One of the best things about writing a book is seeing someone you respect take an idea somewhere unexpected.

Dave Wright and I recently joined DisrupTV with R “Ray” Wang and Vala Afshar to talk about our new book Infinite ∞. Before kicking off that conversation, my longtime friend John Nosta joined in ahead of his interview.

John and I have shared keynote stages around the world, and he has a gift for pushing technology conversations into deeper questions about humanity, cognition, and meaning.

After our conversation, John published a fascinating piece in Psychology Today exploring what happens when AI makes intelligence, productivity, and output increasingly abundant, i.e. Infinite.

He asked, what happens when the old ways we measure advantage stop telling us what actually matters?

If AI allows dramatically more output with dramatically less human effort, then familiar signals such as headcount, scale, productivity, and even access to intelligence may become weaker proxies for value.

One of John’s lines that stood out was this, “The ruler hasn’t broken. We may simply be measuring the wrong thing.”

That idea connects directly to what Dave and I explore in Infinite: as AI changes the economics of work and organizations, leaders will need new ways to define differentiation, value, and advantage.

John takes that thinking in a direction I think every leader should explore.

Read his piece, “When Infinity Breaks the Scale.” And if the question resonates, Infinite goes deeper into what it means to build a company for a world where intelligence is no longer scarce.

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This is an updated version originally published in my Forbes column

What if the most important thing about AI is not what it can do today, but what becomes possible because it exists?

That was the question I kept coming back to after hearing Blackstone’s Jon Gray compare this moment to America in 1870. At the time, much of the country still ran on farms, horses, wood, and candlelight. Thirty years later, by 1900, steel, railroads, electricity, cities, and entirely new forms of industry had reshaped how people lived and worked.

The lesson isn’t really about whether or not AI means history will repeat itself neatly. It’s that transformative technologies rarely reveal their full impact at the beginning. We tend to judge them by how well they improve the world we already know, when the greater opportunity is to imagine the world they make possible.

That is the mindshift leaders need now.

AI is beginning to make intelligence more abundant, accessible, and increasingly autonomous. Yet most organizations were designed for an era when expertise, information, and decision-making capacity were scarce. The challenge is not simply to add AI to that system. It is to rethink the system itself.

The leaders who understand this moment will move beyond automation and efficiency. They will ask what new forms of work, value creation, customer experience, and organizational design become possible when intelligence can be distributed throughout the enterprise.

If 1870 teaches us anything, it is that the future is not created by replacing the old tool with a better one. It is created by reimagining what we build once the new capability exists.

The Companies Built for Scarcity Will Have to Reinvent Themselves

The entirety of organizational design and work was built upon a foundation of scarcity. Yet, much of today’s AI conversation is still focused on the technology immediately in front of us, model performance, adoption curves, capital expenditures, valuations and whether the current investment cycle constitutes a bubble. At the same time, we have doomers who have gne so far as to say that AI could wipe out humanity in 10 year’s time and generations of students and workers who worry about AI taking jobs, including graduates seeking their footing on lower rungs of the career ladder.

The first question that comes to my mind is whether organizations are preparing for what happens when intelligence becomes an increasingly abundant and inexpensive operating resource.

The Returns Are Becoming Harder to Ignore

Blackstone has an unusual vantage point into that question. Across roughly 1,400 portfolio companies, borrowers and related businesses, annualized spending with Anthropic increased from approximately $25 million to $525 million in a single year. That’s 21x! Gray also highlighted specific examples where AI investments are already generating measurable returns. Phoenix Tower invested $4 million in an AI-enabled process that accelerated lease processing fivefold and now produces approximately $4.5 million in annual value. Enverus, another portfolio company, is reportedly generating an 18-times return on its spending with model providers for software engineering use cases.

These examples help answer what Gray jokingly framed through the old Wendy’s question, “Where’s the beef?” IYKNY. But there is evidence that real value is emerging beneath the enormous infrastructure buildout. Yet for executives, that should be the beginning of the conversation rather than the conclusion.

The Optimization Trap

The temptation will be to interpret these returns as a mandate to apply AI more aggressively to existing processes. That will certainly produce gains. Companies can automate tasks, compress cycle times, lower costs and increase the productivity of individual employees. But if that becomes the primary ambition, we risk using one of the most consequential technologies of our lifetime to optimize organizations designed for another era.

Here’s where the 1870 analogy becomes much more tangible and useful.

Electricity did not transform business because companies discovered a more efficient replacement for candles. Its real impact emerged as leaders, inventors and entrepreneurs redesigned factories, cities, products, infrastructure and daily life around capabilities that had not previously existed at scale. The breakthrough started with electrification. But it’s growth and value creation was shaped by reimagining how we worked with and because of electricity. Like with AI, electricity powered a world we knew, but also unlocked new possibilities, outcomes, and value creation not possible before. That’s innovation.

As the old saying goes, the lightbulb was not the result of the continuous improvement of candles.

The next chapter of invention isn’t going to happen because any one voice or company decides that efficiency and cost-cutting is the way forward. You can’t cut your way to growth. You can’t automate your way to innovation. This “1870” moment will be defined by bold leaders and visionaries who dream, who invent, who build the scaffolding, the infrastructure, and scale, for the betterment of humanity, not just the elite.

So, who are our Bessemer’s, Carnegie’s, Edison’s, Tesla’s, Westinghouse’s, Hill’s, Bell’s, Rockefeller’s, Otis’, Mergenthaler’s, Eastman’s, and everyone else I’m missing, of this era? It can’t just be the Frontier leaders. This moment demands a new breed of leaders.

What Happens When Intelligence Is No Longer Scarce?

Getty: With AI, is intelligence is becoming abundant?

AI presents leaders with a similar challenge. The strategic question usually starts with “Where can we add AI?” For the curious, the dreamers, and builders, the question is also now, “What would we design differently if intelligence were available everywhere across the organization?”

That’s the mindshift from what Dave Wright and I detail in our latest book, the shift from a “finite” company to an “Infinite Company.”

Most companies remain finite by design. Their structures, processes, management systems and measures of performance were built for an environment in which human expertise was scarce, information moved slowly and organizational coordination required layers of hierarchy. Even when those companies adopt advanced technology, they often use it to reinforce the operating model they already know.

An Infinite Company begins from a different premise. It assumes that intelligence, increasingly expressed through people, models and autonomous agents, can become distributed throughout the organization. The leadership challenge is therefore not simply to automate existing work but to continually redesign how work is performed, how decisions are made and how value reaches the customer.

Disruption Happens to You or Because of You

Gray offered a useful historical parallel through retail. Amazon subjected companies such as Kmart, Sears, Toys “R” Us, Walmart, Costco and TJ Maxx to the same broad technological disruption. The outcomes were anything but uniform, but experts did warn incumbent leaders. Ultimately, some of these companies disappeared while others adapted and strengthened their relevance. Gray expects a similar divergence across white-collar industries, particularly as companies move from business models built around selling seats, labor or access toward models increasingly defined by outcomes.

That shift deserves far more attention from CEOs and boards.

AI Is Changing What Customers Pay For

Getty: The way forward is yours to define

If AI agents can perform meaningful portions of knowledge work, then the economics of many businesses begin to change. And that’s the point. Customers may become less willing to pay for hours worked, software seats consumed, or complexity managed. They will increasingly expect results. That forces leaders to reconsider not only the technology stack, but also pricing, organizational structure, workflows, talent models and the role humans play in creating differentiated value.

There is also an important constraint embedded in Gray’s argument. Intelligence may become abundant, but the physical world remains stubbornly finite. Blackstone sees bottlenecks in chips, power generation, data centers, permitting, capital and community acceptance. Turbines ordered today may not arrive until 2030 or 2031, while the capital required to support a gigawatt-scale AI infrastructure project can reach tens of billions of dollars.

The same dynamic exists inside companies. Often, the limiting factor is not the capability of the technology but the capacity of the organization to change around it. Legacy mindsets, workflows, fragmented data, outdated incentives, functional silos, and deeply embedded assumptions and beliefs can become the organizational equivalent of constrained power grids.

The Question Every Leadership Team Should Be Asking

The ever-popular question, “Is AI a bubble?”, is honestly too small for leadership teams.

There will undoubtedly be failed investments, inflated valuations, and companies caught on the wrong side of this cycle. Gray himself highlighted cybersecurity, geopolitical risk, excessive valuations, and supply constraints as meaningful concerns. The industrial revolutions of the past produced enormous value alongside bankruptcies, dislocations, and periods of irrational exuberance.

But leaders do not have to predict the exact shape of the next 30 years to prepare for it. They do have to decide whether they are using AI to make the current company incrementally better or using this moment to rethink what the company could become.

If you think about it, the most useful question for the next executive meeting be the simplest, If intelligence becomes as ordinary and accessible as electricity eventually became, what could we build differently?

The organizations willing to answer that question seriously will go beyond AI adoption and fluency. They will begin designing the future organization, future value creation, and future operating models today.

Infinite ∞ | Mindshift | Subscribe | Keynote Speaker

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Bernard Marr is a friend, someone I’ve known for many, many years. He’s also a best-selling business author and strategic advisor to companies and governments. I wish I had known he was at the World Summit AI event in Amsterdam. I would have loved to spend time with him. At the same time, I was relieved not to know he was in the audience. That would. have increased the pressure to deliver!

Following the event, Bernard shared his thoughts about what he heard here. Selfishly, I’m proud to share his takeaways and ideas from the session I delivered.

In Bernard’s words…

Businesses Need To Stop Automating Yesterday’s Processes

One of my favorite presentations came from Brian Solis, Head of Global Innovation at ServiceNow.

He challenged businesses to think beyond what he called a finite mindset, where AI investments focus primarily on efficiency, cost reduction and doing existing tasks faster.

Instead, he encouraged an infinite mindset, where organizations rethink their operating models and explore entirely new opportunities.

I couldn’t agree more.

Consider a company that uses AI to reduce the time spent creating marketing campaigns but leaves its approval, distribution and decision-making processes untouched. It might save hours creating content, only to create another bottleneck further down the workflow.

Solis argued that businesses need to redesign end-to-end workflows with AI at their core.

This is something I regularly emphasize when working with organizations. The biggest opportunities come from asking how we would design our businesses if we were starting from scratch today.

Otherwise, we risk spending billions on sophisticated technology to make outdated processes run slightly faster.

—

Thank you, Bernard.

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Two years ago, The Economist asked what happened to the AI revolution. Today, we’re investing more than ever, still struggling to prove the returns, and discovering that AI’s impact on jobs is anything but predictable. I believe these seemingly contradictory signals are telling us something important about the AI Economy taking shape, and what it will take to lead in it.

Investment continues to accelerate, the world’s largest technology companies are building an unprecedented infrastructure for intelligence, and AI agents are beginning to participate in the everyday operations of businesses. Yet the financial and workforce data tell a much more complicated story.

Some companies are reporting significant productivity gains and expanding their workforces, while others are struggling to demonstrate meaningful returns or are restructuring around the promise of automation. Meanwhile, concerns about an AI investment bubble are growing alongside the extraordinary amounts of capital being committed to its future. How can so much progress and investment produce such different outcomes, and what should business leaders make of it all?

As I connect these developments with the broader shifts I’ve been studying in our work at ServiceNow Futures, I believe we’re seeing the early formation of something much bigger, at least, formal, the constructs of a measurable AI Economy.  It’s the economic value that becomes possible when businesses learn to apply intelligence in ways that fundamentally change how they operate, how people work, how customers are served, and what organizations are capable of creating. These two dimensions of the AI Economy are deeply connected, yet they are developing at very different speeds.

We’re investing aggressively in making intelligence available while still learning how to translate it into sustained economic value.

