employees, particularly younger workers, encounter AI through headlines about layoffs, automation, job loss, and displacement. Then they hear their own executives describe AI in terms of efficiency, productivity, cost reduction, and doing more with fewer resources. It is not difficult to understand why enthusiasm may be limited.

Employees are not necessarily resisting the technology itself. They may be resisting the future they believe leadership is designing around it.

When companies introduce AI without a clear human narrative, people are left to fill in the blanks. Executives say “transformation,” while employees hear “replacement.” Leaders say “productivity,” while teams hear “more work with fewer people.” Management says “efficiency,” while workers wonder whether their experience, judgment, and contribution are being reduced to a line item.

This is not simply a communication problem. It is a trust problem, and trust cannot be repaired with a training module or a software demonstration.

Leaders have to explain why AI is being introduced, how work will change, which skills will become more valuable, what employees will be able to do that they could not do before, and how the value created by AI will be reinvested. They must show that the goal is not merely to make work cheaper, but to make people more capable and the organization more valuable.

The most responsible leaders aren’t promising that roles won’t ever change. They will help people understand how roles evolve and give them the tools, support, and confidence to evolve with them. They are creating a future with their employees instead of announcing a future to them.

AI Still Needs Human Direction

For all the extraordinary claims made about artificial intelligence, AI does not understand your company in the way you do. It does not possess institutional memory, lived experience, moral responsibility, empathy, or a sense of purpose. It can identify patterns, generate options, synthesize information, and increasingly take action through AI agents, but it does not inherently know what matters.

That distinction is essential because business decisions are rarely based on information alone. They involve context, tradeoffs, consequences, timing, relationships, values, and judgment. A recommendation may be mathematically sound and still be strategically foolish. A decision may increase efficiency while damaging trust. A process may become faster or less expensive while producing a worse experience for employees or customers.

AI cannot resolve those tensions on its own. It needs people who can recognize what is missing, challenge the answer, understand the broader context, and decide what outcome is worth pursuing.

This is why leadership becomes more important in an AI-driven organization, not less. The companies that succeed will not simply be those that deploy the most models, copilots, or agents. They will be the companies that develop people who know how to think more expansively because these tools exist.

The Risk of Outsourcing Our Thinking

One of the easiest mistakes to make with AI is to confuse speed with intelligence. When a tool can write an email, summarize a report, build a presentation, or produce an answer in seconds, it feels as though we have become more productive. Sometimes we have. At other times, we’ve simply transferred our thinking to a system and accepted whatever came back. And that can create AI slop and impose an AI tax on others.

Convenience can become dependency. And with AI help, it might become the MO. If AI writes every message, distills every document, frames every argument, and proposes every decision, we may save time while gradually weakening the skills we need most: critical thinking, synthesis, creativity, judgment, and original point of view.

The more valuable use of AI is not to have it think instead of us or for us, but to help us think beyond ourselves. It can compare opposing perspectives, uncover contradictions, challenge assumptions, model scenarios, simulate stakeholder reactions, and surface questions we may not have considered.

Consider the difference between asking AI to summarize ten reports so you do not have to read them and asking it to compare those reports, identify where they disagree, surface convergences or next layer implications, and reveal the questions no one appears to be asking. The first saves time. The second improves the quality of thought and your differentiated value.

From AI Fluency to Augmented Intelligence

Many organizations are now focused on AI adoption and fluency. Employees are being taught how to use chatbots, copilots, image generators, and other intelligent tools. This work is necessary, but fluency should be treated as the starting point rather than the destination.

Knowing how to operate AI does not mean knowing how to create meaningful value with it. A person can learn to prompt a system without learning to think more creatively, question assumptions, or make better decisions.

I’ve been exploring this distinction through the concept of AIQ. The term is often used to describe an Artificial Intelligence Quotient, or a person’s ability to understand and use AI. I believe we need a more ambitious definition: Augmented Intelligence Quotient.

The Augmented Intelligence Quotient is the ability to combine human imagination, judgment, empathy, creativity, and experience with machine speed, scale, and pattern recognition. It is not about asking AI to do our work for us. It is about using AI to extend what we are capable of seeing, understanding, and creating.

Said another way, AIQ is the practice of exploring what we could not produce without AI and what AI could not create without us.

A leader with basic AI fluency may AI to write a strategy memo. A leader practicing augmented intelligence may ask AI to challenge that strategy from the perspective of a customer, competitor, regulator, employee, investor, and future market entrant. The value is not simply in producing more content. It is in improving the quality of the leader’s judgment before a decision is made.

Leaders who use AI to ask more consequential questions will develop more original ideas and make better decisions.

You Compete With AI, Not Against It

The public narrative often positions humans and machines as opponents. It makes for an effective headline, but it is not the most useful way to think about the future of work.

In practice, the more immediate competition is between people who know how to work with AI and people who do not. A salesperson who uses AI to understand an account, prepare for a meeting, anticipate objections, and personalize recommendations will have an advantage over someone relying only on memory and instinct. A manager who uses AI to model scenarios, identify operational patterns, and challenge assumptions will have an advantage over someone relying exclusively on historical reports.

The same is true at the organizational level. A company that combines human expertise with machine intelligence will outperform one that treats AI only as an efficiency or cost-reduction tool.

This is also why the skills becoming more important in the AI era are deeply human. Curiosity, creative thinking, empathy, resilience, adaptability, critical judgment, and the ability to learn and unlearn will become more valuable because machines can scale execution, but humans must still determine what deserves to be scaled.

Iterative AI and Innovative AI

One of the most useful distinctions for leaders is the difference between iterative AI and innovative AI.

Iterative AI improves what the organization already does. It can make processes faster, reduce costs, remove friction, automate repetitive work, improve forecasting, and increase productivity. It asks familiar and necessary questions: How can we perform this task more efficiently? Where can we remove bottlenecks? How can we reduce errors? How can we serve customers more quickly?

Every organization should pursue these opportunities because there is no virtue in preserving unnecessary complexity or inefficient work.

The risk appears when optimization becomes the entire strategy.

Innovative AI asks a different set of questions. Why does this process exist in the first place? Are we solving the right problem, or simply improving an old solution? What customer need remains unmet? What new service, experience, or business model becomes possible now that intelligence is more scalable? What would we create if we were starting the company today?

Iterative AI improves the current business. Innovative AI helps build the next one.

The strongest companies will pursue both, using efficiency gains to create the capacity required for reinvention. That second step is where many organizations fall short. They automate work, reduce expense, and declare success without deciding how the liberated time, talent, and resources will be invested in growth.

Efficiency is valuable, but it is not the same as innovation. You cannot automate your way to imagination, and you cannot cut your way to growth.

Dave Wright and I explore this topic and what to do differently in depth in our new book, “Infinite: How Visionary Leaders Transform Today’s Businesses into AI-Forward Companies.”

Every Business Has a Reinvention Opportunity

The most immediate applications of AI are already visible across sales, service, operations, finance, marketing, talent, and product development. AI can help teams prepare for customer conversations, personalize follow-up, analyze demand, clarify proposals, improve forecasting, detect risk, summarize service histories, and recommend next actions.

Yes. These use cases matter because they improve existing work. The larger opportunity, however, emerges when intelligence begins to connect workflows and functions across the organization.

Imagine a company that anticipates demand using a combination of customer behavior, market signals, weather, operational patterns, and real-time context. Imagine a service organization that does more than resolve issues quickly; it identifies the patterns behind those issues and helps redesign the experience so the problems occur less often. Imagine training that continuously adapts to an employee’s role, performance, knowledge gaps, and customer needs. Imagine turning years of institutional expertise into an intelligent service that customers can access whenever they need it.

This goes beyond productivity. These ideas represent new capabilities, better experiences, and potentially new sources of revenue.

This is where AI moves from optimization to reinvention.

Start with One Workflow, but Question the Workflow

Leaders often ask where to begin, and a practical answer is to select one meaningful workflow. Choose an area where friction is visible, the outcome matters, and employees or customers would notice the improvement. Establish clear governance, define what AI can access and do, and create a controlled environment for experimentation.

Governance is especially important as AI agents become more capable of interacting with systems and performing work. Organizations must define what requires human approval, what should be monitored, which actions are prohibited, and how mistakes will be contained.

