The intelligent web is entering its next phase. AI systems are moving from reading and summarizing the internet to researching, deciding, transacting, and increasingly interacting with other agents. That changes what leaders have to design for now.

At WP Engine’s DE{CODE}, I explored an idea that has been occupying a lot of my thinking lately: the intelligent web. We are redesigning the internet for two audiences at once, human beings and the AI systems increasingly acting on their behalf. Search is becoming synthesis. Browsing is becoming delegation. Content is becoming context. Websites are evolving from destinations into intelligent business platforms.

My keynote at the Agent-to-Agent GTM Summit gave me an opportunity to take that thinking one step further.

AI is beginning to participate in the market.

HUMAN Security’s 2026 State of AI Traffic & Cyberthreat Benchmark Report found that monthly AI-driven traffic grew 187% over the course of 2025, while traffic from AI agents and agentic browsers grew 7,851% year over year.

Agentic traffic is still young as a share of the overall web, which explains some of that extraordinary growth rate, but the direction is unmistakable, machines are starting to navigate and act across the internet with purpose.

This is where the intelligent web begins to become a machine-native market.

When Intelligence Becomes a Market Participant

 Almost everything we built on the commercial internet assumed a human being was sitting on the other side of the screen. We designed pages for eyeballs, navigation for fingers and cursors, copy for action, forms for human input, and checkout to transact.

Agents are pursuing many of the same outcomes through a very different interface with the world.

They parse. Compare. Verify. Summarize. Rank. Recommend. Ask other systems for information. Invoke tools. Request permission. Execute transactions. Escalate when they reach the boundaries of their authority.

That changes the design brief.

In my A2A keynote, I defined a machine-native market as one where intent, identity, trust, price, availability, proof, permission, payment, service, and fulfillment are discoverable, readable, verifiable, and actionable by agents.

There is a lot packed into that definition because a machine-native market requires us to move beyond the visible web. Product information, pricing, policies, credentials, availability, permissions, APIs, transactions, workflows, customer proof, service capabilities, identity, and provenance all become part of the experience.

For the last three decades, companies obsessed over what customers could see. The next era asks leaders to become equally intentional about what machines can understand, verify, and do.

The Agent Endpoint Becomes the Storefront

In the human-first web, the website became the storefront. In a machine-native market, the agent endpoint becomes a storefront, too.

Imagine a B2B buyer beginning with a goal instead of a search query: “Find the best platform for our requirements, compare three vendors, evaluate total cost, confirm security and compliance, identify implementation risks, and bring me a recommendation.”

That single request could mobilize a research agent, procurement agent, security agent, finance agent, architecture agent, and eventually the seller’s own agents. The human executive may enter the experience after much of the discovery and evaluation has already happened.

This is already becoming technically possible because an interoperability layer is emerging around agents. Anthropic introduced the Model Context Protocol (MCP) as an open standard for connecting AI applications with external data and tools. Google introduced Agent2Agent (A2A) so agents built on different systems can discover one another, exchange information, and coordinate work.

Commerce is moving in the same direction. Stripe and OpenAI developed the Agentic Commerce Protocol (ACP) to enable commerce flows among buyers, AI agents, and businesses, while Google’s commerce and payments work is establishing mechanisms for agent-driven transactions and proof of authorization.

These protocols will evolve. Others will emerge. The strategic signal matters more than any one standard: the web is acquiring an operating layer for machines that can communicate, coordinate, and transact.

For leaders, that means your next customer journey may begin somewhere you do not control, move through systems you never designed as customer touchpoints, and reach a decision before a person ever visits your homepage.

The Funnel Becomes a Decision Graph

 For years, the digital GTM equation followed a familiar sequence: create content, optimize search, earn attention, drive traffic, convert. That model gave us the funnel.

Machine-mediated journeys behave more like a decision graph.

A human’s intent can trigger a network of queries, comparisons, validations, permissions, transactions, and handoffs. Some occur sequentially. Others happen at the same time. Some are handled entirely by machines until the decision reaches a threshold where judgment, authorization, ambiguity, or emotion requires a person.

This gives us new moments of truth to design around.

Can an agent find the right product and understand who it is for? Can it determine whether pricing is current? Can it substantiate a claim? Can it compare your offering with a competitor without guessing? Can it verify security and compliance? Can it understand what it is authorized to do? Can it complete the transaction? If something fails, can it recover? When judgment is required, can it reach the right human with the right context?

Every “no” in that journey becomes friction.

Every ambiguity becomes risk.

Every contradiction becomes a reason to choose someone else.

That is why the shift from funnel to decision graph matters. The work is no longer concentrated at the point of conversion. Trust, context, permission, and actionability have to survive the entire journey.

Trust Becomes Machine-Readable

 Trust has always been one of the great currencies of business. The difference now is that some of it must become legible to machines.

For a person, trust can be emotional and experiential. We remember how a company made us feel. We respond to story, reputation, identity, design, service, values, and human judgment.

An agent needs evidence it can evaluate.

That is what I mean by machine-readable trust: the ability for an agent to verify who you are, what you sell, what is true, what is current, what is authorized, what is compliant, what has been proven, and what happens next.