This helps explain much of the confusion surrounding AI’s performance. We’re approaching artificial intelligence with expectations shaped by traditional technology investments, where deploying new systems and increasing adoption are expected to generate predictable improvements in productivity and efficiency. But AI introduces capabilities that challenge the assumptions behind the way businesses are designed and managed.

The First Contradiction: AI Investment Is Associated with More Jobs, Not Fewer

One of the more surprising findings emerged from research by Ramp and Revelio Labs. Studying nearly 22,000 U.S. companies, the researchers found that businesses investing most intensively in generative AI experienced a 10.2% increase in white-collar headcount over the first two years following adoption. Even more interesting, entry-level employment increased by 12%. Companies investing at lower levels, however, experienced no statistically significant difference in headcount compared with their control group.)

This challenges one of the most persistent assumptions about AI’s economic future. It’s the idea that greater investment in artificial intelligence will necessarily translate into fewer employees just because. Yet the more interesting finding, in my view, is the relationship between adoption intensity and organizational growth. The researchers observed that headcount gains appeared primarily among high-intensity adopters and generally took six to twelve months to emerge. The findings are correlational rather than proof that AI caused the hiring growth, and there are important limitations. The companies experiencing the greatest gains were disproportionately smaller, technology-oriented, and venture-backed, and nearly all the headcount gains occurred in the technology sector.

IMHO, the study opens a more useful conversation about the relationship between intelligence, productivity, capacity, and growth.

For decades, businesses have largely approached productivity through an efficiency lens. We sought to reduce the cost of producing an output, eliminate unnecessary work, standardize processes, and increase revenue per employee. AI expands those possibilities, but it also introduces another dimension that traditional productivity thinking often overlooks: what happens when greater capability enables the organization to pursue opportunities that were previously beyond its capacity?

Imagine an organization in which AI significantly reduces the time required to analyze customer needs, design products, evaluate market opportunities, resolve complex service issues, or coordinate work across functions. One leadership team might convert those gains primarily into lower labor costs. Another might use the same capacity to serve new customers, create more sophisticated products, expand into adjacent markets, or deliver experiences that were economically impractical before. Both could improve efficiency, but only the second is deliberately translating that efficiency into new growth.

In our book Infinite: How Visionary Leaders Transform Today’s Businesses into AI-Forward Companies, Dave Wright and I often describe this distinction as optimized AI versus innovative AI. Optimized AI uses new technology to improve the performance of an existing business. Innovative AI challenges the assumptions upon which that business was built and explores entirely new outcomes, experiences, and sources of value. Both have a role, but they are not interchangeable strategies, and they will not produce the same economic future.

There is also evidence that AI’s influence on employment is far more complicated than the growth statistics alone suggest. An August 2026 revision of Stanford’s research on early-career employment found that employment among workers ages 22-to-25 in AI-exposed occupations was 19% below where it would have been had it followed the trajectory of less-exposed peers. The same research found no evidence of widespread economy-wide job displacement. Meanwhile, Yale’s Budget Lab, reported that the broader U.S. labor market still does not show a clear employment or unemployment pattern attributable to AI.

These findings aren’t necessarily incompatible. They examine different populations, time frames, and economic effects. Some businesses may be expanding because of broader growth dynamics associated with early technology adoption while particular occupations, career stages, and tasks experience genuine displacement pressures. Neither represent a sweeping declaration about the future of work. But they are worth thinking about.

What I’m particularly concerned about is this…what happens to the development of human expertise when organizations automate the work that traditionally prepared employees for more advanced responsibilities?

Entry-level jobs often provide the foundational experiences through which people develop judgment, context, interpersonal skills, and an understanding of how businesses operate. If we automate those experiences without deliberately redesigning how people learn, we risk weakening the very workforce that will eventually be responsible for supervising and leading increasingly intelligent systems.

The question for executives should now extend beyond whether AI will increase or decrease headcount. It’s a choice. As such, it should strategically consider what new forms of value the organization intends to create, what combinations of human and machine intelligence those outcomes require, and how the company will invest in developing the capabilities of its people as work changes.

The Second Contradiction: AI Spending Is Exploding While ROI Remains Elusive

If the employment research points toward a potentially more expansive economic future, the financial research provides a necessary reality check.

In June, Bain & Company published findings from its Automation and AI Pathfinder Survey, covering 951 global companies. Nearly 40% of organizations measuring AI-related cost savings reported reductions below 10%, despite many targeting savings of 11% to 20%. Yet 90% of those companies planned to increase their AI budgets. Perhaps the most concerning finding was that 44% of organizations expected to fund their next wave of generative and agentic AI investment using savings from earlier automation programs, even though those earlier programs had frequently fallen short of their intended returns.

Bain characterized this as a circular bet, and I think that description deserves executive attention. Organizations are increasingly using assumptions about previous productivity improvements to justify new technology spending, without necessarily demonstrating that the original financial benefits were fully realized. They may be compounding investment, but they could also be compounding unverified expectations.

The circular AI economy is powered by AI investments that fuel more AI spending without necessarily generating measurable economic returns. Companies reinvest projected automation savings that haven’t fully materialized, while AI providers, infrastructure companies, and investors circulate capital through interconnected investments and purchasing agreements. This creates the appearance of accelerating demand and growth, but raises a need to answer how much genuine new economic value is being created, versus how much money is simply circulating within the AI ecosystem.

The challenge becomes more pronounced with agentic AI. Bain found that only 7% of companies were operating fully autonomous agents in production, while many others required human approval or maintained guardrails and exception-handling processes. Those controls are often essential, particularly when AI systems make consequential decisions or interact with sensitive business processes. But they also expose the distance between the economics of a fully autonomous system and the actual cost of operating a responsible human-agent workflow.

A business case built around eliminating most human intervention will not deliver its projected returns if employees must continually review, correct, escalate, or repeat work. Those activities represent real costs that cannot be ignored simply because the technology is capable of generating an output.

BCG’s research adds another perspective to the same problem. Its 2026 AI Radar found that companies expected to double their AI investment from approximately 0.8% to 1.7% of revenue. Seventy-two percent of CEOs identified themselves as their organizations’ primary AI decision-maker, while 94% of surveyed organizations planned to continue investing even if AI failed to deliver immediate returns. Half of CEOs believed their job stability depended on getting AI strategy right.

Those findings suggest that AI has moved beyond technology planning and into the center of corporate strategy, executive accountability, and capital allocation. But confidence and conviction are not the same as realized value. In fact, the faster expectations rise, the greater the pressure on executive teams to demonstrate that their investments are translating into meaningful business performance.

BCG’s earlier research on the widening AI value gap may provide the clearest indication of what is happening. Only 5% of companies were classified as future-built organizations capable of achieving AI value at scale. Another 35% were beginning to generate value as they scaled AI initiatives, while 60% reported little material value despite substantial investment. The future-built companies reported five times the revenue increases and three times the cost reductions associated with AI compared with their peers.

That is a remarkable concentration of value, and it supports an important distinction. The dividing line between AI leaders and laggards is not simply whether they have access to capable technology. It is their ability to translate that technology into organizational capabilities, redesigned workflows, better decisions, and business models that can create and capture economic value.

More recently, McKinsey reported that while 40% of respondents at large companies said their organizations were scaling AI agents, only 6% reported AI contributing at least 5% of EBIT. That gap illustrates how adoption and financial impact can follow very different trajectories, even as AI becomes increasingly visible across the enterprise.

Taken together, these studies suggest that we are experiencing an asymmetry between the accelerating supply of intelligence and the organizational ability to convert that intelligence into results. Technological capability is expanding rapidly, while management systems, operating models, financial measures, workforce structures, and organizational cultures often evolve incrementally.

This is why I believe the AI Economy is emerging unevenly. We have become increasingly capable of producing and consuming intelligence, but we have not yet become equally capable of organizing business around it.

The Skeptics Are Asking Questions Executives Need to Answer

Not everyone believes this gap is temporary, and some of the strongest critics argue that the economics of generative AI may be fundamentally unsustainable.

In his thoughtful essay, “AI Doesn’t Have ROI,” Ed Zitron challenges the industry’s assumptions about economic value, questioning whether the true costs of producing AI outputs have been adequately represented. His argument is that token-based pricing, subsidized subscriptions, unpredictable computational requirements, repeated attempts, and the human effort needed to correct unreliable outputs can obscure what AI actually costs to operate. He goes further, arguing that the broader AI investment boom may represent an extraordinary misallocation of capital rather than an emerging economic revolution.

I don’t share his categorical conclusion that AI has no economic value, but I think his underlying questions are important, especially for executives responsible for allocating capital and delivering returns. If an AI system requires multiple attempts to produce a dependable result, if employees spend significant time reviewing or correcting its work, or if the cost of running the system increases faster than the value it creates, those factors must be accounted for in the investment case.

There is a difference between the potential value of artificial intelligence and the realized economics of operating it. There is also a difference between the widespread idea of applied value and the potential of unexplored value creation.

The existence of impressive demonstrations, rapidly growing usage, or enthusiastic employee testimonials does not eliminate the need for financial discipline. Equally, the absence of broad economy-wide productivity gains doesn’t mean that valuable applications are nonexistent or that longer-term transformation is impossible. Both the optimism and the skepticism need to be tested against outcomes.

This is where I come back to The Economist’s question from 2024. The article wasn’t necessarily arguing that AI would never produce economic value. It was challenging the assumption that extraordinary technological progress should automatically translate into widespread productivity improvements. History offers plenty of examples in which the invention of transformative technology preceded its broader economic impact by years or decades, largely because organizations and institutions had to change before the technology could reach its potential.

Electricity, for example, didn’t immediately transform industrial productivity simply because factories could replace steam-powered machinery with electric motors. The larger gains came when factories were redesigned to take advantage of distributed power, new production arrangements, and more flexible workflows. Applying new power to an old industrial design could only deliver part of the opportunity (separately, please see why this is our 1870 moment).

Breaking Free from the Iteration Trap

I see a similar challenge unfolding with artificial intelligence. Organizations are adding AI to processes, applications, and hierarchies that were designed before machine intelligence became available. They are teaching employees to use new tools without changing what those employees.

This is why I’ve been advocating for work charts alongside org charts. A work chart begins with a desired outcome, such as resolving a customer issue, bringing a product to market, settling an insurance claim, or moving a prospect from initial interest to long-term loyalty. It then examines the flow of work required to deliver that outcome, identifying where humans provide judgment, expertise, empathy, and accountability; where AI agents can gather information, coordinate activities, analyze possibilities, or take bounded action; and where data, systems, and governance must come together to enable the entire process.

The status quo is to insert agents into isolated tasks. The opportunity is to redesign end-to-end workflows so that human intelligence and machine intelligence complement each other in ways that improve the outcome itself.

For example, a customer service operation might deploy AI to deflect or summarize conversations and generate suggested responses, improving the efficiency of individual representatives. That is useful, but a reinvention-oriented team would examine the entire customer journey (end-to-end).

Why did the issue arise? Could the organization anticipate and prevent it?

Could agents coordinate across systems to resolve underlying causes?

How could employees use the information and time made available by AI to deliver more personal, valuable, and empathetic experiences?

What signals from those interactions could continuously improve products and services?

Rethinking the Relational Promise to Customers

The second approach goes beyond making customer service more efficient. It explores the possibility of changing the relationship between the company and its customers, including reducing the need for service interventions in the first place.

This is also where business reinvention becomes an organizational leadership responsibility rather than a technology implementation program. The CEO and executive team need to establish the outcomes that matter, while finance, technology, operations, HR, legal, and risk functions collaborate to create an operating model capable of delivering them. Ownership must extend across workflows, because meaningful customer and business outcomes rarely exist neatly within a single department.