At the same time, governance should enable responsible experimentation rather than become a sophisticated excuse for doing nothing.

Once a workflow has been selected, leaders should resist the temptation to automate it exactly as it exists. Many business processes were designed for another era, shaped by paper forms, functional silos, manual approvals, disconnected systems, and organizational politics. Automating a flawed process can make dysfunction move faster, which is an impressive technical achievement but a questionable business strategy.

Begin with the outcome instead. What is the customer or employee trying to accomplish? Where is information lost? Which steps add value and which survive only because no one has challenged them? Where is human judgment essential? What can AI handle? What should always be escalated? How would this workflow be designed if intelligence were available from the beginning?

That’s how business reinvention takes shape: not through one sweeping transformation program, but through the thoughtful redesign of value creation, one workflow at a time.

Innovation Requires a Beginner’s Mind

Experience helps leaders recognize patterns, avoid mistakes, and make decisions quickly. It can also become a filter that limits what they are willing to consider. Experts know what has worked. Innovators remain curious about what may work next.

A beginner’s mind does not require abandoning expertise. It means creating enough distance from what we know to explore what we may be missing.

Most leaders operate within two familiar categories: what they know and what they know they do not know. Together, these define the boundaries of the comfort zone. AI gives us a powerful way to explore a third category: what we do not know we do not know.

That territory is ambiguous and difficult to measure. It may not come with a benchmark, case study, or guaranteed return. It is also where new possibilities tend to emerge.

To explore it, organizations need more than technology. They need psychological safety. Employees must feel that they can ask questions that sound naïve, challenge assumptions that appear permanent, and test ideas that may not work. Leaders who demand certainty from the beginning will receive predictable ideas based on the past.

Innovation requires a willingness to be wrong before something more valuable can be discovered.

A More Useful Leadership Practice

Leaders do not need to become AI experts or engineers, but they do need to become more intentional about how they think with AI.

Instead of asking AI only to write the memo, ask it to find weaknesses in the argument. Instead of asking it to summarize the market, ask it to identify signals that do not fit the prevailing narrative. Instead of asking how AI can reduce the cost of service, ask what recurring service problems reveal about the design of the business. Instead of training employees only to prompt faster, redesign work so they can spend more time applying judgment, empathy, creativity, and synthesis.

A useful discipline is to begin each day with one exploratory question: What am I not seeing? Which assumption is limiting our options? What would customers expect if they knew nothing about how our industry traditionally operates? Which process would we eliminate if we were designing the company today? What new value becomes possible when human imagination and machine intelligence work together?

The goal is not to accept every answer AI provides. The goal is to use those answers to stretch our own thinking and expand the range of options we are capable of considering.

The Future Remains a Leadership Choice

AI is not something employees should be left to figure out alone, or read or hear about, and it is not a transformation that can be delegated entirely to IT. It is becoming part of how organizations think, learn, decide, operate, and create value. That makes it a leadership responsibility.

The companies that thrive will be led by people who understand how technology changes behavior, how expectations evolve, how work must be redesigned, and how intelligence can be used to create better outcomes for customers, employees, partners, and communities.

Those leaders will not pretend to have every answer. They will be willing to learn publicly, experiment thoughtfully, protect trust, create room for curiosity, and ask questions worthy of the capabilities now available to us.

AI will continue to advance. Agents will become more capable. Workflows will become more autonomous, and established business models will be challenged. Yet AI does not get to decide what your organization becomes. That remains a human responsibility and, for leaders willing to think more ambitiously, an extraordinary opportunity.

Infinite ∞ | Mindshift | Subscribe | Keynote Speaker

The post AI Is Changing How We Think, Work, and Lead; What We Do Next Is Up to Us appeared first on Brian Solis.

At Synthesia Live in New York, I had the privilege of closing a day dedicated to the future of AI video, learning, communication, and work. By the time I walked on stage, the audience had already heard amazing speakers, seen compelling demos, and explored what happens when AI powered avatars and videos becomes something more dynamic, adaptive, and personal for customers and employees.

So I didn’t want to give another “AI is changing everything” talk. (Full talk at the end)

Instead, I wanted to ask a different question.

What happens when AI changes us?

You and me. Us. How we think, how we learn, how we communicate, how we create, how we lead, how we show up for customers, employees, students, and one another, how we perceive and how we’re perceived. We are and aren’t changing with how we use AI. Research shows that we’re starting to let AI do our thinking for us. We’re starting to sound and write like AI. We’re imposing an AI Tax on our friends, communities, and colleagues. We’re exchanging quality for quantity, speed for uniqueness, and content for context.

At the same time, the same is true in our work. We’re largely employing AI to do yesterday’s work. Most companies are still modernizing the past with AI. We’re doing the same in our day-to-day work, all in the name of productivity boosts.

Businesses are taking legacy processes, legacy training programs, legacy service models, legacy communications, and wrapping them in shiny new AI interfaces. They are making yesterday more efficient, cheaper, and scalable.

But faster old work is still old work.

And in what’s becoming an AI-first world, for better or worse, the opportunity isn’t to do what we already know more efficiently. The opportunity is to do what we couldn’t do before.

That was the real message of my keynote.

AI automation is only the beginning of a new AI status quo. The message I wanted to share is that the future belongs to those who use AI to augment people and help them grow, personalize experiences, and build entirely new forms of capability.

The Control-Alt-Delete Moment Facing All of Us

The night before Synthesia Live, I had flown in from Silicon Valley where I met with a group of executives from Australian universities and institutions. They were on the last stop of an AI tour and made time to visit us at ServiceNowHQ.

They had spent days meeting companies, founders, researchers, and AI leaders. They had seen enough to understand that education, work, skills, and institutions were not simply facing a technology upgrade. It was a test of vision and leadership.

By the end of our conversation, I asked them what they wanted to take back home. What was the one thing they wanted to change?

The answer was legacy.

Everything about how their institutions taught, measured, supported, and prepared people for the future was suddenly open for reconsideration. They didn’t want to go home as executives defending the institution. They wanted to go home as students, ready to learn and unlearn.

That is the mindshift AI requires.

It is not about asking, “How do we use AI to make our current model more efficient?”

It is asking, “What about yesterday deserves to live tomorrow?”

And then, with equal honesty, “What must we now do differently because the future is no longer waiting for us?”

The Student Who Built a Machine to Avoid Handwriting Homework

I shared a story during the keynote about a high school student in India. His school reportedly banned AI-generated homework and required assignments to be handwritten so students would have to think about what they were writing.

So, he built a machine that could write in his handwriting while AI generated the content.

@taylorlorenz

I feel like this kid should never have to do homework again?! Also it does math! #tech #technology #ai #creator #creatoreconomy #success #artificialintelligence #siliconvalley #contentcreator #innovation #influencer

♬ Dance You Outta My Head – Cat Janice

Now, the point is not to celebrate cheating. The point is to notice what happens when human imagination collides with legacy constraints.

A student saw the system, understood the constraint, and redesigned around it. You could say, he understood the assignment. Ha!

That is creativity.

That is also a warning.

If students can reimagine the future of homework, leaders can reimagine the future of work, learning, service, and communication. But only if they’re willing to stop protecting the past.

Said No Customer Ever

One of my opening slides read:

“I hope I get to talk to a customer service AI chatbot today.” – Said no customer ever.

Another read:

“I hope I get to take an AI audio training course today.” – Said no employee ever.

We laugh because it is true.

Article content
While AI is advancing at an exponential pace, many experiences are still designed in ways that are counterintuitive to how people now think, learn, and behave.

As a digital anthropologist, I’ve long studied how technology reshapes behavior. Think about TikTok. When people scroll through endless short-form videos, they begin to process information differently. Attention changes. Expectations change. Learning patterns change. Then we hand that same person a 90-minute training video, a PDF, or a textbook and wonder why engagement collapses.

AI is doing something similar.

We are learning to work differently. Think differently. Ask differently. Create differently. But most enterprise systems still expect people to behave as if nothing has changed.

The future of learning, service, and communication is not more content at efficiency and scale. It is more intuitive, adaptive, and human-centered experiences.