Think about how much of that information exists today in disconnected places. Product claims live on web pages. Pricing sits in databases. security certifications live in portals. Legal terms are buried in documents. Customer proof appears in case studies. API documentation exists somewhere else. Policies may contradict implementation reality. Escalation paths often live in the heads of employees.

A human can sometimes work through that mess. An agent will interpret the mess as uncertainty.

An agent-ready company starts turning that uncertainty into verifiable infrastructure: structured product and pricing information, machine-readable documentation, permissioned data access, clear policy boundaries, authentication, decision logs, procurement-ready evidence, support workflows agents can invoke, and explicit escalation rules.

This is where one of my favorite equations from these keynotes starts to become very real: AI becomes the UI. Trust becomes the click.

In an AI-mediated journey, the interface may be a recommendation, a comparison, a voice response, a purchasing agent, or a service agent. The “click” becomes the moment intelligence decides there is enough confidence to proceed.

Brand Has Two Audiences Now

This changes brand in an important way.

Brand has always lived in memory. Increasingly, it will live in two forms of memory at once.

For humans, brands create emotional memory. For machines, brands need verifiable evidence.

Those two jobs belong together. A company can be machine-readable and completely forgettable. It can also be beloved by people and nearly impossible for an agent to evaluate or transact with. The companies that matter in a machine-native market will learn to do both.

This builds directly on the argument I made at WP Engine. We are entering an era of abundant synthetic output, and abundance changes what becomes scarce. When everyone can publish more, generate more, automate more, and fill every channel with more, more stops mattering. The premium moves to originality, usefulness, specificity, authority, empathy, taste, and trust.

I called that becoming source-worthy: creating information and experiences credible enough for people to rely on and AI systems to cite, synthesize, and recommend.

Machine-native markets extend that idea.

Source-worthy gets you into the decision. Agent-ready helps you become the decision.

Your brand becomes a signal interpreted by humans and machines, and clarity becomes a competitive advantage.

What do you stand for? What problem are you uniquely equipped to solve? What can you prove? What can you make easier? What outcome can someone trust you to deliver?

I have asked some version of those questions throughout my career. AI gives them new urgency because ambiguity becomes expensive when intelligence is interpreting you at machine speed.

CX + UX + AX

We spent decades building disciplines around customer experience and user experience. Machine-native markets introduce another layer I believe leaders should start taking seriously: Agent Experience, or AX.

An agent has an experience with your company even if it does not “feel” that experience the way a human does. Its experience is expressed through friction, confidence, completion, recovery, and the quality of the outcome it can return to the person it represents.

Stale pricing is AX friction. Conflicting claims are AX friction. An API that exposes information but cannot support action is AX friction. Ambiguous permissions, authentication loops, inaccessible documentation, opaque policies, and failed transactions without recoverable paths are all AX friction.

I think organizations should begin measuring something I would call Agent Experience Debt. It’s the accumulated complexity, inconsistency, opacity, disconnected systems, and inaccessible knowledge that make it difficult for an agent to confidently understand or interact with the business.

Today, that debt creates work for customers and employees. I call that added burden the AI Tax when technology supposedly designed to make things easier shifts more work onto the human on the other side.

Tomorrow, Agent Experience Debt could have a more direct economic consequence. Your company may become difficult for agents to interpret, expensive for them to work with, harder to verify, or easier to exclude from consideration.

From GTM Stack to Market Operating System

This is why I believe go-to-market has to evolve into something closer to context architecture.

Marketing, product, commerce, IT, security, legal, service, and data teams are all shaping pieces of an experience that agents will traverse as one system. Marketers are helping construct the knowledge layer AI uses to understand the company. Product teams are designing machine-actionable capabilities. IT is exposing tools and data safely. Security is establishing identity and permissions. Legal is turning policy into boundaries that machines can follow. Commerce teams are enabling transactions. Service teams are building workflows agents can invoke.

The customer sees the outcome. The agent experiences the architecture underneath it.

At the A2A Summit, I proposed a simple Market OS as a way for leaders to begin organizing this work:

1. Search and discovery: understand where AI systems discover, synthesize, answer, recommend, and increasingly act. Audit what they can learn about your company and where the signal breaks down.
2. Context: make product facts, claims, documentation, proof, pricing, availability, policies, and outcomes structured, current, specific, attributable, and useful.
3. Protocols: decide how agents can connect to your systems, data, tools, and other agents. MCP and A2A are early examples of this emerging interoperability layer.
4. Transactions: define what an authorized agent can actually accomplish: purchase, pay, schedule, provision, renew, return, invoke service, check fulfillment, resolve a problem, or escalate. ACP, UCP, and AP2 are signals that commerce itself is becoming agent-ready.
5. Trust: operationalize provenance, verification, auditability, permissions, policy, agent identity, decision logs, proof, and human escalation.

The leadership question changes from “How do we show up in AI?” to something much more consequential: 

How does intelligence experience our company? What can it discover? What can it understand? What can it verify? What can it do? Where does it encounter uncertainty? When should it escalate? What would make it recommend us? What would make it return?

Those are the questions I would put on the agenda of every leadership team now.