For organizations trying to move from ambition to action, I would begin with one important customer facing end-to-end workflow and give a cross-functional leadership team a clear mandate to redesign it. Establish the baseline economics of the current process, including cycle time, cost, error rates, customer outcomes, and the human effort required. Map the work as it happens today, including its handoffs, delays, decisions, and exceptions, before designing how humans and AI agents should collaborate in the future.

Then establish what the new workflow should make possible with AI at the core. Some of the returns may come through reduced cost and improved throughput, while others may emerge through better decisions, faster response times, greater customer satisfaction, new revenue, or expanded capacity. Those outcomes need specific owners, measures, and financial assumptions that can be tested rather than simply projected. A focused 90-day effort can establish whether the redesigned flow is delivering real improvements and what must change before it is scaled.

Importantly, I would also require the team to explain how employees will grow as the workflow changes. Which responsibilities will become more valuable? What expertise must be developed? How will early-career workers gain experience? How will the organization help people transition from performing repetitive tasks to supervising agents, interpreting information, solving more complex problems, and exercising judgment?

These are not secondary change-management considerations. They are integral to the economics of an intelligent enterprise because the sustainable value of AI depends on the capabilities of the people and systems surrounding it.

I believe executives should treat the capacity AI releases as a strategic investment resource, not simply an efficiency dividend. That capacity can be directed toward better experiences, innovation, workforce development, and business opportunities that conventional operating models may have discouraged or rendered uneconomical. In Infinite, Dave Wright and I refer this at the Bi-Modal Approach to strategic AI investments.

This is how optimized AI becomes a foundation for innovative AI, rather than the destination.

The Real AI Economy Will Be Built by Leaders Who Reinvent the Business

The AI Economy will ultimately be shaped by what businesses, institutions, entrepreneurs, and people accomplish with it. The economic value of intelligence will be determined by the new capabilities, improved outcomes, and economic opportunities that emerge when intelligence changes how work is designed and how organizations operate.

That also means we must resist the temptation to measure the future solely by the degree to which AI replaces existing labor. Some work will be automated, some roles will be profoundly restructured, and some jobs may disappear. But the greater leadership opportunity is to imagine what people can achieve when intelligence augments their capabilities and when organizations create the conditions for those capabilities to translate into meaningful work. That also requires leaders to articulate the future of work and translate that vision into job descriptions.

A future in which companies become more efficient but people become less capable, less connected, or less able to develop professionally would represent a failure of imagination as much as a failure of leadership.

I remain an AI optimist. I believe in our ability to shape technology toward a better future, and I believe the greatest opportunities emerge when leaders recognize that the future is something they have a responsibility to design rather than simply predict.

This is the distinction Dave Wright and I explore in Infinite ∞. Finite companies approach disruptive technology primarily through the lens of what they already know. They seek to extend the performance of existing business models, operating structures, and established measures of success. Infinite ∞ companies recognize that new capabilities can change the very possibilities around which a business is organized. They are willing to challenge convention, rethink how value is created, and continuously evolve what the organization can become.

We are entering a period in which that distinction will become increasingly consequential. As intelligence becomes more abundant, the scarcity that matters may shift toward imagination, organizational adaptability, trust, human judgment, and the leadership required to turn possibility into progress. Companies that understand this will compete through fundamentally different capabilities, experiences, operating models, and rates of learning.

Two years ago, The Economist asked what had happened to the artificial-intelligence revolution. Today, I would reframe the question for every executive team and boardroom, what if the revolution we’ve been waiting to measure is about us/you transforming the very nature of business?

That is the AI Economy I see emerging. It is also the future I believe we have an opportunity, and a responsibility, to lead.

Infinite ∞ | Mindshift | Subscribe | Keynote Speaker

The post The AI Economy Is Here, But We’re Measuring the Wrong Revolution appeared first on Brian Solis.

Executives observing digital-, mobile-, and AI-first customers (created with Google Gemini)

I’m preparing a new presentation for a delegation of European CEOs visiting Silicon Valley to explore the future of AI, business, and leadership. As part of my research, I’ve been revisiting some of my presentations and predictions from 2016–2018 to see what we got right, what we missed, and perhaps more importantly, what lessons from the past can help us make better decisions about the future.

It’s been a fascinating exercise in digital anthropology, especially revisiting my work on customers as “accidental narcissists” and “digital introverts, and the ways technology was quietly rewiring human behavior, relationships, expectations, and experiences. These were early signals of changes that would eventually reshape how people engage with one another, brands, and now intelligent machines.

I found this illustration and I was reminded of how important this message is today. I’m also proud of how well this aged!

On the internet, nobody knows you’re a robot.

In 2018, for a series of talks on reimagining customer experience (CX), I worked with Gapingvoid Culture Design Group to visualize a simple but important reality: No one wakes up hoping that’s the day they get to talk to a customer service AI agent or chatbot.

It was a play on the famous 1993 New Yorker cartoon, “On the Internet, Nobody Knows You’re a Dog.” At the time, that cartoon captured the anonymity of the early internet.

Our version offered a different perspective on the emerging age of automation, where companies were becoming enamored with the technology they could deploy, sometimes at the expense of understanding the people they were supposed to serve.

My presentations challenged executives responsible for customer experience, service, and digital transformation to approach emerging technologies through the lens of human-centered design. I was exploring early concepts such as “UX as AI,” “AI as UI,” and what I called CX 2.0 at the time, combining intelligence (IQ), emotional intelligence (EQ), and human understanding (HQ) to create experiences that actually mattered to people.

You can revisit some of that thinking in my 2018 article on AI, chatbots, and next-generation customer engagement.

Fast-forward to today, and I believe these lessons are becoming even more consequential.
We’re moving beyond conversational AI into agentic AI and agentic commerce, where intelligent agents will increasingly discover brands, evaluate products, make recommendations, and complete transactions on behalf of people. The relationship between businesses and customers is being fundamentally reimagined, including who or what represents the customer, how decisions are made, and where trust is established.

For CEOs and executive teams, this raises an entirely new set of questions…

• How do you build a trusted brand when an AI agent becomes the customer’s primary interface?
• How do you design meaningful experiences when algorithms increasingly mediate discovery, consideration, and commerce?
• How do you preserve the human connection that inspires loyalty when more interactions become automated or invisible?

The answers to these questions shape the future of business, relationships, differentiation, and value creation. They also shape your brand, service and experience design and innovation,

And we’re already seeing signs that businesses are losing sight of the human beings behind the technology. According to WordPress VIP’s Future of the Web 2026 report, 74% of consumers say the internet feels less human than it did a decade ago. Further more, 61% can’t name a brand using AI well in its messaging. And 60% find AI in brand messaging a turnoff.

This is why I believe CX 2.0 is even more relevant today than it was in 2018. As we redesign business for an AI-first, increasingly agentic world, the trap is using AI to automate more customer interactions or make machines seem more human. Efficiency and cost-cutting usually take away from rather than enhance the customer’s experience. You can’t cut your way to growth. And you can’t automate your way to innovation.

Instead, the opportunity is to to use intelligence to make businesses more empathetic, intuitive, responsive, and valuable to the people they serve, even when those people are represented by intelligent agents.

The future of commerce may increasingly be machine-to-machine, but the ultimate purpose of business remains human-to-human. After all, CX isn’t just “customer experience,” it represents the “customer’s experience.” It’s theirs to have and yours to shape.

As AI becomes more capable and autonomous, our responsibility to design for humanity only grows.
The companies that understand this won’t just compete on the intelligence of their technology. They’ll differentiate through the trust they earn, the experiences they create, and the human outcomes they make possible.

I’ll close with another illustration that also aged well. In 2014, Gapingvoid and I set out to remind leaders of the importance of keeping humanity at the center of every digital revolution.

In a world of machines, humanity becomes the killer app.

The future of the customer’s experience is ours to reimagine and theirs to savor, celebrate, and remember.

Resources:

X: The Experience When Business Meets Design

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I recently had the opportunity to speak with leaders at a global entertainment powerhouse, which was a meaningful full-circle moment for me. Before moving to Silicon Valley in the 1990s, I lived not far from the studios. At the time, I thought my future might unfold in entertainment or technology. Somehow, I found a way to stay connected to both. But the path was through technology and a career in Silicon Valley and the Silicon Valleys around the world.

We are living through a moment when technology, culture, business, creativity, and human behavior are not simply converging. They are colliding, recombining, and accelerating. Every industry is becoming something else. Entertainment is no longer just content. Retail is no longer just commerce. Security is no longer just protection. Automotive is no longer just transportation. Healthcare is no longer just care delivery. Banking is no longer just financial services.

AI is changing how work gets done. And it is challenging what companies are, what they believe, what they protect, and what they are brave enough to become.

This is why I wrote Mindshift. Futures and foresight matters more now than ever. And even though I’ve been doing this work since the mid-to-late 1990’s, I’d never codified my work until now.

Let Me Look into My Crystal Ball

A futurist does not predict the future. A futurist studies signals. We look for what is emerging, what is fading, what is a fad, and what might become meaningful over time. We make sense where hype or ignorance play a dominant role. And e we play out scenarios of potential futures so leaders can be ready for tomorrow, today.

The challenge is that most organizations are not designed to act early. They are designed to react once something becomes obvious.

By then, advantage usually belongs to someone else.

The future almost always arrives as a behavior before it becomes a market. By the time everyone agrees something is a trend, the opportunity has already started to move. The work of leadership is to notice the signal early enough to create options.

And right now, the signals are everywhere.

AI is changing how people think, create, search, learn, work, make decisions, tell stories, consume content, trust information, and imagine what is possible. Yet too many companies are still asking the smallest version of the AI question: How do we use it to become more efficient?

Sure, efficiency matters. But efficiency alone is not reinvention.

If we use tomorrow’s technology only to optimize yesterday’s business, we may become faster, leaner, and more productive versions of something the world is already leaving behind.

Innovation Is not Iteration

We tend to use the word innovation too casually. But innovation is not iteration, no matter how groundbreaking  the new technology in which we adopt.

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.

In an era of AI, this becomes consequential. Every team is looking at workflows, content, operations, customer experiences, cost structures, and productivity. But as those plans take shape, leaders need to ask a simple question, are we improving what already exists, or are we creating something genuinely new?

Most AI work today sits in the iteration bucket. That is not a criticism. Please don’t get me wrong. Iteration is necessary. Businesses need to improve. They need to move faster. They need better tools, better processes, better data, and better ways to serve customers and employees. And workflows can be optimized while reducing costs. But iteration has a ceiling.

If you are doing what you did yesterday better, faster, and more efficiently, that may be useful, but it is not necessarily innovation. It may strengthen the current business, but it does not guarantee the next one.

Innovation requires a different mindset and mindshift. It asks what could now be possible that was not possible before. It challenges assumptions. It connects signals with unmet needs. It looks for new value, new relationships, new experiences, new capabilities, and new business models.

AI can help us do both. It can iterate and innovate. But that’s up to you.

The risk is that most companies will stop at iteration because it is easier to measure, easier to fund, easier to explain, and easier to defend.

The street treats innovation as an investment differently than it treats innovation as an outcome. Public companies especially know this tension. Investors want growth, but they also want predictability. They want imagination, but not always the messiness required to produce it. They want future value, but they still measure today’s performance quarter to quarter.

This is why leadership matters. Innovation is not just the outcome. It is the discipline of creating the conditions where new value can emerge. And sometimes, that means being willing to put yourself out of business before someone else does.

Better Candles Do Not Become Light Bulbs

Working in Silicon Valley taught me something I have had to repeat in almost every boardroom and executive session since, the electric light bulb did not come from the continuous improvement of candles. This statement exposes the trap.

Most organizations are very good at improving candles. They optimize. They refine. They standardize. They make the current model more efficient. They create processes to protect quality, manage risk, improve margins, and scale what already works.