And that’s the challenge. Productivity is not the summit, although it is touted as such. Personalization at scale, learning, service, marketing, is the real win.

Cognitive dAIrwinism

There is another side to this story.

AI did not arrive with an instruction manual. It didn’t come with a parent, a teacher, or a wise mentor telling us how to use it in ways that make us better.

So, we are already seeing new failure modes emerge.

Cognitive offloading. AI atrophy. Cognitive debt. Digital amnesia. AI sycophancy. AI slop. AI brain fry. Cognitive surrender.

This is what I call cognitive dAIrwinism.

How we use AI shapes how we think. If we use AI to think for us, we risk outsourcing the very capabilities we need to strengthen in an era of AI. If we use it to move faster without judgment, we scale mediocrity and miss new opportunities. If we use it to generate more content without care, we create AI slop.

And slop has a cost. It’s called an AI Tax.

It is the hidden labor required to review, correct, rewrite, validate, and tolerate low-quality AI output. Employees feel it every day. It shows up as rework and editing, frustration, lost trust, more questions and more meetings, more requests like, “can you make this sound less like AI?”

Speed is becomes the norm, but hopefully not the goal.

When quality gives way to quantity, trust becomes the casualty. And trust is priceless.

AI is Not Taking Jobs, It is Taking Tasks

The headlines tell us AI is coming for jobs. And yes, there will be disruption. There already is. But there is an important distinction leaders must understand. AI automates tasks. Jobs are bundles of tasks, judgment, relationships, context, accountability, empathy, creativity, and decision-making.

Agents can execute tasks at scale. But they still need human intention, supervision, standards, ethics, and human imagination. Whether humans are in the loop or above the loop, the future of work still needs people.

The question is whether companies use AI to automate people out of work or augment people into more valuable work.

The World Economic Forum estimates that 170 million new roles will be created this decade, even as 92 million are displaced. It also projects that 39% of core skills will change by 2030, and 63% of employers already cite skills gaps as the biggest barrier to transformation.

This is bigger than HR. This is a leadership mandate.

If skills are changing in real time, learning has to show up in real time. If AI is changing work, learning cannot remain a library of courses people visit when they have time. It has to become a living system that coaches, adapts, guides, and improves people in the flow of work at their pace of learning, closing the skills gaps that unique to each person.

Training has to change. We’re not aiming to check more boxes around training, the goal is to make people measurably better. Measurably happier. Measurably more capable.

From AI Fluency to Augmented Intelligence

A lot of organizations are talking about AI fluency right now. That is good. But basic fluency is not enough. The question is, what kind of fluency are we scaling? What are the skills needed to compete with AI and what are the gaps across the organization.

Are we teaching people how to use AI to create more slop? Or are we teaching them how to do what they couldn’t do without AI and what AI couldn’t do without them?

OpenAI has described a capability overhang: the widening gap between what frontier models can do and how most people actually use them. According to OpenAI, typical power users rely on roughly 7x more advanced thinking capabilities than typical users. That means there is already a gap among AI users.

The capability overhang doesn’t measure people who have AI and those who do not. It measures the capability and output in thinking and working with AI vs. those who only know how to prompt it.

This is why I believe we need to move from AIQ as an “Artificial Intelligence Quotient” to AIQ as “Augmented Intelligence Quotient.” It helps leaders and people balance all the critical “quotients” necessary to navigate a future that has yet to be charted, IQ, EQ (emotional intelligence), SQ (social quotient), AQ (adversity quotient).

Artificial intelligence quotient, as many describe it, is about tool use.

Augmented Intelligence Quotient is about human expansion.

AIQ is how we use AI to scale thinking, creativity, decision velocity, problem-solving, empathy, and imagination. It is the ability to work with AI in ways that make us more capable, not more dependent.

Otherwise, we are not using AI. AI is using us.

Iterative AI and Innovative AI

As Dave Wright and I outline in our new book, “Infinite: How Visionary Leaders Transform Today’s Businesses into AI-Forward Companies,” there are two forms of AI strategy every leader needs to understand.

The first is iterative AI.

Iterative AI improves what already exists. It removes routine work, reduces bottlenecks, accelerates workflows, scales scripting, localization, updating, distribution, and reporting. It helps make current learning more visual, engaging, personalized, and easier to retain. It helps managers coach more consistently. It helps teams work faster and more efficiently.

This is important work.

Some of yesterday deserves to live tomorrow. Iterative AI helps make that work better.

But then there is innovative AI.

Innovative AI creates new value. It asks what becomes possible when expertise can scale visually, conversationally, and personally. It asks why learning is still treated as content delivery instead of capability building. It asks what happens when video is no longer passive, but interactive, adaptive, and human-like in every experience.

It asks:

What if every employee had access to an on-demand coach, tutor, role-play partner, or guide?

What if onboarding adapted to each person’s role, skill level, region, language, and confidence?

What if sales enablement became practice, not passive consumption?

What if leadership communication became interactive and measurable?

What if learning happened exactly when someone needed to decide, act, or improve?

Iterative AI makes the old model better.

Innovative AI makes new models possible.

And disruption happens when new things make old things obsolete.

The good news is that disruption doesn’t have to happen to you. It can happen because of you.

AI Video as the Adaptive Interface for Human Capability

For years, video was a broadcast format. One person to many…one message, one version, one language, one pace, one experience.

With AI, video becomes something else.

It becomes a scalable interface for human-centered, personalized experiences in learning, service, onboarding, research, enablement, and communication.

The first chapter of AI in learning was asset acceleration. We used it to create faster, translate faster, localize, update, reduce production costs, and increase reach.

The next chapter is capability acceleration…auditing and closing the gap causing the capability overhang.

This is where AI video, agents, data, and workflows converge to create adaptive experiences that don’t just deliver information, but help people become better at what they do and where they need to be.

At the Synthesia event, data showed that leaders expect AI to drive more personalized learning, wider internal reach, and better learner engagement. That is the right direction. But the opportunity is bigger than engagement.

Engagement is not a click.

Experience is not an NPS score.

An experience is how someone feels in a moment. What they remember. What they do next.

A great learning experience should build capability and confidence. A great service experience should reduce friction, create trust, and build relationships. A great communication experience should make people feel seen, informed, and capable.

AI video should be used to make learning and service more human.

The IKEA Lesson: Automate the Routine, Augment the Human

One of my favorite examples is IKEA.

IKEA introduced an AI chatbot named Billie, a nod to its famous bookcase. Billie was designed to handle routine customer inquiries and deflect cases that did not need to escalate to a human. It worked. Billie handled nearly half of customer inquiries.

A traditional automation mindset would stop there.

The board slide writes itself: AI deflects 47% of inquiries. Fewer calls = lower cost. Reduce headcount. And boom…ROI achieved.

But IKEA did something more interesting.

They studied the other engagements.

What couldn’t Billie solve?

Inside those unresolved inquiries, they found a signal. Customers were asking for help with interior design, room planning, and product decisions. They didn’t just need answers. They needed advice. Taste. Context. Judgment. You know, the things where humans thrive in their help.

So, IKEA reskilled thousands of customer service employees into remote interior design advisors. They turned a cost center into a growth channel that generated roughly €1.3 billion in sales in its first year.

That is the difference between automation and augmentation.

Automation asks: “How many people can we remove?”

Augmentation asks: “What new value can our people create now that AI has removed the routine?”

That is the future of work.

We’re talking about fewer people doing less work. We’re talking about more capable people doing more meaningful work creating and delivering new value, better experiences, and more meaningful relationships.

The Future OS of Learning

For learning and development leaders, this moment is profound.

The future of L&D is not a larger library of AI created content. It is a living system that powers the future of work. It’s a system that can sense skill gaps, personalize development, coach in real time, simulate scenarios, support managers, guide decisions, measure progress, and adapt as work changes.

In this future, L&D is where intelligence lives. L&D becomes a capability engine. HR becomes a reinvention partner. IT becomes the infrastructure for human and agent collaboration. Leaders become designers of new ways of working. And employees become active participants in their own augmentation.

The future of work is AI plus humans.

AI Is a Test of Leadership

At the end of my keynote, I shared that the real test of AI is leadership.

In my work with companies around the world, across industries, I see a lot of management. I do not always see a lot of leadership.