The More Agentic Markets Become, the More Human the Important Moments Can Be

 There is a fascinating paradox emerging alongside all of this.

As more of the journey becomes automated, the moments that remain human can become more valuable.

Gartner reported in March 2026 that 67% of B2B buyers prefer a rep-free experience. Two months later, Gartner reported that 69% of B2B buyers turn to sales representatives to validate AI-generated insights.

I love the tension in those two findings because it points toward a much more interesting future for sales than “AI replaces sellers.” Buyers want independence when independence serves them. They want people when judgment, reassurance, complexity, risk, alignment, and trust become consequential.

Agents can absorb more research, comparison, qualification, procurement, coordination, and routine service. That creates an opportunity to redesign human work around what people do exceptionally well: judgment, empathy, imagination, negotiation, creativity, relationships, and navigating ambiguity.

Sales can become more human in the moments that matter. Service can, too.

We have spent years treating service as a cost center to be optimized. Yet service often happens at the most emotionally charged moment in the relationship: something broke, something is late, someone is confused, money is at stake, or trust is suddenly fragile. Automation can resolve the predictable. People should be empowered to make the ambiguous and emotional moments exceptional.

That is the human side of machine-native design.

Human-First Is the Soul, Machine-Native Is the System

The intelligent web is growing into a market environment where people, agents representing people, agents representing companies, and agents interacting with other agents will increasingly coexist.

We are early enough that many of the rules, interfaces, norms, protocols, and expectations are still being invented. That is what makes this moment so exciting.

We get to decide how trust works. We get to decide how permission works. We get to decide how agents represent our interests and our brands. We get to decide where autonomy ends and human judgment begins. We get to rethink discovery, commerce, service, experience, GTM, and the very architecture through which companies participate in markets.

The imperative I shared at the end of the A2A keynote still feels like the right design principle. It’s this, Human-first becomes the soul. Machine-native becomes the system.

Build the system so machines can understand you, verify you, work with you, and act.

Design the experience so the human outcome remains meaningful, useful, trustworthy, and worth choosing.

The web is becoming intelligent. The market is becoming agentic. Intelligence itself is becoming a participant.

And we are the architects of what comes next.

Infinite ∞ | Mindshift | Subscribe | Keynote Speaker

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AI has a leadership problem. For the last few years, people have been told that AI is coming for their jobs, their careers, even their relevance. Then we act surprised when people respond with fear, anger, or outright rejection.

I recently joined my new friend Cal Fussman to talk about why I believe this is a leadership failure, and why we still have an opportunity to change the future.

I shared a moment that changed my own thinking. After listening to Geoffrey Hinton (watch the video here) describe a future in which humans may no longer be the apex intelligence, I found myself confronting a my truth:

There are things I cannot control. But there are also things I can. That was my Mindshift moment.

Instead of asking what AI will do to us, we can start asking what becomes possible with it.

What can I do now that I couldn’t do before?

What can AI do that it couldn’t do without me?

That is the difference between artificial intelligence and augmented intelligence.

The future doesn’t have to be something we inherit. Instead, we must participate in shapingit.

Thank you, Cal, for such a thoughtful conversation. I look forward to our next one!

Please watch and share your thoughts!

p.s. this interview was scheduled to talk about Dave’s and my new book, Infinite. I’m not sure we actually got to it!

The post Stop Asking What AI Will Do to Us and Start Asking What We Can Do With It that makes us stronger 💪🦾 appeared first on Brian Solis.

AI is moving quickly into every corner of the enterprise. But I think we’re still asking questions that are far too small for the moment.

How do we automate more? How do we increase productivity? How do we reduce costs?

I recently joined Ray Wang and Vala Afshar on DisrupTV with my Infinite co-author and ServiceNow Chief Innovation Officer Dave Wright to explore a much bigger question:

What would you build if AI gave you the opportunity to rethink the business itself?

That is the premise behind Infinite: How Visionary Leaders Transform Today’s Businesses into AI-Forward Companies.

Dave and I believe we’re entering a period where competitive advantage will increasingly come from an organization’s ability to rethink how work gets done, how decisions get made, how humans and intelligent agents collaborate, and how value moves across the enterprise.

That requires moving beyond isolated AI pilots and incremental efficiency plays. It means redesigning workflows around outcomes. It means giving intelligent systems the ability to sense, reason, act, and learn within the right guardrails. And it means measuring AI not only by hours saved or headcount reduced, but by the new value, growth, intelligence, and capacity an organization creates.

This is the difference between using AI to optimize the business you have and using it to invent the business you need next.

We covered all of this and more in a wide-ranging conversation about AI-forward companies, agentic execution, the future of work, and why this moment calls for something more ambitious than transformation as usual.

Infinite ∞ | Mindshift | Subscribe | Keynote Speaker

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Via Chief Executive

We’re making the same decisions with AI. But for leaders who ask different questions, AI changes the assignment.

Dave Wright and Brian Solis share lessons for CEOs from their new book, Infinite: How Visionary Leaders Transform Today’s Businesses into AI-Forward Companies.