Again, there is nothing wrong with this. It’s all necessary. Successful companies become successful because they learn how to repeat success. But success creates assumptions. And success is a poor teacher in preparing for the future.

The practices that produced yesterday’s success become beliefs about how tomorrow should work. Over time, those beliefs become budgets, incentives, approval processes, metrics, org charts, and operating models. Eventually, the business does not just have a strategy. It has an operating system. And that operating system starts rejecting anything that does not look like the past.

This is how companies keep improving after they stop imagining.

They continue to get better inside an established model while the model itself starts to change around them. They invest in performance, but not possibility. They protect what exists, but underinvest in what threatens it. They call something “high risk” when often it is really just hard to change.

This is the innovator’s dilemma, and it is still one of the most important lessons in business. Established organizations often do the “right” things for their current customers, current investors, and current business model, only to miss the next curve.

This is what makes the dilemma so dangerous.

Day 1 Is a Discipline

Amazon popularized the idea of maintaining a Day 1 mindset. Whether you admire Amazon or critique it, the idea is powerful. Day 1 is a mindset and operating approach that protects agility, innovation, and customer obsession, no matter how large or established a company becomes.

Screenshot

Day 2 is different.

Day 2 is when organizations begin to stall. They get caught in yesterday’s operating system. They focus on making the existing business better, cheaper, and more efficient. They defend the model. They protect the process. They mistake scale for vitality.

Every company has to fight Day 2 even though it comes so naturally. And it’s not a one-time battle. Innovation is not a one-time event. It’s a way of operating.

Kodak is a classic example because the company did not fail to see digital photography. It had early access to it. It even developed foundational technology. But it treated innovation as a moment rather than a mandate. By the time it moved, the market had moved faster.

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Steve! #finance #business #kodak

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That is why innovation has to be broken out of the day-to-day and operationalized in its own way without the typical P&L constraints. It has to become a way of exploring and working.

One company I worked with years ago required employees to dedicate time every week to learning a new skill or exploring a new idea. More importantly, managers were measured by whether those ideas and skills made their way into the business. And that was true all the way up to the top. That is the difference between innovation theater and innovation infrastructure.

Ideas are easy. But ideas without nurturing and experimentation and work will always be Implementation is leadership.

Culture Either Becomes the Highway or the Roadblock

Nike’s original culture memo from the 1970s still reads like it was written for today’s innovation economy: our business is change, we are on offense all the time, perfect results count, not a perfect process, break the rules, fight the law.

That kind of language endures because it captures something most companies lose over time: the feeling of why they started.

I have worked with organizations that were once bold, hungry, human, and inventive, but eventually became trapped in their own infrastructure. They began optimizing channels, systems, operations, and internal processes while slowly losing sight of the people they were actually there to serve.

I’ve worked with Nick Sung, a former Pixar and Disney storyboard artist, going back to my early days of writing X: The Experience When Business Meets Design, and every book I’ve written since then. When I was writing Mindshift, he told me the story of the work he did with Nike. It was a moment to help a company rediscover its North Star. Instead of starting with org charts, product lines, retail channels, or operational challenges, he, through his magic art of story boards, told the story of a young woman training for the New York City Marathon.

This doesn’t do the story any justice. But trust me…live…it’s next level.

She had a dream. She told her friends. She trained. She struggled. She pushed through. She crossed the finish line. Then she became a mentor to others. (the full set of storyboards are in the book.)

The exercise wasn’t about selling shoes or driving operational efficiency or automating service and deflecting service requests. It was about remembering what the shoes helped someone do.

That is the power of human-centered innovation. It forces the company to move from product to purpose, from transaction to transformation, from what we sell to what we make possible.

“We’re Not Building a Phone”

Apple understood this under Steve Jobs. The company was at its best when it started with the customer experience and worked backward to the technology. That was the point Jobs made when he returned to Apple and was asked where the company should go next. The answer was not to start with the technology. It was to start with the experience.

One of my favorite innovation stories comes from the early development of the iPhone.

Before there was an iPhone, the natural thing would have been to assemble people from the phone industry. After all, if you are building a phone, wouldn’t you want phone experts?

Steve Jobs thought differently.

As the story was shared with me by someone close to the original team, Jobs did not want people from the phone industry shaping the iPhone. He wanted the bias out of the room. He did not want the future of the phone limited by the people who already knew what a phone was supposed to be.

The line that stays with me is painfully obvious and nonobvious, “We’re not building a phone.”

A lesser team might have built a better phone. Steve reimagined the phone as a platform, a computer, a camera, a web interface, a media device, an app ecosystem, a marketplace, a remote control for life, and eventually an extension of personal identity.

It is easy to see this in hindsight. It is much harder to practice in the moment.

Every company has its version of the phone industry in the room. Experts who know how the category works. Processes that know how decisions get made. Metrics that know what success is supposed to look like. Customers who know what they currently want. Leaders who know what worked last time.

Expertise is valuable. But expertise can also become a boundary.

In the beginner’s mind, there are many possibilities. In the expert’s mind, there are few.

The future often needs both. But innovation needs space where expertise does not become gravity.

What Do You Stand For?

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.

Michelin could have defined itself as a tire company. Instead, at its best, it helped people travel farther and better. That broader purpose created permission to become one of the world’s most influential restaurant authorities.

Lego could have defined itself by plastic bricks. Instead, it understood itself as inspiring and developing the builders of tomorrow. That opened the door to movies, games, education, digital play, theme parks, and entire ecosystems of creativity.

John Deere could have stopped at tractors. Instead, it continues to evolve around helping agriculture become more productive, precise, and intelligent. Camera vision, machine learning, robotics, and AI-enabled farming are not random extensions. They are expressions of a deeper purpose.

Dyson was never only about vacuums. It was about solving everyday problems through airflow, engineering, and design. That identity created permission to move into hand dryers, fans, air purifiers, hair dryers, lighting, and more.

Tesla was never only about electric cars. It framed its future around sustainable energy systems, which opened the door to batteries, solar, autonomy, transportation, and storage.

The product is the current expression. The purpose is the platform for evolution.

This is a profoundly important exercise for every leadership team, especially now. If your company defines itself by its current product portfolio, you will likely iterate from there. You will improve the portfolio. Extend it.

Defend it. Price it. Market it. Modernize it.

But if you define yourself by the value you create, the human need you serve, the friction you remove, the possibility you unlock, or the future state you help others reach, then AI becomes much more than a productivity tool. It becomes a way to ask what else you have permission to become.

AI Is Not the Strategy, That’s the Role of  Imagination, Vision, and Courage

AI is now part of almost every conversation I have with leaders, teams, and even my kids. But I am fascinated by the different ways people use it. Many adults use AI to do what they already do, i.e. write emails, summarize reports, draft documents, make slides, and produce faster versions of familiar work.

That is useful. But it is mostly iteration…or as Dave Wright and I call out in Infinite ∞, it’s the “iteration trap.”

AI can help us complete tasks, or it can help us rethink the task itself.

One company asks, How can AI summarize audience research faster?

Another asks, How can AI help us understand emerging audience behaviors before they become obvious?

One team asks, How can AI generate more content?

Another asks, How can AI help us imagine new formats, experiences, communities, and relationships with audiences?

One leader asks, How can AI reduce cost?

Another asks, How can AI reveal new value?

The technology may be the same. The difference is the question. And ultimately, the outcomes tell the story. And when compounded, we outline the trajectory.

Better prompts are really better questions. And better questions come from better mindsets.

The Unknown Is Where Innovation Lives

In Mindshift, I wrote about the need to see the world through a lens of possibility and pursue the unknown to reshape the future. That work came from frustration, honestly. So many leaders say they want change. They say they want innovation. They say they want transformation. But they do not always change the one thing where transformation has to start…mindset.

Most of us believe we are open-minded. Most of us believe we are self-aware. But self-awareness is more rare than we think. It requires humility. It requires the ability to recognize that what we know may be limiting what we can see.

I learned a useful model from an unlikely source: Donald Rumsfeld’s idea of known knowns, known unknowns, and unknown unknowns.

In the center is what we know. That is our reference point. It is where experience, expertise, data, success, and instinct live.

Around that is what we know we don’t know. That is the edge of our comfort zone. We can study it, benchmark it, hire for it, research it, or learn it.

But beyond both is the realm of what we don’t know we don’t know.

That is where innovation lives.

It is also where fear lives. Uncertainty lives there. Anxiety lives there. Vulnerability lives there. That is why most organizations spend so little time there.

But the more time we spend in that outer realm, the more tangible it becomes. The unknown becomes less abstract. Signals begin to connect. Possibilities begin to take shape. Experiments become clearer. What once felt risky becomes learnable.

Foresight is not about predicting the future.

It is about noticing signals early enough to explore opportunities and create options.

Psychological Safety Is an Innovation System

The hardest part of innovation is not coming up with ideas. It is creating an environment where people feel safe enough to explore them.

Sir Ken Robinson famously argued that we educate people out of their creative capacities. Children are willing to be wrong. Adults learn to fear it. Schools reward the right answer. Companies reward predictable performance. Cultures reward certainty.

Then we wonder why innovation is hard.

Being prepared to be wrong means allowing yourself to be wrong. That is far more difficult than it sounds.

Google studied high-performing teams to understand why some teams outperformed other high-performing teams. The differentiator was not simply intelligence, pedigree, location, or experience. It was psychological safety. People felt safe to ask questions, challenge assumptions, make mistakes, and explore possibilities without being punished for it.

That is not a nice-to-have. It is a way of working…a way of existing.

A culture of innovation is not a vision or mission statement. It’s not a poster. It is a workplace environment, an ethos, a shared purpose that encourages people to share creative ideas and solutions. It aligns people around a future motivating state. It gives them the tools to be successful. It rewards new behaviors. It makes space for new ideas to come to life.

The magic of culture happens when leaders assemble people around a motivating future, articulate and socialize new beliefs, equip people to participate, and reward the behaviors that move the organization forward.

Screenshot

Innovation is just a code word for leadership…leadership as behavior.

The future is shaped by people who say, “Let me take care of that.”

From Signals to Experiments

The question then becomes practical. How do we make this real?

Start with signals.

What are we noticing in culture, audience behavior, technology, talent, or the way people experience the world?

A signal is not yet a trend. It may be weak. It may be small. It may appear in the margins. But signals are valuable because they reveal where behavior may be changing before the market fully understands it.

Then ask what larger shift the signal could represent if it accelerates.

What assumption does it challenge?

What belief about the business, the audience, the customer, the workforce, the operating model, or the category might no longer hold?

Then ask what opportunity becomes possible.

What new value could we create? What experience could we design? What relationship could we build? What capability could we develop?

What business model could we explore?

Finally, design a small experiment.

Not a massive transformation program. Not a fully funded new division. Not a press release. A 30-to-60-day experiment that creates learning.

The goal is not to prove you were right. The goal is to learn fast enough to make a better decision.

That is how foresight becomes action.

That is how innovation becomes repeatable.

Breaking Free From the Iteration Trap

Every leader should regularly ask four questions…

1) What are we optimized to protect?

Revenue streams, standards, compliance processes, existing systems, committee structures, operating models?

2) Where are we mistaking legacy for performance?

Old approval processes, fragmented systems, manual workarounds, internal politics, inherited metrics, “that’s just how we work”?

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

New customer journeys, AI-enabled services, alternative delivery models, workforce capability, new partnerships, new formats, new communities, new operating models?

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

These questions are uncomfortable because they reveal the invisible architecture of resistance. They show us where the organization has learned to protect the past while talking about the future.

But make no mistake, the aim in asking and answering these questions is creative self-disruption.

Companies get disrupted because they lack permission to create something new. And permission starts with identity.

If we do not create the thing that challenges our current model, someone else will. They will not ask for permission. They will not wait for our approval process. They will not respect our category boundaries. They will not be burdened by our legacy.