Management asks how to reduce cost. Leadership asks how to create value.

Management asks how to automate the process. Leadership asks whether the process deserves to exist and if not, they ask the questions that help identify better ways of working. In the process, they identify new opportunities for value creation.

Management asks how to scale content. Leadership asks how to scale capability.

Management asks how to use AI. Leadership asks what kind of future AI should help us build.

This is where the human quotients matter. IQ, EQ, SQ, AQ. Intelligence. Empathy. Social connection. Adversity. And now, AIQ. Augmented Intelligence Quotient is the means toward human expansion.

AI can scale thinking, creativity, decisions, and problem-solving. Agents can scale capabilities across workflows, teams, and organizations. But they need us to set direction, define standards, ask better questions, and imagine better outcomes.

The future is being built by those willing to learn, unlearn, experiment, and lead.

Nothing new happens without something new happening.

That “something” starts with us.

To view the the other sessions, and they’re amazing, please click here.

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The post Beyond AI Automation: The Human Future of Work, Learning, and Communication appeared first on Brian Solis.

For decades, we built the web for people.

Now, AI is becoming a user too.

It searches, reads, compares, summarizes, recommends, and increasingly acts on our behalf. That means the web is no longer just a collection of destinations for humans. It is becoming an intelligent layer where machines help decide what gets discovered, trusted, chosen, and acted upon.

This is a much bigger shift than AI-generated content or the future of search.

The web itself is changing. And as content becomes infinite, attention scarcer, and discovery increasingly mediated by AI, the advantage will go to those who become the signal…for humans and AI agents.

The Intelligence RenAIssance

I recently had the opportunity to keynote WP Engine’s DE{CODE} event, where this year’s theme explored one of the most important shifts now reshaping business, marketing, commerce, publishing, and customer experience: the rise of the intelligent web. [FULL VIDEO AT THE END]

This is not just the next chapter of the internet. It is a fundamental rewiring of how people discover, evaluate, decide, buy, learn, and engage. And for the first time, we are no longer designing digital experiences only for humans. We are also designing for the AI agents that increasingly search, summarize, compare, recommend, and act on their behalf.

Websites are no longer static destinations. Content is no longer enough. Search is no longer just search. And AI is not simply a tool to produce more, faster. Used in the right way, AI becomes a catalyst for relevance, trust, personalization, and meaningful engagement. Used in the wrong way, it accelerates noise, slop, sameness, and cognitive surrender.

In my keynote, I explored how leaders, marketers, agencies, publishers, eCommerce brands, SMBs, and creatives can navigate this shift with intention.

This article expands on the ideas I shared in the keynote. My hope is that it gives you a deeper way to think about what is changing, why it matters, and what to do next. Because the future of the web is not just being built by AI. It is being built by us.

Welcome to the Intelligent Web

We are entering an industry renAIssance.

Research shows that one-third of all new published websites are generated by AI. According to Wired, researches from Imperial College of London, Stanford University, and the Internet Archive found that not only are 35% of all new websites AI-generated, online writing is “increasingly sanitized and artificially cheerful,” which appears to be making the internet fake-happy. And according to projections from ARK Invest, AI-generated text has officially outpaced human-written content, symbolizing a shift in how information is created and consumed across the internet.

This is an inflection point.

Now, the web is being reimagined for two audiences at once: human beings and the AI agents increasingly acting on our behalf. Search is becoming synthesis. Browsing is becoming delegation. Content is becoming context. Websites are becoming intelligent business platforms. And every organization now has to ask a new question:

Are we building for synthetic traffic and transactions, or are we building for engagement and trust?

That was the heart of my keynote at WP Engine’s DE{CODE}, where this year’s event explored how AI is reshaping the web for both humans and agents. WP Engine framed the moment as a move from static digital delivery systems to “intelligent, context-rich experiences” that AI systems will seek and cite.

The intelligent web will not reward everyone equally. It will reward those who become useful, credible, discoverable, readable, actionable, and trusted. It will reward those who stop treating AI as a content machine and start treating it as a catalyst for better experiences, deeper relevance, and more meaningful relationships.

That is why I opened the keynote with a simple truth: none of this came with an instruction manual. There is no official playbook for how to build a web that serves humans and machines. There is no universal standard for agent-ready commerce. There is no mature operating model for trust in an AI-mediated customer journey. And yet, here we are, building the future in real time.

The question is not whether the web will become intelligent.

It already is.

The question is whether we will be intelligent enough to shape it in a meaningful way from here.

Cognitive Darwinism Comes for the Web

I call this moment Cognitive Darwinism, which represents a human moment in how we choose to work with AI. Do we let AI do our thinking or do we expand our thinking with AI?

Cognitive Darwinism is the evolution of behavior, decision-making, creativity, and trust under the pressure of artificial intelligence. Some people will use AI to think better. Others will use it to avoid thinking altogether. Some companies will use AI to augment imagination. Others will use it to automate mediocrity at speed and scale.

AI can extend us. It can help us explore the unknown, challenge assumptions, synthesize complexity, and design possibilities we may not have seen otherwise. But when we simply ask AI to do the work for us, we risk collapsing into the known. We ask for answers instead of better questions. We accelerate output without deepening insight. We publish without learning. We create without meaning.

That is artificial intelligence. And because it offloaded thinking, it’s being studied as “cognitive surrender.” And surrender has consequences.

Researchers are already examining how AI use can encourage cognitive offloading and affect critical thinking. Gallup found that Gen Z’s sentiment toward AI has become more negative over the past year, with excitement and hope falling and anger rising.

48% of Gen Z workers say that the risks of AI in the workforce outweigh its benefits and a staggering 80% say that using it as a shortcut makes learning more difficult. Forty-four percent of Gen Z workers admit to sabotaging their employers’ AI deployments as a form of rebellion. They “hate” AI because of 1) fears of job displacement, 2) security holes, and 3) AI paradoxically adds more work (that’s the AI Tax!)

This does not mean AI is bad. It just means the way we use it matters, and we have a lot of work to do.

The intelligent web cannot be built on cognitive surrender nor on through a heavy AI tax. It has to be built on cognitive augmentation and human empowerment.

That means AI should not replace our thinking, it should provoke it. It should not flatten our creativity. It should expand it. It should not remove the human from the equation. It should make the human more capable, more curious, more discerning, and more useful.

The future of the web does not belong to those who can produce the most.

It belongs to those who can matter the most.

AI Slop Is the New Spam

Before we continue, we have to talk about slop because it perfectly captures what happens when speed and scale divorce themselves from quality and care.

Merriam-Webster named “slop” its 2025 Word of the Year, defining it as low-quality digital content produced usually in quantity by artificial intelligence.

AI slop is what happens when organizations confuse publishing content with serving people. It is what happens when marketers use AI to meet quotas instead of fulfill needs. It is what happens when agencies sell volume instead of value. It is what happens when creators stop creating and start outsourcing their voice to the statistical average of the internet to hit arbitrary OKRs.

The problem is not that AI can’t create good content. The problem is that too many people use AI to create content without purpose, empathy, originality, or accountability.

And when that happens, trust erodes (hello LinkedIn peers!)

In the keynote, I made the point that when we use AI to scale content without thinking about the person on the other side, we are not just producing more noise. We are creating more work for everyone else. We are increasing the burden required to find what is useful, true, relevant, and actionable. That burden is the AI tax.

Humans pay it when they have to wade through garbage. And the won’t for long.

Agents will pay it when they have to determine what is credible enough to summarize, recommend, or act upon.

Brands will pay it when they are no longer trusted.

As the web becomes more polluted with synthetic sameness, the value of signal increases. Originality becomes more valuable. Authority becomes more valuable. Empathy becomes more valuable. Taste becomes more valuable. Trust becomes more valuable.

Slop creates the opening for substance, and that window is only growing.

Content Is No Longer King, Context Is (It’s Always Been)

For years, we said content was king and for years, in every era of the web, I’ve said that context is king.

In the early web, that made sense. Content gave people reasons to visit. Content helped brands become visible. Content powered search. Content built audiences.

But in the intelligent web, content alone is not enough.

Context is king.