5 AI decisions every CEO must own before agents start running the business

For years, CEOs could delegate digital transformation. Digital transformation often lived somewhere between the CIO, the CDO, a transformation office and a series of platform modernization programs. The company bought new systems, migrated old processes, digitized workflows and measured progress in efficiency, speed, adoption and cost savings.

It was important work. But in hindsight, much of it wasn’t transformation. “Digital” didn’t really change as much as it optimized from the analog and disparate processes of yesteryear. So technically, those investments were more about digitization than “digital transformation.” We took the analog business and placed it online. We moved software and data to the cloud. We modernized departments. We optimized silos. We made yesterday’s company faster and more efficient.

We’re making the same decisions with AI. But for leaders who ask different questions, AI changes the assignment.

his is not digital transformation 2.0 now with AI. This is AI business reinvention.

AI agents don’t simply answer questions or summarize documents. They can, but where’s the transformation in that? Increasingly, they perceive, reason, decide, coordinate, transact and act across systems. They don’t respect org charts. They expose friction. They’re impeded by operational and data siloes. But, they learn from outcomes. They move through workflows. They connect disparate handoffs. They become teammates, operators, analysts, service representatives, software builders, compliance assistants and eventually autonomous participants in how the business runs.

That means the CEO can’t treat agents as another technology rollout. When agents begin running pieces of the business, they are also reshaping the business.

This is why AI is now a CEO-level design decision. The CEO’s job is to make the decisions only the CEO can make: what the company is becoming, where intelligence should compound, what humans must continue to own, how value will be measured and who has authority to redesign the work across silos.

Before AI agents are deployed the business, CEOs must own five decisions to set a course toward AI business transformation vs. automating yesteryear’s digitization.

1. Decide what AI is for before you decide where AI goes.

2. Decide which workflows matter most, and which ones are quietly wasting the company’s capacity.

3. Decide where agents can act, where humans must approve and where autonomy is off limits.

4. Decide how value will be measured beyond productivity.

5. Decide who owns end-to-end flow across silos.

Please read the full article with actionable insights at Chief Executive.

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Every company is now asking some version of the same question…how do we use AI to become more efficient? It’s the right question. But it’s not the only question. If we use AI only to automate existing processes, reduce costs, and accelerate yesterday’s work, we may become faster versions of who we already were. That might improve productivity, but it won’t guarantee relevance. The bigger opportunity is to use AI to challenge the assumptions that define the business itself: what we make, how we work, who we serve, and what value we’re truly here to create.

At PhySec in Washington, D.C., I had the privilege of joining a rare kind of executive gathering: intimate, focused, off-the-record, and intentionally designed for the conversations that don’t usually happen in a ballroom or event center.

The room brought together leaders from across the global physical security industry. This isn’t about security though. This is for every leader. Why? Because right now, this is about leadership, innovation, and self-inflicted business disruption.

The companies that define themselves by what they make eventually get trapped by what they made. Companies that define themselves by what they stand for keep earning permission to evolve.

That was the heart of my message.

The future doesn’t reward better candles

One of the slides I shared quoted a line that captures the problem with most transformation efforts: “The electric light bulb did not come from the continuous improvement of candles.”

Most organizations are very good at improving candles. They optimize. They refine. They standardize. They create committees. They protect revenue streams. They make existing systems more efficient. They make yesterday perform better tomorrow.

There is nothing wrong with that. In fact, every successful business has to do it. The challenge is that continuous improvement, on its own, rarely produces the next curve.

That’s the innovator’s dilemma.

Not that I’m a fan of Facebook, but I appreciate the idea here

Established organizations often do the “right” things for their current customers, investors, operating model, and category. They serve today’s demand. They protect today’s margins. They listen to today’s market signals. They invest in what looks rational.

And then, slowly at first and then suddenly, the market moves.

Kodak did not miss digital photography because it lacked access to technology. Blockbuster did not miss streaming because nobody saw the signal. Walmart did not miss e-commerce because the emerging internet was a secret.

And yet, industry leaders historically have missed their moments.

The music industry did not invent iTunes. The publishing industry did not invent the next model for music. The television industry did not invent the modern streaming experience. Hospitality did not invent Airbnb. Taxi companies did not invent Uber.

These companies and industries were not unintelligent. They were optimized. And optimization has a gravity of its own.

The systems that make a company successful often become the same systems that prevent it from becoming something new.

That is why disruption happens in one of two ways.

It happens to you.

Or it happens because of you.

Iteration, innovation, and disruption are not the same thing

One of the most important distinctions leaders can make right now is the difference between iteration, innovation, and disruption.

Iteration is doing the same things better.

Innovation is doing new things that create new value.

Disruption is doing new things at scale that make the old things obsolete.

And right now, we’re making the same mistakes with AI.

Most AI strategies I see today are iteration strategies dressed up as transformation strategies. They ask: How can we go faster? Where can we take out cost? How can we do this less expensively or more efficiently? How can we upgrade our tools and processes with AI?

Those are important questions. They just aren’t enough.

They produce efficiency. They produce productivity. They may improve margins. They may reduce friction. But they do not necessarily create new value. And they certainly do not guarantee relevance in a market where intelligence is becoming abundant.