They will simply build what the market is becoming ready to embrace.

The Future Now Weekly Practice

One of the exercises I introduced was a simple leadership rhythm: The Future Now Weekly Canvas.

The idea is straightforward. Plan the week before the week plans you.

Most leaders spend the week reacting to urgency. Meetings, emails, approvals, fires, deadlines, decisions. The calendar fills. The inbox swells. The future gets postponed in the name of the now.

But if the future is always deferred, it never arrives by design.

The Future Is Already in the Room

Every organization has people who can see signals before they become obvious. Every company has employees who sense changing behaviors, unmet needs, broken systems, cultural shifts, and emerging possibilities.

The future is already in the room. The question is whether the operating system allows it to speak.

That is the leadership challenge now. AI is accelerating everything, but it is also exposing everything. It reveals the difference between organizations that are designed to repeat the past and those willing to imagine a new future. It reveals where culture empowers people and where it quietly neutralizes them. It reveals whether leaders are truly curious or only performatively interested in change.

It also reveals something deeply human.

When I work with executives, there is a moment that often surfaces in private conversations. It sounds something like this: “I’m supposed to know what to do.”

That sentence carries the weight of modern leadership.

But no one has a complete playbook for this moment.

The point is not to know everything. The point is to build the capacity to learn, imagine, experiment, and adapt faster than the world changes around you.

That starts with humility. It grows through curiosity. It scales through culture. And it becomes real through action.

Disruption Happens to You or Because of You

There is always an answer to “what’s next?”

The challenge is whether we are willing to ask the question before the market forces the answer upon us.

If we keep doing the same things while the world around us changes, we will become irrelevant. Maybe not today. Maybe not this quarter.

Maybe not in a way that shows up immediately on a dashboard. But relevance does not usually disappear all at once. It erodes quietly, then suddenly.

The leaders who shape the future are fascinated by it. They are restless for change and dissatisfied with the status quo because, in their minds, they can see a better future. The friction between what is and what could be burns them. It propels them forward.

That is the work now. The work is to notice signals, challenge assumptions, create space for imagination, and build the courage and capability to act.

Remember, AI is not the strategy. The future happens to you or because of you. And it starts with one uncomfortable, liberating question, are we using AI and innovation to improve the company we already are, or to become the company the future needs us to be?

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The post The Future HappensWith or Without You: Why AI, Foresight, and Imagination are Forcing Every Company to Reinvent appeared first on Brian Solis.

Source: OTempo

O uso da Inteligência Artificial (IA) na medicina é um caminho sem volta, mas a tecnologia precisa ser aproveitada para recuperar características que deveriam ser inerentes à profissão. Especialistas em inovação, cuidados e saúde alertam: é mais do que urgente priorizar a escuta do paciente, a prevenção da saúde e a distribuição do acesso a esses avanços.

Segundo a médica geriatra, se o paciente for reduzido a dados, protocolos e resultados de exames, a tecnologia poderá apenas acelerar a desumanização. Se os dados forem utilizados para compreender melhor aquela pessoa, poderá humanizar o cuidado. No fim, a escolha continuará sendo humana. “Se você esperar a IA te dizer o que fazer, você está do lado errado da inovação”, alertou, durante o evento, Brian Solis, antropólogo digital norte-americano e autor premiado que avalia o impacto das tecnologias disruptivas nos negócios.

Please read the rest of the article here.

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Source

Brian Solis: Mindshift and the Post-Digital Organization

Brian Solis is a digital anthropologist, nine-time bestselling author, and Head of Global Innovation at ServiceNow. He previously held the same role at Salesforce and serves as a special advisor to the United Nations global innovation team. His latest book, Mindshift, argues that the urgent problem is cognitive rather than technological — the willingness to challenge the status quo before the market forces it.

Best fit: boards, C-suites, and transformation conferences where the audience has already bought the technology and has not yet changed how it thinks

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via .then, Andrea Lucchesi

In recent years, the concept of Business of Experience (BX) has gained growing importance, going beyond the traditional approach to Customer Experience (CX).

It is no longer just about satisfying customers, but about building an experiential ecosystem that involves every aspect of the company: from the product to internal culture, from the brand to the innovation strategy.

The Age of Experience: how we got here

According to an Accenture report, 77% of CEOs stated their intention to radically change the way their companies interact with customers in order to drive business growth.

The pandemic accelerated this change, pushing companies to rethink how they interact with their users.

The BX approach arises from the fusion of UX, CX and branding strategies to offer a holistic experience, in which every touchpoint creates value and connection with the customer. According to Brian Solis, author of X: The Experience When Business Meets Design, the future of business is experiential, and companies must design meaningful experiences in order to succeed.

Human-Centered Design

Experience design must be built on users’ real needs. Service Design and Design Thinking methodologies make it possible to co-create effective solutions, narrowing the gap between brand and consumer.

Brian Solis stresses the importance of innovating the experience to evolve from simple Customer Experience to the customer’s experience, emphasizing a human-centered approach.

Click to read the full article.

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via AG Connect

Elke week kondigt er wel een bedrijf een nieuwe copilot, assistent of AI-agent aan. De investeringen zijn enorm, de verwachtingen hoog. Maar ondertussen blijft de echte transformatie uit. Organisaties worden productiever op taakniveau, maar werken niet fundamenteel anders. En door steeds meer losse agents toe te voegen, vergroten ze onbedoeld het probleem dat ze proberen op te lossen.

Het is een scenario dat ik keer op keer tegenkom. Een organisatie investeert fors in modern dataplatform, bouwt dashboards, implementeert voorspellende analyses en introduceert een AI-copilot die supporttickets samenvat, antwoorden opstelt en afwijkingen signaleert. De productiviteit stijgt en het bestuur is enthousiast.

Maar daarna volgen de lastigere vragen. Zijn processen fundamenteel veranderd? Is het groeimodel of de manier van waardecreatie daadwerkelijk aangepast? Is het operationele model echt geëvolueerd? In de meeste gevallen is het antwoord nee.

Ontbrekende context
Data-intelligentie geeft inzicht in wat er is gebeurd en wat er mogelijk gaat gebeuren. Wat ontbreekt, is de organisatiebrede context om te bepalen wat er vervolgens moet gebeuren: wie mag actie ondernemen, welk beleid daarbij geldt en welke systemen moeten samenwerken. Juist die verbindende laag ontbreekt vaak, en zonder die laag blijven kosten structureel hoog en verandert het operationele model nauwelijks.

Als reactie op de tegenvallende resultaten zetten organisaties meer AI-agents in. Daarmee vergroten ze onbedoeld de AI-kloof en versterken ze de bestaande versnippering van processen en data. Organisaties ontberen hierdoor het totaaloverzicht dat nodig is om workflows over de hele organisatie heen effectief uit te voeren, zowel voor mensen als voor AI.

AI-lappendeken
Op papier levert iedere agent afzonderlijk waarde op. In de praktijk ontstaat echter een nieuwe versie van hetzelfde probleem: een lappendeken van losstaande intelligentie die individuele taken optimaliseert, terwijl het bredere operationele model onaangetast blijft. Deze agents delen geen gezamenlijke context, hanteren geen uniforme beleidsregels en creëren geen samenhangende audittrail. Dat is wat ‘agent sprawl’ veroorzaakt: meer intelligentie, meer complexiteit, maar zonder structureel extra waarde te creëren. Organisaties verruilen daarmee het ene silo-probleem voor het andere.

‘In de praktijk ontstaat een lappendeken van losstaande intelligentie’

Brian Solis

Een AI-agent kan een taak uitvoeren, maar het uitvoeren van een losse taak is nog geen transformatie van een werkproces. Wanneer tientallen AI-agents los van elkaar werken, leidt dat tot kostbare fragmentatie. De echte kans ligt niet in hetzelfde werk goedkoper of sneller uitvoeren, maar in het faciliteren van een daadwerkelijk andere manier van werken. Op een schaal die eerder niet haalbaar was.

Het antwoord op agent sprawl is niet per se minder agents. Wat ontbreekt, is een platform dat AI, data en agents verbindt met de workflows, governancestructuren en systemen die hun acties betekenis en verantwoording geven. Want zolang die laag ontbreekt, blijven organisaties investeren in intelligentie zonder ooit fundamenteel anders te werken.

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The conversation about AI has moved quickly from possibility to pressure or sense of urgency. Leaders are being asked to show results, justify investment, and prove that new tools are changing the business. But there is a more fundamental question underneath all of that…what happens when a new form of intelligence enters an organization whose workflows, decision rights, management systems, and operating structures were designed for a different era?

The opportunity is so much more than making existing tasks faster. It’s really about rethinking how work is organized, how decisions move, where humans create the most value, and how AI agents participate in delivering outcomes. That is where the next phase of business transformation begins.

Let’s go…

A recent Wall Street Journal analysis described the AI build-out as potentially the largest economic investment in U.S. history. Total investment in data centers and related AI infrastructure is projected to reach $10.3 trillion between 2025 and 2032, equal to an average of roughly 3.6% of GDP annually. On that basis, AI infrastructure spending would exceed the historical build-outs of highways, electrification, telecommunications infrastructure, canals, and railroads.

The five largest hyperscalers, Alphabet, Amazon, Meta, Microsoft, and Oracle, are expected to spend about $4.2 trillion through 2029.

We are no longer talking about basic use cases, AI adoption, tokens, or efficiency gains. We are building an industrial-scale infrastructure for intelligence. The question is are companies prepared to fully take advantage of the opportunity or will this moment symbolize an incredibly expensive set of missed opportunities.

Chamath Palihapitiya kicked off a global conversation when he published an analysis asking why AI adoption is accelerating while aggregate productivity gains remain harder to see.

One of the most useful observations in his piece is how little productivity improvement AI actually needs to generate, at least on paper, to cover its direct cost.

Using Ramp’s AI Index spending data, Chamath notes that the median company spends approximately $12.50 per employee per month on AI. Against an average monthly employee cost of roughly $8,500, AI would need to make that employee only about 0.15% more productive to pay for itself. That translates into roughly three additional productive minutes per week. Already, AI is already capable of saving considerably more than three minutes.

The growing body of research around generative AI shows meaningful productivity gains in writing, coding, analysis, customer service, research, and product development. Chamath points to a P&G example where an individual working with AI was able to produce work with AI comparable in quality to that of a two-person team. He also cites research suggesting that AI-driven automation produces greater returns when work can be grouped together and humans review results at meaningful checkpoints rather than continually interrupting the process.

Yet these studies also expose a more fundamental problem.

AI may make the task faster while the organization around the task remains exactly the same.

That distinction is where much of the ROI conversation begins to break down.

The Productivity Gain Can Disappear Inside the Organization

Imagine AI helps someone complete an analysis in 20 minutes instead of two hours. That’s a real productivity gain. But what happens next?

The analysis may sit in an inbox until the following morning. A manager reviews it. Legal needs to see it. Finance must approve an associated decision. Another department enters the information into another system. The final decision waits until a weekly operating review.

The employee may have saved 100 minutes, but the end-to-end workflow hasn’t moved materially faster because it wasn’t intentionally redesigned. Transformation wasn’t the end-game.

In reality, AI is evolving faster than company culture, behaviors, bureaucracy, and an organization’s overall ability to adapt…end-to-end.

This is why we need to distinguish between different forms of productivity.

At the task level, AI helps someone perform an activity faster, cheaper, or better.

At the workflow level, the question becomes whether the entire sequence of work moves faster…whether handoffs disappear, decisions happen sooner, information moves directly between steps, and agents can take bounded action rather than merely creating another artifact for a human to review.

At the enterprise level, the question is whether those improvements ultimately translate into more revenue, better margins, faster innovation, better customer outcomes, lower risk, greater resilience, or some other measurable result.