Context means relevance. It means usefulness. It means the right information, in the right format, at the right moment, for the right human or agent trying to achieve a specific outcome. It is not just what you publish. It is why it matters, who it serves, how it is understood, and what it helps someone do next.

People do not search because they want search results. They search because they want progress and outcomes. They want an answer. A recommendation. A decision. A purchase. A fix. A path. A moment of clarity.

Agents are no different. They will not browse like humans. They will parse, compare, evaluate, summarize, rank, recommend, and act. They will look for structured meaning. They will reward clarity. They will need evidence. They will need permission and governance. They will need signals of trust.

This is why the intelligent web is not merely a publishing challenge. It is an experience design challenge.

We are no longer just designing pages. We are designing outcomes.

From Search Engines to Answer Engines to Action Engines

Search is changing faster than most organizations are prepared to admit.

Pew Research Center found that Google users who encountered an AI summary clicked a traditional search result in 8% of visits, compared with 15% for users who did not see an AI summary. Pew also found that users clicked links inside AI summaries in only 1% of visits.

Source: HUMAN, “The 2026 State of AI Traffic & Cyberthreat Benchmark Report“

This is the shift from search engines to answer engines.

But the next phase is already emerging: action engines.

OpenAI describes ChatGPT agent as a system that can reason, research, navigate websites, fill out forms, work with files, connect to tools, and take actions on a user’s behalf. ChatGPT Atlas extends that idea into the browser, where users can ask ChatGPT to take action directly on the web.

That changes the game.

When a person asks an agent to find a product, compare providers, book a reservation, evaluate software, renew a subscription, or resolve a service issue, your website is no longer simply being visited. It is being interpreted. It is being judged. It may be acted upon without the human ever experiencing your carefully designed funnel.

The customer journey does not disappear. It becomes mediated. And if discovery, comparison, and conversion are increasingly mediated by AI, then brands must design for both the human and the machine. Not one or the other. Both.

That means your content must be understandable. Your data must be structured. Your policies must be clear. Your value must be legible. Your trust signals must be visible. Your experience must be actionable.

SEO is not dead. But SEO is no longer enough.

We now need to think in terms of AI search visibility, generative engine optimization, answer engine optimization, and agentic optimization. WP Engine’s DE{CODE} agenda reflected this shift directly, with tracks covering agent-ready architecture, structured data, semantic HTML, AI citation strategies, and agentic commerce.

The old equation was: Create content. Win search. Earn attention.

The new equation is: Create meaning. Earn trust. Become the source. Enable action.

The Website Becomes an Intelligent Business Platform

A website used to be a destination.

Then it became a channel.

Now it is becoming something more dynamic: an intelligent business platform.

That means your site is not just where people learn about you. It is where humans and agents understand what you stand for, what you offer, how credible you are, how to transact with you, how to resolve issues, and how to continue the relationship.

This requires a different mindset.

A site built for the intelligent web needs to be human-readable and machine-readable. It needs to be emotionally resonant and structurally clear. It needs story and schema. It needs brand and metadata. It needs originality and operational readiness.

This is where many organizations will get stuck. They will treat machine readability as a technical SEO project. They will add schema, update tags, chase citations, and drive links.

Important? Yes.

Sufficient? No.

The intelligent web is not just about being found by machines. It is about being trusted by machines because you are useful to people.

That is a very different bar.

AI Becomes the UI

One of the most important ideas from the keynote is also one of the simplest:

AI becomes the UI.

As AI mediates more interactions, the interface between your brand and your customer may not be your homepage, your app, your landing page, or your ad. It may be a summary. A recommendation. A comparison table. A voice response. A video. A purchase agent. A service agent. A synthetic answer built from signals across the web.

Content gets indexed.

Context provides framing.

Your brand gets chosen.

That means brand is no longer just what you say about yourself. Brand is what systems can understand, verify, summarize, and recommend about you. It is what you produce. What you enable. What you help someone accomplish. It is your usefulness made visible.

In the keynote, I asked the question I keep coming back to: #WDYSF

WDYSF: What Do You Stand For?

If AI systems are going to interpret, summarize, rank, recommend, and act based on what they understand about you, then ambiguity becomes expensive. Generic positioning becomes dangerous. Slop becomes self-sabotage. Your brand must become a signal strong enough for people and machines to recognize.

The intelligent web rewards clarity.

So, what do you stand for?

What do you make easier?

What do you make possible?

What do you help people become?

Source-Worthy Is the New Search-Worthy

In a world of infinite output, being searchable is not enough. You have to become source-worthy.

Source-worthy content is not content created to fill a slot. It is content created to be relied upon. It has perspective. Evidence. Utility. Originality. Context. It helps someone or something (ahem, AI agents) make a better decision.

AI systems need sources to synthesize from. Agents need signals to act upon. Humans need confidence to trust what they are seeing.

In the keynote, I encouraged leaders, marketers, agencies, creatives, publishers, eCommerce brands, and SMBs to stop trying to outpublish everyone. That is the wrong race. The goal is to out-clarify, out-serve, out-story, out-experience, out-community, and ultimately out-trust them.

That is such an important distinction.

The intelligent web does not need more content. It needs better answers.

It needs better experiences. It needs better signals. It needs more humanity.

The Rise of Agent-Ready Commerce

Commerce is about to become more agentic.

Today, we search, compare, click, browse, filter, read reviews, abandon carts, return later, and maybe convert. Tomorrow, agents will do more of that work for us. They will search across providers, compare options, evaluate constraints, check preferences, manage timing, apply policies, and potentially initiate transactions.

I shared an example in the keynote of using OpenAI’s Atlas browser to help pursue hard-to-get restaurant reservations in New York. The point was not restaurants. The point was delegation. I wanted an outcome. The agent helped with research, availability, waitlists, and next steps. But the desire, the context, the judgment, and the meaning were still human.

This is the future of commerce.

Humans will still want things. Agents will help fulfill those wants.

That means businesses need to design journeys that agents can understand and navigate without losing the emotional, experiential, and trust-building elements that make people choose one brand over another.

Agent-ready commerce is not just about transactions. It is about continuity. It is about service. It is about permissions, security, identity, policy, and trust. It is about knowing when an agent can act, when a human should be consulted, and how the experience should feel when the person re-enters the loop.

This is why the future of eCommerce is not just conversion optimization.

It is relationship architecture.

What Leaders Must Do Now

Leaders have a simple but difficult job in this moment: decide what kind of company they are becoming as discovery, comparison, and conversion become increasingly mediated by AI.

That means asking harder questions.

Are we using AI to automate yesterday’s assumptions, or to imagine tomorrow’s possibilities?

Are we creating more content, or more clarity

Are we optimizing for traffic, or designing for trust?

Are we making our business easier for agents to understand and act upon?

Are we building experiences people can feel and machines can verify?

The intelligent web is not an IT initiative. It is not a marketing initiative. It is not an agency initiative. It is a business reinvention initiative.

It belongs at the leadership table because it changes how customers discover you, how they evaluate you, how they buy from you, how they trust you, and how they come back.

What Marketers Must Do Now

Marketers are not just producing campaigns anymore.

They are shaping a knowledge layer that AI systems will use to describe, compare, and transact with the brand. That means every asset becomes part of a larger system of meaning.

Your messaging has to be consistent. Your claims have to be supportable. Your expertise has to be findable. Your content has to help people take the next step. Your brand has to show up with clarity across text, audio, video, community, documentation, reviews, commerce, and service.

Marketing becomes less about pushing messages and more about designing moments of trust.

What Creatives Must Do Now

Creatives have an extraordinary opportunity.

AI can generate. But it cannot care.

It can mimic style. But it cannot originate from lived experience.

It can remix patterns. But it does not possess intuition, empathy, taste, or conviction.

That is the creative advantage.

In an era of synthetic abundance, originality becomes premium. Voice becomes premium. Experience design becomes premium. The ability to make someone feel something becomes premium.

Creatives should not compete with AI at the level of output. They should use AI to expand their range, accelerate exploration, test possibilities, and then bring the human magic that AI cannot manufacture.

The intelligent web needs more than information.

It needs imagination.

What Agencies Must Do Now

For agencies, this is a control-alt-delete moment.