AI is already being applied to optimize yesterday’s work. It writes emails. Summarizes meetings. Speeds up research. Generates code. Automates tasks. Deflects calls. Drafts documents. Creates content. In physical security, it can enhance monitoring, detection, alerting, access control, compliance, reporting, and response.

Again, all valuable.

But if AI is used only to automate the past, it locks the organization into a finite future.

Someone else will collaborate with AI to build what wasn’t possible before. Someone else will build with AI to become infinite ∞.

That is where innovative AI begins.

Innovative AI asks different questions. Why do we assume this is the right path forward? Are we solving the right problems, or just improving old ones? How might we create new customer, employee, tenant, citizen, or community value? How can AI connect workflows, data, and systems to create business value? How can we reimagine stakeholder experiences? What new services, operating models, or revenue streams become possible when AI is no longer treated as a tool, but as a collaborator?

For physical security leaders, this is the moment to ask: Are we in the business of locks, cameras, credentials, guards, sensors, and systems?

Or are we in the business of trust, movement, safety, intelligence, and experience?

For leaders of any industry, this is the moment to ask: Are we in the business of “what got us here” or are we in the business of “what will get us to the next thing?”

Asking the question opens the door to permission. And permission creates the future.

Strategic imagination starts with identity

Companies don’t get disrupted because they lack technology. They get disrupted because they lack permission to become something new.

And permission starts with identity.

Remember why you started.

Ask, “what do I stand for?”

This is why #WDYSF (what do you stand for? is no longer a brand question. It is a strategic question.

When a company defines itself by its product, it inherits the limits of that product. When it defines itself by a higher purpose, it creates room to evolve as customers, markets, and technologies change.

That distinction is everywhere once you start looking for it.

What you stand for is the essence of who you are and what you do. WDYSF does not mean any one type of product. It’s the spirit of “why” you exist!

Nintendo began in 1889 as a playing card company. If Nintendo had defined itself as a playing card business, it would have remained trapped by the category it started in. Instead, the company kept evolving around something much bigger: play. That gave Nintendo permission to move from cards to toys to arcades to consoles to characters to worlds to experiences. It became one of the most important companies in gaming because it was never really limited to the format of yesterday’s game.

Michelin made tires. But if Michelin only saw itself as a tire company, it would have been limited by rubber, roads, and replacement cycles. Its broader purpose was helping people travel farther and better. That identity created permission to become one of the world’s most influential restaurant authorities. The Michelin Guide did not come from the hospitality industry. It came from a tire company that understood movement, travel, and experience.

Lego made plastic bricks. But Lego’s purpose was never just plastic bricks. It was inspiring and developing the builders of tomorrow. That unlocked movies, games, education, digital play, theme parks, and entire ecosystems of imagination. Lego did not stay in the business of bricks. It expanded into the business of building builders.

John Deere made tractors. But when the company defines itself around helping agriculture become more productive, precise, and intelligent, then camera vision, machine learning, robotics, and AI-enabled farming become natural extensions of its purpose. See & Spray represents agriculture becoming more intelligent.

Dyson made vacuums. Very good, very expensive vacuums. But Dyson’s true platform was never cleaning floors. It was airflow, engineering, and design applied to everyday problems. That identity gave the company permission to expand into hand dryers, fans, air purifiers, hair dryers, lighting, and beyond.

Tesla was perceived as an electric car company. But its ambition was always broader: sustainable energy systems. That gave Tesla permission to play across transportation, batteries, solar, autonomy, and energy storage. The car was not the whole story. It was an entry point into a larger system.

Amazon started as an online bookstore. But its deeper work was removing friction from commerce, infrastructure, logistics, and data. That identity created the path to marketplaces, fulfillment, delivery, cloud services, and an operating system for modern business. It did not stop at retail because its real work was never simply selling books.

Apple offers another powerful lesson. Apple was a computer company. Then it became a music company. Then a phone company. Then a services company. With Apple TV, Apple also stepped into the television experience, not by becoming a traditional broadcaster, but by reimagining how television could work as a device, platform, app ecosystem, content service, and experience layer. The television industry could have led that shift. Instead, a company outside the traditional category helped redefine the expectations around how people access, navigate, purchase, stream, and experience content.

This is what happens when companies understand their “why” at a deeper level.

The product is just the current expression.

The purpose is the platform for meaning and evolution in every stage.

So what is your industry’s equivalent?

If your company defines itself by its current product portfolio, services, business model, or operating structure, it will mostly iterate from there. It will optimize what exists. It will improve the familiar. It will use AI to make yesterday faster, cheaper, and more scalable.

That may be necessary. But it is not enough.

The bigger opportunity is to define the company around the value it creates, the problems it solves, the people it serves, and the future it has permission to shape.

If you define yourself by what you make, you inherit the limits of what you made. But if you define yourself by what you stand for, new possibilities open. A product becomes a platform. A process becomes an experience. A service becomes an ecosystem. A company becomes more than its current category.

That future is already here. And at at the same time, the future is emerging.