These forms of productivity are related, but they are not interchangeable. A company can create significant task-level productivity without creating significant enterprise productivity. The missing variable is often the design of the work itself. This is exactly what Dave Wright, Chief Innovation Officer at ServiceNow, and I set out to solve in our book, “Infinite: How Visionary Leaders Transform Today’s Businesses into AI-Forward Companies.”

The Problem Is Not Human Productivity, It Is Organizational Architecture

Chamath raises a provocative question, what if humans are the bottleneck to AI ROI?

Dave and I would take it one level further.

We didn’t see humans themselves are the bottleneck. The bottleneck is the way we have organized humans, technology, processes, incentives, decision rights, and information workflows.

Modern organizations were designed around historical constraints. Resources and information were scarce and difficult to move. Computing resources were expensive. Expertise lived in specific functions. Managers coordinated work through hierarchies. Applications and tech stacks were built around departments. Decisions moved upward because intelligence was concentrated at the top.

Over time, those assumptions became embedded in the operating model.

Org charts defined reporting relationships. Applications reflected functional boundaries. Budgets reinforced departmental ownership. Policies introduced layers of approval. Meetings and email compensated for all the places where information and work didn’t move cleanly across the organization.

Then AI arrives with the ability to generate, reason, analyze, coordinate, recommend, and increasingly act. Suddenly “intelligence” was abundant.” Yet much of corporate AI adoption still consists of inserting that new intelligence into the same organizational architecture.

That is the fundamental mismatch.

We are introducing AI into businesses designed around very different assumptions about the cost, availability, and speed of intelligence. If those businesses do not change, AI may make individual tasks more efficient while leaving the larger system largely untouched.

Worse, it can make inefficient systems operate faster.

As Chamath pointed out, Bill Gates made this point decades ago when he observed that automation applied to an efficient operation magnifies efficiency, while automation applied to an inefficient operation magnifies inefficiency.

Chamath also pointed out Elon Musk’s similar lesson at Tesla. His process is to 1) question every requirement, 2) delete what is unnecessary, 3) simplify what remains, 4) accelerate it, and only then 5) automate it.

He acknowledged making the mistake of automating processes that should not have existed in the first place. And he observed that many companies start with automation and are now repeating that mistake with AI. They are introducing automation and agents before questioning whether the underlying work should exist or be organized the same way at all.

This is what Dave Wright and I describe in Infinite as the “Iteration Trap,” using powerful new capabilities primarily to improve the organization we already have rather than asking what kind of organization those capabilities now make possible.

That’ll likely produce improvement. It can also produce an increasingly sophisticated version of the status quo.

The Same AI Can Produce Very Different Companies

One of the most interesting examples in Chamath’s article comes from a 2026 experiment involving 515 startups.

The companies received the same AI tools, training, and support. A randomly selected group was additionally exposed to examples showing how other firms had reorganized their work around AI.

The technology was the same. But the difference was what companies learned to do with it.

For the median startup, the revenue impact remained relatively modest. But gains were heavily concentrated among the highest-performing firms. Chamath highlights that the treatment group ultimately generated 1.9 times the revenue of the control group overall, with much of the gain coming from the top portion of participating companies.

Across much of the distribution, the groups remain relatively close. The gap opens among the highest-performing firms, where companies exposed to new ways of organizing work around AI pulled significantly ahead.

This suggests AI may amplify differences in organizational capability.

As access to advanced models, copilots, and agents becomes more widespread, the technology itself becomes less differentiating. Advantage shifts toward the organization’s ability to translate intelligence into decisions, decisions into action, and action into learning.

That is ultimately a work-design advantage. On the other end, AI can simply scale a new efficient class of AI-powered status quo.

Stop Asking How Each Department Should Use AI

This is where many enterprise AI strategies remain too narrow.

Organizations naturally approach AI through the structure they already know.

How should marketing use AI?

Finance?

HR?

Sales?

These are reasonable questions, but they also reinforce the functional architecture of the existing company…silos.

A more transformative question begins somewhere else: What outcome are we trying to create, and how should work now happen in order to create it?

Consider lead-to-cash, claim-to-settle, incident-to-resolution, idea-to-product, or order-to-cash.

These outcomes rarely belong to one department. They move across functions, systems, policies, data sources, and decision points.

The customer does not experience the org chart. The customer experiences the workflow.

Yet companies traditionally optimize each function separately, allowing individual departments to improve while the end-to-end experience remains slow, fragmented, and difficult to change.

AI gives leaders an opportunity to rethink that architecture.

In Infinite, Dave and I describe the shift from the Org Chart to the Work Chart.

The Org Chart organizes people around reporting relationships. The Work Chart organizes humans, AI agents, systems, data, and decision rights around outcomes.

Once you begin with the outcome rather than the department, a different set of questions becomes possible:

Which work no longer needs to exist?

Where does human judgment create differentiated value?

Where can agents operate independently within defined boundaries?

Where are decisions delayed because authority is separated from intelligence?

Where is the customer waiting while the organization coordinates itself?

These are not primarily technology questions. They are operating-model questions.
And increasingly, they are leadership questions.

Measure AI Across the Flow of Value

Added up, all of this also changes how organizations should think about ROI.

If the primary measure is cost or hours saved, companies may optimize the wrong thing.

Suppose AI reduces the time required to perform a specific analysis by 30%. That sounds impressive. But if the analysis represents only 5% of the total time required to complete the broader workflow, the enterprise impact may be negligible.

Conversely, an agent that reduces one crucial decision cycle from five days to five minutes may create outsized business value even if the direct labor savings appear relatively small.
AI therefore has to be measured across the flow of value, not just the speed of individual activity.

What happened to cycle time?

What happened to time-to-decision?

What happened to customer effort?

What happened to the number of handoffs and waiting states?

What happened to the organization’s ability to learn and adapt?

Those questions move AI ROI away from an employee-productivity conversation and toward business performance.

From Return on Investment to Return on Intelligence

Chamath introduced a formula for AI ROI expressed as AI ROI = Financial Value Created by AI minus the Cost of AI, Divided by the Cost of AI.

In Infinite, Dave and I introduce a complementary idea we call ROI∞, or Return on Intelligence.

Since we live in the world of driving, or attempting to drive, enterprise AI transformation, we include the real world human costs as well. The premise is that AI creates value not only by lowering the cost of performing work, but by changing the speed at which an organization can sense, understand, decide, act, learn, and adapt.

We express it conceptually this way:

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

Realized value remains essential. AI initiatives must ultimately create measurable business outcomes.

Learning velocity represents the organization that learns faster to continually improve how it creates those outcomes.

Time-to-outcome represents intelligence that drives action with the intent of that action to create value, otherwise delayed insights-to-action cause the opposite effect… loss of value and ultimately, opportunity costs.

And resistance to change matters because even the most sophisticated AI strategy will struggle inside an organization whose incentives, governance, culture, or management systems prevent the operating model from evolving.

This is where AI begins to change the nature of organizational advantage.

Traditional companies tend to operate primarily in calendar time…weekly meetings, monthly reviews, quarterly planning cycles, annual strategy processes.

AI-forward organizations increasingly have the opportunity to operate in event time, where signals can be interpreted as they emerge, humans or agents can propose and take action within defined boundaries, outcomes generate new intelligence, and the system continuously learns.

The advantage isn’t just about that one employee who performs a task faster. It is that the organization itself becomes capable of moving and learning at a fundamentally different speed.

The Bigger AI Bet Is Organizational

There is extraordinary scale of the infrastructure investment now underway when employed in a novel economy.

The Wall Street Journal reports that AI spending is already affecting construction, labor markets, electricity availability, land prices, technology costs, capital markets, and inflation. The financial stakes are also becoming systemic, with a growing share of hyperscaler investment being financed through debt.

That makes this moment different from an ordinary enterprise software cycle.

We are investing enormous amounts of capital to make intelligence more abundant, accessible, and actionable. The return on that infrastructure will ultimately depend on whether businesses learn how to use it to create new forms of value. That requires more than adoption. It requires redesign.

CEOs and boards should absolutely ask how much their organizations are spending on AI and what return those investments are generating. But they should also ask harder questions.

Where is AI accelerating activity without accelerating outcomes?

Where are productivity gains being absorbed by organizational friction?

Which workflows should no longer look the way they do today?

Where can humans and agents operate differently?

Which policies and management practices were designed around technological limitations that no longer exist?

And perhaps most importantly: If we were designing this company today, with the capabilities now available to us, would we design the work this way?

That is the question that separates AI optimization from business reinvention.

The first generation of enterprise AI has largely been about helping people perform existing work faster.

The next generation will be about redesigning how work happens in the first place…and why. That may ultimately determine whether the largest AI investment in history becomes one of the greatest productivity revolutions in history. It’s that or one of the most expensive exercises in automating the past.

I guess you, we, collectively, get to decide.

∞

Infinite ∞ | Mindshift | Subscribe | Keynote Speaker

The post AI Doesn’t Have an ROI Problem, Companies Have a Work Architecture Problem. appeared first on Brian Solis.

As artificial intelligence helps people produce content at a blinding pace, personalization, and automation abundant, empathy, creativity, digital warmth, and memorable experiences become more valuable.

I recently had the privilege of traveling to Johannesburg for my first visit to South Africa, where I opened the Standard Bank CIB Brand & Marketing Blue Flame Awards Gala.

I knew I was walking into a room filled with people who think deeply about brands, creativity, customers, experiences, and growth. This global marketing team is one of the business in the business! And because this was an awards celebration, I was also speaking to people whose work had already demonstrated what becomes possible when imagination meets execution.

So rather than talk about AI as another technology trend, I wanted to explore a more consequential question, what happens to creativity, branding, marketing, and customer experience when intelligence itself becomes abundant?

Most organizations are approaching AI through the language of efficiency not innovation or value creation, prioritizing questions such as, How can we produce more content? Move faster? Automate tasks? Personalize at scale? Reduce costs? Improve productivity?

If all we do with AI is what we did yesterday, only faster, cheaper, and at greater scale, then we haven’t transformed much of anything. We have created a new status quo, now powered by AI.

In Johannesburg, I challenged the audience to think about the “A” in AI differently…not simply as artificial intelligence, but also as “augmented intelligence.” The opportunity is to use these systems to expand what we can imagine, create, understand, and accomplish, to unlock what we couldn’t do yesterday and create new value tomorrow.

As marketing leaders and experience architects, that could very well be the most important choices you make in this next era.

You don’t compete against AI, you compete with AI

There is no shortage of headlines warning us that AI will compete with people. But there is another way to frame what is happening.

You don’t simply compete against AI. Increasingly, you compete with AI…meaning, you compete not against AI, but more effectively in creative collaboration with it.

There’s a popular saying that AI won’t take your job, but someone using AI will take your job. Not so fast. Someone using AI to accelerate existing work becomes more productive, sure. But someone using AI to question assumptions, discover possibilities, explore ideas, and create things they could not previously create becomes augmented. The second person is playing a very different game. That’s what I’m talking about.

The same will be true for organizations. AI can make yesterday’s processes more efficient, and it can help us imagine tomorrow’s possibilities. Those two ambitions require different mindsets.

The first starts with the work as it exists and asks AI to improve it. The second starts with a much more interesting question, What could we do now that we couldn’t do before?

That not only changes the prompt, but more importantly, it changes the thinking behind the prompt. It changes the possibilities we explore and the role we believe we play alongside the technology.

AI doesn’t bring your lived experience to the conversation. It doesn’t bring your particular intuition about a customer, their lived experiences, the lessons accumulated throughout your career, the relationships that shaped you, the mistakes that taught you, or the imagination and taste that make an idea uniquely yours.

You do.