The old model of building sites, producing content, running campaigns, and optimizing for search is being rewritten. Clients will still need those things, but they will need them in service of a larger mandate: becoming visible, credible, recommended, and actionable in an AI-mediated web.

That makes agencies more valuable, not less, if they evolve.

Agencies can become advisors of trust. Architects of agent-ready experiences. Designers of relevance. Builders of intelligent business platforms. Translators between brand, technology, data, content, commerce, and customer experience.

The best agencies will not sell more output.

They will sell better outcomes.

What SMBs Must Do Now

Small and mid-sized businesses should not try to outpublish bigger brands.

That is not the game.

The advantage for SMBs is closeness. Specificity. Community. Service. Story. Trust. The ability to be human in a web that is becoming increasingly synthetic.

Do not fall into the AI slop business.

Use AI to understand your customers better. Use it to answer real questions. Use it to improve service. Use it to make your expertise clearer. Use it to become easier to find, easier to trust, and easier to choose.

Bigger brands may have more content.

You can have more meaning.

No Matter Who You Are, Just Be the Signal

The intelligent web is already messy.

AI-generated writing is exploding. Whether one treats that as an exact measurement or a directional signal, the implication is impossible to ignore: when production becomes infinite, discernment becomes priceless.

We are also seeing the same pattern in software. Fast Company reported that Apple’s App Store saw 235,800 new apps in the first quarter of 2026, up 84% from the same period the prior year, as AI tools made app creation faster and easier. This is what happens when creation gets democratized before quality systems catch up.

More becomes meaningless.

Speed becomes slop.

Volume becomes noise.

And that is why this moment belongs to the signal makers.

Be the source. Be the standard. Be the reprieve. Be the thing someone finds and feels: finally, this is what I was looking for.

The Architects of the Intelligent Web

The intelligent web will not build itself.

Or, more accurately, it will build itself badly if we leave it to automation alone.

This is our moment to shape it with purpose, imagination, empathy, and responsibility. To design for humans and agents. To build for discovery and action. To create source-worthy content. To make sites machine-readable and machine-actionable. To protect people, data, identity, and trust. To turn brand into a signal that stands out in a sea of sameness.

That is why I hope you watch the full keynote.

Not because it has all the answers. It does not. None of us do yet.

But it does frame the questions every leader, marketer, creative, developer, agency, publisher, eCommerce company, and SMB needs to ask now.

The web is becoming intelligent.

The future is becoming agentic.

And we are not bystanders.

We are the architects of the intelligent web.

So let’s build something worthy of the humans it serves.

Infinite ∞ | Mindshift | Subscribe | Keynote Speaker

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via Stereo Cien, MARÍA SOLEDAD GUIJOSA VIVANCO

En una época marcada por la hiperconectividad, las notificaciones constantes y el exceso de información, el reconocido futurista y analista digital Brian Solis presenta una propuesta para recuperar el control sobre el tiempo, la creatividad y el bienestar personal en su libro El camino lo creas tú: : Vive una vida más creativa, productiva y feliz.

Conocido internacionalmente por sus trabajos sobre innovación, transformación digital y experiencia del cliente, Solis cambia el enfoque que lo hizo famoso para explorar una pregunta cada vez más relevante: ¿cómo construir una vida con propósito en un mundo que compite permanentemente por nuestra atención?

Lejos de plantear un rechazo a la tecnología, el autor sostiene que el verdadero desafío consiste en aprender a utilizarla de manera consciente. A lo largo del libro explica cómo la economía de la atención ha modificado nuestros hábitos, reduciendo la capacidad de concentración y afectando la creatividad, la productividad e incluso la salud emocional.

A través de un recorrido que combina investigación, experiencias personales y ejercicios prácticos, Solis invita al lector a detenerse, replantear prioridades y recuperar espacios de reflexión. Conceptos como el enfoque, el silencio, la visualización y la atención plena se convierten en herramientas para romper con la dependencia de las pantallas y volver a tomar decisiones alineadas con los objetivos personales.

Más que un libro de autoayuda, El camino lo creas tú funciona como una guía para comprender el impacto de la tecnología en la vida cotidiana y aprender a convivir con ella sin perder el equilibrio. El autor plantea que el éxito no depende únicamente de trabajar más o estar siempre disponible, sino de desarrollar la capacidad de concentrarse en lo verdaderamente importante.

Brian Solis es considerado una de las voces más influyentes en temas de innovación y transformación digital. Ha asesorado a empresas de alcance global y es autor de títulos como What’s the Future of Business?, X: The Experience When Business Meets Design, Lifescale y Mindshift, obras en las que analiza la relación entre las personas y la evolución tecnológica.

Con El camino lo creas tú, Solis amplía esa conversación hacia el terreno del desarrollo personal, ofreciendo una reflexión oportuna para quienes buscan encontrar un mejor equilibrio entre el mundo digital y la vida real.

Compra el libro y comienza tu nuevo viaje y también aquí: Amazon.com.mx

Lifescale

The post Brian Solis propone una nueva forma de vivir en El camino lo creas tú: Vive una vida más creativa, productiva y feliz appeared first on Brian Solis.

It was an honor to join Johan Roos and Deborah Perry Piscione at the 17th Global Peter Drucker Forum for a session with a title that left little room for abstraction: “The CEO Guide to Using AI.”

Johan is a respected management scholar, educator, author, and longtime contributor to the Drucker Forum. Deborah is a Silicon Valley entrepreneur, investor, and author studying how AI, robotics, and other disruptive technologies are reshaping employment and society. I joined them as a practicing futurist in my role at ServiceNow, and, as I introduced myself that morning, still very much a student of AI.

Anyone who tells you they have mastered this moment probably has not spent enough time understanding the technology and its impact on work, the workforce,  and on ourselves.

Johan opened the session by asking each of us to make a strong claim, a provocation, something expressed to raise the collective heart rate of the room. A thoughtful discussion, after all, should do more than confirm what everyone already believes.

So I offered mine:

CEOs are on a path to automating themselves out of a job.

My point was not that an algorithm would soon occupy the C-Suite, though there are parts that could. My argument was that many leaders are using AI to do what their organizations already do, only faster, cheaper, and at greater scale.

They are automating tasks, accelerating processes, increasing output, reducing costs, and optimizing operating models built for another era. Those improvements and incremental gains are real. They may even generate impressive returns over the next quarter.

But a faster version of yesterday’s company is not necessarily prepared for tomorrow. This is a different moment in time.

The defining responsibility of a CEO is not to make the past run more efficiently. It is to imagine and build the future before someone else does.

That became the central tension of our conversation.

Deborah challenged CEOs to understand the economic and technological forces already reshaping work.

Johan challenged leaders to protect and expand human capacity as AI becomes more pervasive. I tried to connect those provocations to the choices CEOs and boards must make now.

Looking back, the session was less of a panel and more an intervention about leadership.

AI Can Help CEOs Manage. The Opportunity Is to Help Them Lead

There are certainly CEOs who are challenging the status quo. There are CEOs who recognize they’re not the right leaders to see AI business reinvention through. And there are CEOs already outsourcing their thinking to AI. In fact, a recent study showed 62% of CEOs reported using AI to make the majority of their decisions. 70 percent admitted to second-guessing their judgements when their choices conflict with AI’s recommendations. 65 percent felt decision-making had become less collaborative since adopting AI. And to top it off, 46% said they now rely on AI more than on the advice of colleagues.

One CEO in the audience offered a useful distinction. AI, he explained, could automate much of his work “below the line”: reviews, analysis, administration, reporting, and other activities that consume executive attention. That would give him more time “above the line” to think about the future of the product, the company, and the market.

I told him he was a blueprint for other CEOs.

But no one in the room asked what they weren’t thinking about, what they hadn’t considered, or what other CEOs or founders were doing differently. And when curiosity or humility aren’t given space to explore what we don’t know to know, any conversation will be limited to validation, agreement, incremental thinking, and reinforcing existing confirmation biases.

That’s exactly why for transformation, growth, and innovation to materialize, leaders must first recognize that there may be other questions, scenarios, and opportunities they’re not yet considering. This is the inception of a mindshift. And from there, an infinite journey can unfold.

His point captured the opportunity. AI can give leaders back time, but what they do with that time will determine whether it becomes a source of reinvention or merely another productivity tool.