AI is transforming every business from something that records and reacts into something that senses, reasons, recommends, acts, learns, and adapts. Generative AI expands thinking and creation. Agentic AI coordinates workflows across people, platforms, policies, and systems. Physical AI brings intelligence into machines, environments, infrastructure, and the real world. Autonomous AI begins to execute bounded actions with human oversight, not always human instruction.

This is more than becoming a better version of today’s business. This is about creating a new operating layer for enterprise transformation and innovation.

The leaders who understand that will not simply use AI to improve the company they already are. They will use AI to become the company they were meant to become next.

AI Darwinism and the capability overhang

Before AI, I spent years studying what I called Digital Darwinism: the evolution of technology and society when they move faster than organizations can adapt.

Digital transformation was supposed to be the answer. But in reality, much of what passed for digital transformation was digitization. Companies moved to the cloud, adopted SaaS, modernized interfaces, and automated analog processes. They scaled yesterday’s operating model with newer tools.

Now we are seeing the same pattern with AI.

Automation. Productivity. Costs. Headcount Efficiency. All but evolution and reinvention.

That is why I call this next chapter AI Darwinism.

It is the evolution of markets when intelligence itself becomes abundant.

AI Darwinism works against you when you use AI only to automate the past.

AI Darwinism works for you when you use AI to evolve.

The gap is already visible at the individual level. OpenAI has described a “capability overhang,” where power users extract far more value from AI than typical users. In the deck, I referenced the widening gap between people who use AI casually and those who understand how to pull advanced reasoning, creativity, decision support, and problem-solving from these systems.

That gap exists in every company now.

Some people are becoming exponentially more capable. Others are using AI to make the same work faster. Some teams are reinventing workflows. Others are generating more AI slop that someone else has to rewrite, correct, verify, and clean up.

This creates an AI divide inside the organization before it ever shows up in the market.

And that divide becomes a leadership issue.

Microsoft’s Work Trend Index found that only one in four AI users say their leadership is clearly and consistently aligned on AI. That line stood out to me because it mirrors what I see over and over again. Most organizations do not have an AI vision. They have AI activity.

Activity is not strategy. AI is not strategy.

Just like a goal to “deflect 70% of calls” is not a strategy. It is a tactic. And if the only way to prove ROI is to cut heads, then we have chosen the smallest possible imagination for the technology.

A stronger question would be: How can we use AI to increase revenue, improve safety outcomes, reduce risk, create new services, increase customer trust, elevate employee capability, or redesign the workflow entirely?

Then we can ask: How must work flow differently to achieve that outcome?

That is where AI stops being a tool and becomes a catalyst.

The next era of AI still evolving

We tend to talk about AI as if it is a single technology. It isn’t.

Generative AI was the introduction. It taught the world that machines could generate language, images, software, ideas, summaries, and synthetic outputs at scale.

Agentic AI is the next step. It moves from generation to coordination and action. Agents can pursue goals, use tools, connect systems, manage workflows, escalate exceptions, and collaborate with humans and other agents.

Physical AI brings intelligence into machines, sensors, devices, buildings, environments, robotics, cameras, access systems, and infrastructure. For physical security, this is especially important. It means the physical world becomes computational, contextual, and responsive.

Autonomous AI is the frontier where systems begin to operate with less direct supervision, with humans increasingly on the loop rather than always in the loop.

Each wave raises the stakes.

If we deploy generative AI into old processes, we get faster old processes.

If we deploy agents into old workflows, we get automated old workflows.

If we deploy physical AI into old security models, we get smarter versions of yesterday’s systems.

The opportunity is to reimagine the workflow before we scale the technology…to become infinite.

In any industry, that means not just asking how AI can improve what already happens, but how it can change what is possible.

…not just how AI can detect problems, but how it can prevent them.

…not just how it can improve response times, but how it can coordinate action across people, systems, policies, partners, and platforms.

…not just how it can validate a transaction, approve a request, or complete a task, but how it can create trusted, adaptive, contextual experiences.

…not just how it can monitor performance or risk, but how it can inform operations, compliance, customer experience, employee experience, product development, service delivery, and business continuity.

This is the shift from using AI as a tool for optimization to using AI as a catalyst for reinvention.

The better question becomes, “What would this become if intelligence were embedded into the work from the beginning?”

AI scales in ways automation can’t.

It scales thinking.

It scales creation.

It scales decision velocity.

It scales problem-solving.

And agents scale capabilities.

That is a very different conversation than “how do we reduce manual work?”

The most important AI skills are profoundly human

One of the ironies of AI is that the more capable the technology becomes, the more important human qualities become.

Empathy.

Curiosity.

Creativity.

Judgment.

Critical thinking.

Courage.

I don’t love calling them “soft skills.” There is nothing soft about them. These are the skills that determine whether we ask better questions, challenge assumptions, imagine new possibilities, and recognize when the answer AI gives us is elegant but wrong.

As a digital anthropologist, one of the most important things I’m studying is how AI affects us as individuals. AI can make us faster. It can also make us dependent. It can augment our thinking. It can also quietly replace our thinking if we let it.

Many leaders are already offloading too much judgment to AI. They trust the machine’s confidence. They second-guess their own experience when AI disagrees. They start to mistake fluency for truth.