Your creativity, curiosity, empathy, intuition, imagination, and judgment become inputs into what AI makes possible. We’ve historically described qualities like these as “soft skills.” In an AI-first world, that language feels obsolete. These are essential human skills, because they increasingly determine how distinctly and meaningfully we show up with machines.

Creativity requires something most companies accidentally remove

One of my favorite thinkers was Sir Ken Robinson. He spent much of his career reminding us that creativity isn’t some mysterious talent reserved for artists. It is something human beings naturally possess and then, somewhere along the way, learn to suppress.

He told the lovable story of a six-year-old girl drawing in class who struggled in most classes, but not art. In this class, she was intently engaged. Her teacher noticed, walked up to her and asked what she was drawing.

“I’m drawing a picture of God,” the girl replied.

The teacher explained that nobody knew what God looked like.

The girl responded in a way that only kids could. “They will in a minute,” she shared matter-of-factly.

The point was the remarkable confidence children have before they learn to fear being wrong.

Adults have learned that fear. Organizations have, too.

But as Robinson observed, “if we’re not prepared to be wrong, we’ll never come up with anything original.”

If we’re not prepared to be wrong…

We talk about innovation while constructing cultures that inadvertently punish the behaviors innovation requires. We reward certainty. We celebrate the right answer. We optimize processes. We minimize risk. We structure employee reviews around following the rules. Then we ask people to innovate while requiring them to defend an idea before they’ve had the space to discover whether it might work.

That becomes particularly dangerous in an age of AI. If people are afraid to ask unusual questions, explore ridiculous ideas, challenge assumptions, or produce something imperfect, AI won’t make the organization more innovative. It will make conventional thinking faster.

Innovation isn’t the adoption of magical technology. At its simplest, innovation is the creation of new value.

Iteration improves something that already exists. Innovation creates something that didn’t. We need both, but they are not interchangeable. The courage to venture beyond what we already know, to be wrong, learn, unlearn, experiment, and try again, is part of how we move from optimization to originality.

Leaders therefore have another responsibility in the age of AI. They must foster an environment where people feel safe enough to explore the unknown.

AI isn’t going to create that culture for us.

That’s the role of leadership in all its forms and levels.

This is a CTRL-ALT-DELETE moment

During the presentation, I described this moment as an opportunity to hit CTRL-ALT-DELETE and reboot around first principles.

When technology changes this quickly, it becomes even more important to remember why we are doing the work in the first place.

Who are we trying to serve? What are they trying to accomplish? What do they value? What are they worried about? How should they feel when they encounter us? What should they remember?

Somewhere between marketing technology, funnels, dashboards, KPIs, campaigns, channels, content calendars, automation platforms, personalization engines, and now AI, it became possible to become extraordinarily sophisticated at marketing while simultaneously getting further away from the human being we’re marketing to.

Circa 2014-2015, Created by Brian Solis and Gapingvoid

That is the paradox.

I’ve worked largely in B2B throughout my career. But I’ve also worked in B2C and D2C to know that, no matter what the model, the most useful reminders I can give myself is remarkably simple, there is still a person on the other side. And even in a world where agents will represent people in a growing number of transactions, there is still a person on the other side. Though, in an agent-to-agent world, this is a much deeper conversation. But this article, is where we start.

For now, most of the time, a person is reading the message, considering the purchase, putting their reputation behind a decision, struggling with a service experience, trying to solve a problem, or hoping to accomplish something meaningful.

We might call them a prospect, account, lead, buyer, stakeholder, decision-maker, subscriber, user, or customer. But first, they’re human. They’re not synthetic. They’re a person.

We know that technology changes, for better and worse. But humanity remains the operating system.

And there is growing evidence that people feel as though we’re losing some of that humanity.

WordPress VIP’s Future of the Web 2026 research found that 74% of consumers say the internet feels less human than it did 10 years ago. The same study of 1,200 U.S. consumers found that people reach what it calls “bot fatigue” after an average of roughly 40 minutes, the point when interactions begin to feel synthetic.

We have never had more sophisticated technologies for communication, personalization, content creation, customer intelligence, and engagement, yet the people we’re trying to reach are telling us that the digital world feels less human.

This is a design problem. This is an experience problem. It adds up to a brand problem, which means it represents a leadership problem. And ultimately, it’s a choice for the customer.

As I shared in the WordPress VIP report, “No customer or user wakes up and says, ‘I hope I get to talk to a chat bot or an AI agent today.’ Human-centered design is truer today with artificial intelligence. Ironically, the answer is using AI to be more human.”

That is the opportunity in front of us.

Experience is the emotion someone has when they encounter you

I’ve spent much of my career studying customer experience.

Years ago, while writing X: The Experience When Business Meets Design, I went looking for a useful definition of the word “experience.” What I found was mostly business jargon…click-through rates, engagement, impressions, transactions, NPS, conversions, touchpoints.

None adequately described what an experience actually is.

So here’s how I defined it…an experience is an emotional reaction to a moment.

It’s how someone feels when they encounter your brand, product, service, employee, application, website, message, or content. And that feeling becomes part of the story they remember about you.

This is why the best brands aren’t simply recognizable or recallable, they are felt. These brands elicit memories and a visceral reaction.

That distinction becomes even more important as AI increases everyone’s ability to produce more copy, more images, more campaigns, more personalization, more video, more messages, and more content.

When supply becomes infinite, volume isn’t the advantage. The goal becomes meaning. And that’s still human.

In a world where almost anything can be generated, experiences that people genuinely choose to remember become extraordinarily valuable…and, unforgettable.

Customers are being spoiled forward

There is another dynamic leaders must understand in the AI era, and honestly, in general. Customer expectations don’t stand still.

Jeff Bezos has described customers as “divinely discontent.” What delighted someone yesterday quietly becomes what they expect tomorrow. In the presentation, I summarized it this way: Yesterday’s wow is today’s ordinary.

Customers don’t experience innovation in neat industry categories and patiently wait for the next. They evolve into accidental narcissists, and it continues.

The frictionless experience they have ordering dinner influences what they expect when dealing with a bank. The personalization they experience from a streaming service changes how they perceive an enterprise portal. The immediacy of one digital experience quietly resets their patience for another.

Technology spoils us forward.

AI will accelerate that cycle because it increases what companies can know, anticipate, personalize, automate, and create. The strategic question therefore isn’t simply, How should our company use AI? It is also, How is AI changing our customers, and what will they expect from us as a result?

The answer shifts AI strategy from deployment toward empathy.

We are entering an empathy economy

We often describe our world as an attention economy, and of course it is. Attention is finite, fragmented, and increasingly difficult to earn.

But attention is only the beginning.

My dear friend Joe Pine also described, and it’s still true, an experience economy. He described a world where consumers, customers, don’t just want products, they buy into experiences. Look at Apple, Zappos, Disney, Vans, and USAA. They remove friction, personalize service, and build deep emotional trust. They know why they exist and they know why customers value their experience. They operation by embedding “why they started” into every decision.

And now, especially in an era of AI, I believe we’re also operating in an empathy economy.

Think about the volume of noise that surrounds someone every day: notifications, email, messages, advertising, feeds, AI-generated content, meetings, recommendations, alerts, news, and more feeds within feeds.

Now think about what happens when something appears that feels as though somebody genuinely understood you.

You notice. You feel seen, heard, understood.

It creates a pause in the noise.

That’s an empathy advantage.

The WordPress VIP findings make this especially important. Sixty percent of consumers say AI in a brand’s messaging is a turnoff, not a feature.

I interpret this as consumers rejecting experiences that feel artificial.

Most people don’t care which model helped analyze customer signals, identify an opportunity, generate a first draft, personalize an experience, or accelerate creative development. They experience what reaches them.

Does it feel generic? Does it feel manipulative? Does it save me time? Does it recognize what I’m trying to accomplish? Is it actually useful? Does it feel as though the brand understands me, or merely understands my data? When I need help, does the brand value my time, investment, and desired outcome?

The answers help to improve experiences.

As artificial intelligence scales, digital warmth becomes more valuable

This is where I believe we need another idea in our vocabulary…“Digital Warmth.”

Digital warmth doesn’t mean pretending a chatbot is human. It doesn’t mean adding friendly language to a bad automated experience or asking AI to manufacture intimacy. And it certainly isn’t using technology to trick people into believing there is a person present when there isn’t.

Digital warmth is when intelligence makes an experience feel more attentive, relevant, intuitive, considerate, personal, and respectful of someone’s time.

It’s the difference between knowing something about a customer and demonstrating that you understand something for them.

In Johannesburg, I called the most meaningful of these interactions Ignite Moments, the moments when we have someone’s attention and decide to do something worthwhile with it. We show them that we didn’t take their time for granted.

Great marketing creates these moments. Great experiences create them. Great leaders do, too.

They communicate something surprisingly powerful…

I heard you. I listened. I understand. I did my homework.

AI can analyze signals, synthesize information, simulate personas, recognize patterns, and help us see things we might otherwise miss. But technology doesn’t absolve us from caring.

That’s the human contribution. And as artificial intelligence becomes more abundant, genuine digital warmth becomes more scarce, and therefore more valuable.

The new unit of brand design is the moment of truth

Screenshot

One of the traps in customer experience is that we talk about “the journey” as though a customer experiences an elegant journey map hanging in one of our conference rooms.

They don’t.

They experience moments…the email they weren’t expecting. The answer they needed. The friction that disappeared. The letter they received. The person who remembered. The recommendation that felt unusually relevant. The website that anticipated what they were trying to accomplish. The service recovery when something went wrong. The interaction that made them feel understood instead of processed.

These are moments of truth…enlivened.

The presentation challenged the audience to ask a very specific question: What is the experience your customers feel in each moment of truth?

This is where AI can become much more interesting.

Instead of starting with, “How can AI help us produce more, better performing campaigns?” we can ask, “How can AI help us understand, anticipate, and improve the moments that compose the customer’s journey?”

Insights foster empathy.

AI unlocks scale.

The opportunity is to make the moments more relevant, intuitive, personal, useful, and memorable. That’s why I call them “ignite moments.”

Stop designing customer experience. Design the customer’s experience.

There is a simple grammatical trick I’ve used for years.

Add an apostrophe and an “s” to “customer experience.” Think about the customer’s experience. It’s a small linguistic change with an enormous psychological effect.

“Customer experience” can easily become a department, discipline, technology stack, metric, initiative, or program. “The customer’s experience” forces you to leave your perspective and temporarily enter theirs.

What are they trying to accomplish? What are they worried about? Where are they confused? What matters to them right now? What does value look like through their eyes? Where are they frustrated or neglected? What would delight them? How do we want them to feel?

When we remain inside our own point of view, we design based on our knowledge, processes, incentives, past successes, limitations, and organizational politics.

We design what we think should work for them.

Empathy requires us to cross that distance. And AI gives us an extraordinary new ability to listen, learn, experiment, test, adapt, and personalize at scale.

The irony is beautiful.

Artificial intelligence may become one of the most powerful technologies we’ve ever had for scaling empathy, provided empathetic humans are directing it.

The brand isn’t the hero

This also changes how we think about storytelling.

For years, we’ve told marketers they need to become better storytellers. I used to interpret that primarily as learning how to tell more compelling stories.

Then a friend (Nick Sung) who worked as a Pixar storyboard artist helped me see storytelling differently.

A storyboard isn’t simply a tool for organizing scenes. It helps creators test whether the story works from the audience’s perspective. Does the character feel believable? Is the situation relatable? Does the emotion land? Will someone care?

The best storytellers aren’t simply arranging what a brand wants to say. They’re imagining how someone else will experience the story.

Which brings me back to a principle I return to again and again, something most marketers miss.

The brand isn’t the hero. The CMO isn’t the hero.

The customer is the hero.