Too many executives have gradually become managers of institutional complexity. Their days are filled with meetings about the business instead of time spent imagining the future of the business. They review dashboards, manage expectations, navigate politics, prepare for the next board meeting, and protect the operating model.

AI can make those activities more efficient. But leadership is not the administrative maintenance of the present. That’s ‘management.’ Leadership is the ability to see possibilities others do not yet see, articulate a future people can believe in, and create the conditions for the organization to bring it to life.

AI can help CEOs manage more effectively. The real opportunity is to help them lead more imaginatively.

Months after the Drucker Forum, I led a series of workshops with roughly 100 CEOs and chief operators in Arizona. The stated subject was AI, but the real conversation was leadership. Once we created a trusted setting, several executives admitted a version of something they rarely say publicly: “I know this matters. I know we have to move. But I’m not sure where to begin, and I’m not sure the organization is ready.”

That admission was not a failure of leadership. It was the beginning of it. And that’s the point. Humility and curiosity was in the room.

The greater danger is pretending to know, rushing into activity to project confidence, and allowing urgency to substitute for direction.

Deborah Perry Piscione: Understand What Is Coming, Then Decide How to Participate

Deborah brought urgency to the conversation from her vantage point in Silicon Valley. She described a world moving rapidly from generative AI to agentic systems and eventually to physical AI and humanoid robotics. Her message was not that every prediction from Silicon Valley should be accepted as destiny. It was that CEOs cannot afford to remain spectators while the economics of cognitive and physical work are being rewritten.

Her practical advice began with experimentation. Leaders need direct experience with the technology. They must understand what it can do, how quickly it is improving, where it fails, and how employees, customers, entrepreneurs, and competitors are already applying it.

This does not mean a CEO must become an AI engineer. It does mean AI literacy can no longer be delegated entirely to the CIO, a center of excellence, or the unusually enthusiastic person who discovered prompting last Tuesday.

Deborah also advanced an essential strategic choice…should the organization build its own AI capabilities, buy them from trusted providers, or blend internal and external systems?

Her answer was appropriately contextual. Build where proprietary intelligence is central to competitive advantage. Buy where established platforms can provide common capabilities faster and more effectively. Blend when enterprise knowledge, specialized models, external innovation, and human expertise must work together.

But before any of those choices can produce value, Deborah argued, leaders must fix the plumbing. Data has to be usable. Legacy systems must be capable of communicating. Information must move securely across the organization. The infrastructure underneath AI may not be glamorous, but neither is explaining to the board why the brilliant proprietary model cannot access the information required to do its job.

Her point has only become more relevant. In an article I later contributed to Future Economy, I described an emerging enterprise AI transformation gap: companies are producing more intelligence at the individual level, yet they still lack the architecture to let that intelligence act across workflows.

An intelligent model placed inside a fragmented enterprise does not make the enterprise intelligent. It may simply help dysfunction move faster.

Stop Piloting AI Around the Edges of the Business

When an audience member asked about reports showing widespread failure among generative AI pilots, I shared what I routinely see in my work with CEOs and boards.

The pilots are siloed. The data is siloed. The workflow is siloed. The ownership is siloed. The measurement is siloed.

The use case may perform perfectly within its narrow boundary and still fail to create meaningful enterprise value because it was never connected to the way value actually flows through the organization.

Siloed pilots produce siloed outcomes.

The better-performing AI transformations begin with an end-to-end workflow and a meaningful business result. That means looking beyond a single department or task to understand how people, data, systems, decisions, and policies come together to create an outcome for a customer, employee, or stakeholder.

Remember, AI is not a strategy.

The organization needs a strategy for competing and creating value in a world where intelligence is becoming abundant, inexpensive, embedded, and increasingly autonomous. AI is a critical part of that strategy, but it cannot be separated from decisions about customers, work, talent, operating models, growth, governance, and the future identity of the enterprise.

A budget, a timeline, a collection of pilots, and a slide featuring several concentric circles do not constitute transformation.

The more useful question of the moment is “What outcome should now be possible that was not possible before?”

The Danger Is Not Experimenting. It Is Failing to Dream Big Enough.

Near the end of the session, I returned to the opening provocation.

The danger is not that CEOs are experimenting with AI. The danger is that they are not dreaming big enough with it.

When leaders confront an unfamiliar technology, they naturally begin with what they know. They look at the present organization and ask where AI might reduce cost, eliminate effort, improve productivity, or replace labor.

Those are understandable starting points. They are also constrained by the assumptions of the existing business.

If you begin with yesterday’s processes, roles, metrics, and operating model, the most likely outcome is an AI-powered status quo.

You may become more efficient without becoming more innovative, more productive without becoming more valuable, or more automated without becoming truly transformed.

I later explored this at AI Week in Milan. Most organizations are using AI to improve the work they already perform. What they also need is a parallel growth curve dedicated to creating new capabilities, services, experiences, and business models. I describe these as iterative AI and innovative AI. The first improves today. The second invents tomorrow. Leaders need both, and they must prevent the efficiency agenda from consuming the reinvention agenda.

No CEO will be criticized for finding legitimate savings. But efficiency cannot become the outer boundary of imagination.

You cannot automate your way to innovation. You cannot cut your way to growth.

Innovation means creating new value. It means doing something tomorrow that the organization could not do yesterday.

Automate the Work. Augment the People.

I shared the example of IKEA during the panel because it illustrates the difference between treating AI as a cost lever and using it as a platform for growth.

IKEA introduced an AI-powered customer service capability that reportedly handled nearly half of its inbound inquiries. Viewed through an automation lens, that success could have justified significant headcount reduction. If the objective is simply to deflect customer contacts and take out cost, the equation is straightforward.

But IKEA looked at what the AI could not resolve.

Many of the remaining customers wanted help with interior design. Rather than treating those interactions as failures, IKEA recognized an unmet need. Thousands of customer service employees were reskilled as remote design advisers, creating a new service and a significant source of revenue.

The technology absorbed one form of work. The people moved into a more valuable form of work.

Since the forum, I have encountered other examples that reinforce the same lesson.

Ford leaned heavily on automated quality systems and later recognized that technology could not fully replace the judgment and tacit knowledge of experienced specialists. The company brought back more than 350 veteran technical experts—affectionately known as “gray beards”—to find failure points, mentor teams, and improve the systems. Klarna, after becoming one of the most visible symbols of AI-driven efficiency, also began reinvesting in human customer service when it became clear that speed and scale could not entirely replace empathy and human connection.

These stories should not be used to ridicule companies for getting AI wrong. Quite the opposite. They demonstrate what learning looks like.

The mistake is not experimenting and discovering limitations. The mistake is refusing to adapt because the original automation narrative looked better in the investor presentation.

The CEO’s job is to pursue two questions at once:

Where can AI remove friction, repetition, delay, and unnecessary cost?

And where can AI help people become more creative, informed, capable, empathetic, and valuable?

Automation can fund the future. Augmentation helps create it.

Johan Roos: Use AI to Expand the Mind, Not Escape It

Johan brought another essential dimension to the conversation. He argued that professionals, leaders included, face a choice in how they use AI.

AI can amplify curiosity, creativity, critical thinking, communication, and collaboration. Or, it can gradually erode them. It’s a topic I explored in-depth at SXSW 2026.

The risk is cognitive offloading: allowing AI to perform so much of the mental work that our own capacity weakens. This erosion is unlikely to arrive through one dramatic decision. It will happen through hundreds of convenient prompts and outputs.

AI writes the message. AI summarizes the report. AI prepares the argument. AI recommends the restaurant. AI advises us how to handle a disagreement. Eventually, we stop asking AI to help us think and begin asking it what we should think.

The technology becomes more capable while the user becomes less so.

Johan challenged leaders to use it deliberately. His recommendation was to role-model active, thoughtful experimentation and remain alert to the warning signs of over dependence. Use AI before an important communication, he suggested, but then show up as yourself. Do not allow leadership to converge into the same optimized, synthetic voice.

I agree, and I would extend the idea.