AI will tell you your prompt was brilliant. It will validate you. It will make you feel like the smartest person in the room.

The answer more than AI fluency. It is augmented intelligence. It is AIQ: the ability to combine human intelligence, emotional intelligence, strategic intelligence, and artificial intelligence to create outcomes neither humans nor AI could achieve alone.

AI + human is the real summit.

But that requires leaders to create cultures where people can think, challenge, experiment, and be wrong.

Sir Ken Robinson famously warned that we educate people out of their creative capacities. By adulthood, many people are afraid to be wrong. Then they enter organizations that stigmatize mistakes, reward conformity, and call predictable thinking “alignment.”

That culture cannot innovate.

At Google, one of the most important studies of high-performing teams found that psychological safety was the differentiator. The best teams felt safe asking questions, challenging ideas, and being wrong.

That is where innovation begins.

Most brainstorming sessions produce safe ideas because people bring the fear of being wrong into the room. Safe ideas produce linear outcomes. Linear outcomes produce iteration.

Innovation requires a different environment. It requires permission to challenge the status quo before the market does it for you.

Netflix and the courage to cannibalize success

Netflix is one of the clearest examples of a company willing to jump the curve.

I had the opportunity to spend time with Marc Randolph, co-founder of Netflix. One of the original ideas before DVDs-by-mail was hair care by mail. Shampoo, conditioner, treatments. Not exactly the Netflix we know today.

But what made Netflix remarkable wasn’t the first idea. It was its willingness to evolve.

Netflix cannibalized successful models to create the next one. DVDs by mail gave way to streaming. Streaming gave way to original content. Original content was informed by data. Each era required the company to challenge the very model that was working.

In 2000, Netflix famously tried to sell itself to Blockbuster for $50 million. Blockbuster laughed them out of the room.

The lesson isn’t just that Blockbuster missed streaming. The deeper lesson is that Blockbuster followed the curve while Netflix tried to jump it.

Once you are following someone else’s future, you are already behind. Unless you are an exceptional fast follower with extraordinary execution and category-creation strength, the market starts to move without you.

All leaders should take this seriously.

The future competitor may not look like today’s competitor. The company that reimagines security may not come from the security industry. It may come from enterprise software, AI infrastructure, insurance, real estate, facilities management, robotics, identity, logistics, or smart cities.

That is how disruption works. It usually arrives from the edge, not the center.

The unknown unknowns are where innovation lives

Strategy usually starts with what we know.

We are all products of our experience. Leaders become leaders because they have built expertise, pattern recognition, instincts, and judgment. The problem is that what we know can also become a cage.

Around what we know is what we know we don’t know. That is the realm of research, planning, benchmarking, and learning.

But outside of that is the most important space: what we don’t know we don’t know.

That is where innovation lives. This is where we can benefit from a mindshift.

It is uncomfortable because it isn’t immediately tangible. It doesn’t always fit into existing metrics. It may not satisfy current committees. It may look risky because it threatens today’s model. But often what we call “high risk” is really just “hard to change.”

Venture capitalists live in that outer realm. They are not looking for 5% improvement. They are looking for exponential outcomes. They have built systems to explore uncertainty, place bets, learn quickly, and scale what works.

Enterprises need their own version of that capability.

They need a disciplined way to explore the unknown.

Stay curious. Think big. Start small. Fail quickly. Scale fast.

That was one of the closing ideas in my presentation. Think of it as an operating model.

The questions physical security leaders should be asking now

The slides I didn’t fully get to in the room were designed to push this conversation into leadership practice.

What are we optimized to protect?

Revenue streams? Standards? Compliance models? Committee processes? Existing systems?

Where are we mistaking legacy for performance?

Old approval processes? Fragmented systems? Manual workarounds? “That’s just how we work”?

What are we underinvesting in because it threatens today’s model?

New customer journeys? AI-enabled services? Alternative delivery models? Capability uplift? Reimagined operating models?

Where are we calling something “high risk” when it is really “hard to change”?

These questions matter because the future of physical security will not be defined by AI alone. It will be defined by the leaders who decide what AI is allowed to change.

Some organizations will use AI to reduce cost.

Some will use AI to improve efficiency.

Some will use AI to make existing products smarter.

A smaller group will use AI to reimagine what security is, what it enables, and how it creates value across the enterprise.

That smaller group will shape the next category.

The Chief Workflow Officer and the end of siloed transformation

After the talk, one of the conversations turned to change management, which is always the hard part. The technology is often easier to understand than the organizational psychology required to make it matter.

This is why ServiceNow Chief Innovation Officer Dave Wright and I have been exploring the idea of a Chief Workflow Officer (you can learn more in Infinite).

We believe it’s a missing leadership function in AI business reinvention.

AI ROI does not come from sprinkling intelligence onto isolated tasks. It comes from reimagining workflows end to end. That requires someone to start with the business outcome, map how work actually flows across silos, connect systems and data, redesign roles, and define where humans, AI, agents, and governance belong.

Most workflows are treated vertically because organizations are structured vertically. But value usually flows horizontally.

A security incident is more than just a security incident. It may touch facilities, HR, legal, compliance, operations, IT, customer experience, emergency response, insurance, communications, and leadership. A credentialing workflow isn’t just access control. It is identity, trust, policy, employee experience, visitor management, risk, and data.

AI reveals how fragmented work really is.

That is why the first step is ask, “what outcome are we trying to create, and how must work flow differently to achieve it?”

The next chapter belongs to the dilemma’s innovator

The title of my keynote was “The Dilemma’s Innovator.”

It is a play on the innovator’s dilemma, but it is also a call to action.

I called the keynote “The Dilemma’s Innovator” because every leader today is living inside Christensen’s innovator’s dilemma, but AI has raised the stakes and accelerated the timeline.

Christensen taught us that great companies don’t usually fail because they do the wrong things. They fail because they keep doing the right things for the business they already understand. They protect today’s customers, margins, products, processes, and performance metrics until tomorrow’s market is built by someone else.

AI makes that dilemma unavoidable.

The question is no longer whether leaders should optimize the current business. They must. The question is whether they can also become the innovator inside the dilemma. Can they use AI not just to improve what exists, but to imagine what should exist next? Can they automate the past while also building the future? Can they disrupt themselves before someone else does?

That is the dilemma’s innovator: the leader willing to protect what works, challenge what limits growth, and use AI to create new value before the market forces the issue.

The dilemma’s innovator is the leader who can hold both truths at once.

Improve what deserves to be improved.

Reimagine what must be reimagined.

Use AI to drive efficiency, but reinvest those gains into augmentation and innovation.

Protect the core, but do not become trapped by it.

Create space for teams to explore what could put the current model out of business.

Build the next curve before someone else does.

Every business, every industry, has an extraordinary opportunity ahead. Not just to become more efficient. Not just to become more intelligent. But to become more essential to the enterprise than ever before.

Will we automate the past or build what wasn’t possible before?

The delta between linear growth and exponential growth is disruption. And disruption will either happen to us, or because of us.

You, we, are the dilemma’s innovator now.

Infinite ∞ | Mindshift | Subscribe | Keynote Speaker

The post The AI Dilemma’s Innovator: Why AI Makes You Faster While Still Falling Behind appeared first on Brian Solis.

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.

Infinite ∞ | Mindshift | Subscribe | Keynote Speaker | LinkedIn

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

The post The Web Has a New User: AI appeared first on Brian Solis.

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

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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

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

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

It isn’t.

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

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

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

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

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

AI Is Being Applied to Yesterday’s Business

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

AI could easily follow the same path.

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

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

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

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

Transformation Requires a Mindshift

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

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

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

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

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

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

An AI-First Mindset Changes the Questions

Leaders need an AI mindset.

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

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

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

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

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

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

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

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

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

Every Company Needs Two AI Growth Curves

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

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

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

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

Organizations need both curves.

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

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

From Automation to Augmentation to Agents

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

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

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

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

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

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

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

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

Innovation Demands That Leaders Disrupt Themselves

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

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

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

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

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

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

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

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

The Future Is Still a Leadership Decision

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

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

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

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

After watching, bring this question to your leadership team:

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

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

Infinite ∞ | Mindshift | Subscribe | Keynote Speaker

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

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

But they are not entirely wrong.

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

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

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

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

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

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

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

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

These are understandable questions. They are also incomplete questions.

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

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

That’s proper invitation.

Not iteration, but reinvention.

Not just productivity, but possibility.

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

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

AI Is Exposing Leadership, not Just Technology

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

It is exposing how leadership thinks.

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

AI magnifies intent.

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

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

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

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

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

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

Most Companies are Using AI to Improve the Past

This is the trap, and it’s common.

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

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

Leaders will ask bigger questions.

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

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

High Performers Use AI Differently

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

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

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

There are two very different futures emerging right now.

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

In the other, people use AI to expand capability.

One path leads to dependency.

The other leads to evolution.

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

Competing Against AI is the Wrong Mindset

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

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

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

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

When you compete against AI, you protect.

When you compete with AI, you invent.

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

When you compete with AI, you focus on augmentation.

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

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

It changes strategy, which changes leadership.

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

Five Questions Every Leadership Team Should Ask Now

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

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

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

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

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

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

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

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

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

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

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

This Is a Leadership Moment, not Just a Technology Moment

Yes, AI is a technology revolution.

But for business leaders, it is something more consequential.

It is a leadership moment.

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

This moment calls for imagination. It calls for conviction.

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

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

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

Dream bigger.

Think bigger.

Lead bigger.

Watch the ISE Keynote

Infinite ∞ | Mindshift | Subscribe | Keynote Speaker

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

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

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

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

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

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

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

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

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

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

So, let’s dive into it!

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

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

So, let’s dive into it!

The Four AI Cultures Defining the Future at Different Speeds

Something is happening beneath the surface of every AI strategy.

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

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

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

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

AI Darwinism Starts With Culture

This is where AI Darwinism begins.

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

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

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

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

Infinite ∞ | Mindshift | Subscribe | Keynote Speaker

The post The Four AI Cultures Defining the Future of Business at Different Speeds and Scale appeared first on Brian Solis.