CEOs should use AI to improve the quality of their thinking, not to avoid the responsibility of thinking. More so, use AI to expose biases and “expert mind-sets.” Ask it to challenge assumptions, reveal blind spots, argue against the preferred strategy, model alternative futures, and expose uncomfortable trade-offs.

Do not use it to manufacture confidence where none exists.

This is also the thinking behind a concept I later developed called WWAID: What Would AI Do?

WWAID is not an invitation to outsource judgment. It is a pre-prompt mindshift. Before asking AI to improve an existing process, leaders step back and ask whether the process should exist in its current form at all. Before automating a task, ask whether intelligence could eliminate the need for it. Before adding AI to an experience, ask how intelligence might redefine its purpose and value.

The goal is to help us escape the limitations of how we have been trained to think.

Embodied Leadership Will Become More Important, Not Less

Johan also spoke about embodied leadership…the distinctly human experience of being present with another person, reading the room, looking someone in the eye, sensing hesitation, responding to emotion, and communicating with authenticity.

That idea came to life through an audience member who described receiving a question from her daughter. Unsure how to answer, she turned to AI and sent back the resulting response. Her daughter replied, in effect, “Mom, I wanted to talk to you, not AI.”

Ouch, but I’m sure it was a necessary lesson.

The daughter was not only looking for information. She was looking for connection.

Employees often want the same thing from leaders.

They do not only look to a CEO for an answer. They’re looking for judgment, reassurance, honesty, empathy, presence, and some indication that another human being understands what is at stake.

My answer was simple: humility, humanity, and empathy are among the most important leadership traits we can develop in the age of AI.

Then I added, “My goodness, be human.”

Johan connected that moment to the idea behind his work on “human magic”: the qualities we must hold onto and cultivate as algorithms become more capable.

The message has stayed with me.

As AI becomes more advanced, humanity does not become less relevant. It becomes more differentiated…and, augmented.

Leaders will need the humility to admit that they do not have all the answers. They will need the vulnerability to acknowledge uncertainty while still providing direction. They will need the courage to make decisions that balance economic performance with human consequences.

The CEO Guide to Using AI

Our conversation ranged across technology, work, employment, cognition, infrastructure, governance, accountability, and humanity. But the practical guidance for CEOs can be distilled into seven questions.

1. What future are we trying to create?

Do not begin with the model, tool, or vendor. Remember, AI is not the strategy. Begin with the customer, employee, business, or societal outcome the organization should now be capable of delivering.

2. Are we improving the existing company or inventing the next one?

Pursue efficiency and reinvention simultaneously. Improve today’s performance while creating the capabilities, experiences, and business models that could define tomorrow.

3. Which end-to-end workflows matter most?

Move beyond isolated tasks and departmental pilots. Examine how value actually moves across people, functions, systems, decisions, and data.

4. Is the organizational plumbing ready?

Address fragmented data, disconnected systems, siloed workflows, unclear ownership, permissions, security, governance, and technical debt. AI cannot orchestrate an enterprise that cannot communicate with itself.

5. Where should we automate, and where should we augment?

Use AI to eliminate mundane work, but reinvest the resulting capacity in people, learning, experimentation, service, creativity, and growth.

6. How will we protect and expand human judgment?

Define what AI can recommend, what it can decide, what it can execute, and where accountable human intervention remains essential.

7. How will I personally model the change?

A CEO cannot delegate curiosity. Use the tools. Ask better questions. Share what you are learning. Admit what you do not know. Encourage experimentation without pretending uncertainty has disappeared.

The Future Is Still a Leadership Decision

The session was titled “The CEO Guide to Using AI,” but the enduring lesson was not really about using AI.

It was about what kind of leader, and what kind of company, AI will reveal. This is a moment to invent the “infinite company.”

Deborah reminded us that the economics and capabilities of intelligent systems are moving quickly, whether organizations feel ready or not. Progress is not only a question of what technology can do, but also what we allow it to do to us. The audience brought the lived experience of executives navigating urgency, skepticism, responsibility, and uncertainty.

The leaders most vulnerable to AI are those who believe they have the least to learn, who confuse activity with progress, efficiency with growth, and those who use AI only to defend the business model that made them successful.

The defining work of leadership is not to preserve the company that exists. Yet so many are touting this as the next frontier. The real opportunity however is to build the company that deserves to exist next, and to help people become the best versions of themselves along the way.

Please watch the conversation here and share your thoughts!

Thank you for the opportunity to be part of this incredible opportunity, Richard Straub!

Infinite ∞ | Mindshift | Subscribe | Keynote Speaker

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Brian Solis has been named a Top Pop Culture, Futurist, and Trend Keynote Speaker by Futurist Speakers. Today’s Top 100 pop culture experts, keynote speakers, thought leaders, consultants and commentators help audiences understand the trends, entertainment, technology, consumer behaviors, and social movements that impact modern society. And of course the best Top 100 pop culture experts list picks analyze everything from movies and television to video games, celebrity culture, social media, music, fashion, fandoms, and emerging cultural shifts.

For corporate events, conferences, media appearances, and brand strategy sessions, thought leaders and SMEs can help organizations understand how audiences think, what drives attention, and how cultural trends influence consumer behavior.

Below is a list of celebrity Top 100 pop culture experts, commentators, creators, authors, entertainers, and cultural influencers in no particular order.

For booking inquiries, please reach out to Zach Nadler at Vayner Speakers.

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Artificial intelligence is transforming every industry, but simply using AI to automate outdated processes may not be enough to stay competitive.

In this conversation, Brian Solis, Head of Global Innovation at ServiceNow, and Dave Wright, Chief Innovation Officer at ServiceNow, explain why the next generation of market leaders won’t simply adopt AI, they’ll rebuild their organizations around it. Drawing from the ideas in their book, Infinite: How Visionary Leaders Transform Today’sBusinesses into AI-Forward Companies, Brian and Dave explore why traditional organizational structures are becoming obsolete in the AI era.

They discuss how AI-native companies are designing businesses around workflows instead of departmental silos, creating organizations that can continuously adapt, innovate, and unlock entirely new sources of value rather than simply reducing costs. The discussion also examines one of the biggest questions facing executives today: how to measure the true return on AI investments. Instead of focusing solely on productivity gains or headcount reductions, the conversation introduces the concept of “Return on Intelligence” using AI to augment human capabilities, accelerate innovation, and drive long-term growth.

Whether you’re a business leader, investor, entrepreneur, or technology enthusiast, this interview offers valuable insights into how AI is reshaping the future of enterprise strategy.

Thank you, Remy Blaire!

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Innovation creates the most value when it moves beyond ideas and into action. That belief is shaping the content at this year’s BrokerTech Connect, where brokerage leaders, technology innovators, and industry changemakers will come together for candid conversations about what it takes to build the brokerage of the future.

Brokers sit closest to clients and experience firsthand where processes break down, where technology falls short, and where better solutions could create meaningful value; however, too many industry conversations happen in isolation. BrokerTech Connect brings brokers, carriers, wholesalers, insurtech founders, investors, and technology leaders into one intentionally curated environment to challenge assumptions, share practical insights, and turn promising ideas into real progress. At a moment when AI, shifting client expectations, and new operating models are transforming insurance distribution, this kind of broker-led collaboration has never been more important.

Leading that conversation will be keynote speaker Brian Solis, a globally recognized digital futurist, digital anthropologist, and ten-time bestselling author. As Head of Global Innovation at ServiceNow, former Vice President of Global Innovation at Salesforce, and a special adviser to the United Nations’ global innovation team, Solis helps organizations understand emerging technology—and translate its possibilities into meaningful business strategy.

From generative AI and spatial computing to digital twins and quantum computing, Solis is known for making complex trends accessible and actionable. His future-focused perspective will challenge attendees to move beyond feeling overwhelmed by change and begin approaching it with clarity, curiosity, and intention.

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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

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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.

The post Future of Work: Why AI’s Biggest Opportunity For Businesses Goes Beyond Cost Cutting with Brian Solis appeared first on Brian Solis.

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

The post The Unreleased X Trailer: Designing Experiences People Love in the Age of AI appeared first on Brian Solis.

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

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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.

Infinite ∞ | Mindshift | Subscribe | Keynote Speaker

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Jeff Gritchen / MediaNews Group / Orange County Register via Getty Images

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.

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