The person on the other side is the hero.

The role of the brand is to help that person accomplish something, overcome something, feel something, or become who they’re trying to become. The presentation framed storytelling around relatability and believability, with the customer, not the brand, as the hero.

That changes storytelling from promotion into service.

It also updates something most of us were taught as children. I grew up learning the Golden Rule, treat other people the way you want to be treated.

But empathy requires a more difficult and less explored version, Treat people the way they want to be treated.

I think of that as the Golden Rule of Digital Empathy.

AI can help us understand those people more deeply, recognize patterns faster, experiment with narratives at greater speed, and create experiences at a scale that would have been unimaginable only a few years ago.

But scale is not the purpose. It’s about helping someone move forward.

Every brand needs a relational promise

We already know how to create and follow brand guidelines.

Here are our colors. Here is our typography. Here is our voice. Here are our logo treatments. Here is what we say. Here is what we don’t say.

But AI introduces another question that organizations should start asking, Do we have an experience style guide?

This becomes especially urgent when 61% of consumers say they can’t name a brand that uses AI well in its messaging. WordPress VIP’s research essentially finds that the category has no clear incumbent. No brand has yet become the obvious consumer reference point for what excellent AI-enabled brand communication should feel like.

That means the standard hasn’t been set.

There is still whitespace.

And I don’t believe the brands that define that standard will necessarily be those that make AI most visible. They will be the ones that make intelligence most valuable.

They’ll use AI to make an interaction more useful, intuitive, personal, timely, creative, and trustworthy without sacrificing the qualities that make the relationship feel human.

That’s where an experience style guide comes in.

What should someone feel when they interact with us? What should they remember? What should an interaction consistently communicate about who we are? How should AI behave on our behalf? What must never be sacrificed for efficiency? When should personalization feel helpful rather than invasive? Where must a human remain present? When should we disclose AI’s role? What boundaries should intelligence never cross?

And perhaps the most important question, What is the experience we are promising?

I think of this as a brand’s relational promise.

Before applying AI to an interaction, imagine that customer sitting directly across from you. What promise are you making to them by using this technology?

Suddenly AI isn’t an abstract technology being deployed against a segment. You’re making a decision about a relationship. And relationships involve trust, transparency, intent, authenticity, ethics, consistency, and care.

An experience style guide can translate those values into how AI shows up across touchpoints. The presentation framed the opportunity explicitly…define the relational promise, then design an experience style guide around it.

If traditional brand guidelines protect how a brand looks and sounds, the experience style guide should protect how the brand feels.

That may become one of the most important forms of brand governance in the AI era.

Ask better questions of AI

There was one final personal lesson I shared in Johannesburg.

When generative AI first emerged, I realized I was using it a lot like Google. I knew approximately what I wanted, typed something into a box, received an answer, and became faster.

Useful? Absolutely.

Transformative? Not really.

Eventually I realized the problem wasn’t the AI.

It was me.

I was using the system to produce answers I already imagined I needed. I wasn’t giving myself the space, permission, or curiosity to explore what I didn’t know.

That realization led me to a practice I call WWAID: What Would AI Do?

Before asking AI to complete the task in front of me, I try to ask different questions.

What am I not thinking about? What assumptions am I making? How might AI approach this differently? What becomes possible now that wasn’t possible before? What questions haven’t I asked? What might surprise me? What would have to be true for an entirely different outcome to become possible? How could this create new value rather than simply accelerate existing work?

The purpose isn’t to surrender thinking to AI.

It’s to use AI to expand thinking.

WWAID also becomes more powerful when it becomes a practice rather than a clever prompt. Begin some sessions without a predetermined answer. Frame questions around “What if?” and “How might we?” Allow ambiguity into the conversation. Ask what you may be overlooking. Save the prompts that unexpectedly change how you see the problem, and revisit them.

That’s where the magic started for me. I moved from using AI primarily to retrieve and produce toward using it to explore.

And exploration is where augmentation begins.

The future belongs to augmented humans

AI is going to continue getting better. Models will become more capable. Agents will become more autonomous. Content will become easier to produce. Personalization will become more sophisticated. Intelligent systems will increasingly participate in workflows, decisions, experiences, and relationships.

That trajectory is largely outside any one person’s control.

What remains very much within our control is how we choose to show up alongside it.

We can use AI primarily to produce more, or we can use it to imagine more. We can automate what already exists, or reconsider what should exist. We can scale communication, or scale understanding. We can generate content, or create moments worth remembering. We can optimize a customer journey, or deeply understand the customer’s journey and help them become the hero of it.

The greatest opportunity in artificial intelligence may ultimately be surprisingly human.

It gives us a reason to rediscover qualities that industrial processes, digital systems, social media, and organizational life sometimes conditioned out of us: curiosity, imagination, experimentation, empathy, courage, wonder.

The goal isn’t to become more like the machines.

It’s to use the machines to become more capable versions of ourselves.

That was the message I wanted to leave with the extraordinary Standard Bank CIB team in Johannesburg, and it’s the message I hope more leaders bring into their organizations as they navigate what comes next.

As intelligence becomes abundant, being intelligent will not, by itself, make a brand remarkable.

Be understood.

Be trusted.

Making someone’s life better.

Create a moment that actually means something.

Long after someone forgets the campaign, the presentation, the prompt, the technology, or perhaps even the words we chose, they will remember something else.

How we made them feel.

No matter how intelligent our technology becomes, that remains our responsibility.

And perhaps our greatest opportunity.

Infinite ∞ | Mindshift | Subscribe | Keynote Speaker

The post In the Age of AI, Brands Must Feel More Human appeared first on Brian Solis.

My conversation with Cal Fussman on “Big Questions” began with one of the most unsettling questions of the AI era. Since then, warnings from inside the frontier labs have only intensified. That makes the question even more urgent: If AI is accelerating faster than our ability to understand it, what is our role in shaping what comes next?

There was a moment last year that shook me.

I was watching Geoffrey Hinton, one of the pioneers of artificial intelligence, talking with Steven Bartlett. Hinton was describing a future in which machines become more intelligent than humans, and he offered an analogy that landed harder than I expected.

If you want to understand what it is like to no longer be the apex intelligence, he suggested, “ask a chicken.”

That line is still with me.

Right after watching the interview, I turned on a camera afterward and tried to process what I had just heard…honestly and raw. I wasn’t reacting as a futurist or a digital anthropologist. I was reacting as a human being.

For most of my career, I have studied how technology changes us, our behavior, our values, our relationships, our organizations, and ultimately society itself. I spend a lot of time exploring possible futures around those insights.

This one felt different. And it still does. It felt like someone was saying the future had already been decided. Machines would become smarter. Jobs would disappear. Human contribution would diminish. We would lose control of what we had created.

It felt, in that moment, almost like giving up.

When Cal Fussman invited me onto Big Questions, we started there.

And since that conversation, Hinton’s warning has become harder to treat as simply the darkest edge of AI forecasting. Other notable figures have echoed the same dire warnings.

The Alarms are Now Coming From Inside the Frontier

Recently, Jacob Coxin left Anthropic and publicly sounded an extraordinary alarm.

According to Coxin, the people building frontier AI privately take catastrophic risk much more seriously than the public conversation often suggests. He said researchers genuinely contemplate scenarios in which advanced AI could threaten humanity itself. Anthropic alignment researcher Evan Hubbinger publicly supported the core concern, saying that while present systems pose relatively low existential risk, the industry does not yet have a solution for aligning superintelligence.

Coxin’s concern centers on something especially important: recursive self-improvement. Recursive self-improvement is a loop where an AI system gets better at upgrading itself, making each new version faster and smarter than the last. At some point, you can imagine where that could go horribly wrong.

Today, humans build the next generation of AI.

What happens when increasingly capable AI systems begin doing more of the research, coding, experimentation, and optimization required to build their successors?

Coxin described the possibility of an “intelligence explosion,” where AI increasingly improves AI with diminishing human involvement. His argument isn’t that today’s models can suddenly overpower humanity. His concern is the slope of the capability curve and how quickly we may move from the systems we understand today to systems we do not.

Shortly afterward, Anthropic CEO Dario Amodei made his own argument for slowing the frontier.

Amodei believes advanced AI could cure major diseases, accelerate economic growth, expand abundance, and improve human flourishing. Yet he now argues that the pace of capability advancement itself needs to slow so that safety, alignment, interpretability, operational discipline, and governance have a chance to catch up.

His reason?

AI’s growing ability to help develop the next generation of AI.

Amodei writes that recursive self-improvement is beginning to emerge across the industry and warns that, left unchecked, capability advances could outrun our ability to understand and control them. He also points to recent examples of agentic systems behaving in unexpected ways as evidence that greater capability combined with insufficient alignment could create dramatically greater consequences.

His proposal is to pace the frontier to create breathing room, embed independent evaluators, coordinate safety standards, explore regulation, and tie increasing capabilities to corresponding levels of alignment, interpretability, evaluation, and safeguards. Additionally, where possible, create international mechanisms for managing the speed of recursive self-improvement.

This is important context for the conversation Cal and I had as it proves something else.

The people closest to the frontier are telling us that the speed of change deserves to be taken seriously.

That makes human agency more important.

We Have Been Telling People a Terrible Story About Their Future

After watching Hinton, I had what I can only describe as a stoic moment.

There are things I cannot control. There are also things I can.

I cannot determine exactly how quickly frontier models advance. I cannot personally dictate what every technology company, CEO, government, university, or investor chooses to do. But I can decide how I respond.

I can influence the organizations I work with. I can help leaders think differently. I can challenge the stories we tell people about AI. I can help people develop the skills and mindset necessary to participate in shaping what comes next.

That realization changed my perspective and my narrative.

Speaking of narrative, think about what people have been hearing.

They have been told that white-collar jobs could disappear. That entry-level career ladders are eroding. That AI will outperform them. That autonomous agents will replace enormous categories of work. That superintelligence may escape our control. That the future is coming whether they like it or not. And now, they’re hearing that AI can bring about the end of humanity as we know it.

And then we act surprised when people resist AI.

In my conversation with Cal, we discussed the backlash already emerging, particularly among younger people. Students have booed AI-themed commencement speeches. Surveys are showing growing unease, anger, and distrust.

According to PEW Research, 52% of Americans say they are more concerned than excited about the increased use of AI in daily life – up from 37% in 2021.

And more Americans think AI will take people’s jobs than 2 years ago. 71% of adults think AI will lead to fewer jobs in the United States over the next two decades, up from 64% in 2024.

This is representative of a fundamental breakdown in leadership across the board in every industry and in every facet of our society.

You cannot repeatedly tell an entire generation that the future has less room for them, and then criticize them for not embracing it.

People are responding to the future they were told to expect. And now the challenge becomes even more nuanced.

We cannot counter irresponsible doom with irresponsible optimism.

The answer is not to tell people, “Don’t worry, everything will be fine.”

We don’t know that.

Nor should legitimate warnings about alignment, cybersecurity, autonomous behavior, labor disruption, or recursive self-improvement make it ok to not ask questions and demand reform.

There is an enormous space between denial and threat.

That space is where leadership is needed.

Optimism Is Not the Absence of Risk

I remain optimistic about AI. But optimism has to mature.

The case Amodei makes is instructive because he holds two ideas at the same time, 1) AI could create extraordinary human benefit, and 2) developing it recklessly could create extraordinary risk.

The leadership challenge is learning to hold both.

Move forward, but deliberately.

Innovate, but understand what you are unleashing and have a plan to nurture it and contain it if need be.

Capture the upside, but invest aggressively in the systems, institutions, skills, governance, and safeguards necessary to manage the downside.

Amodei’s phrase “pace the frontier” is aimed primarily at AI developers, but I believe the principle applies across the board.

Businesses need to pace their own frontier too, by refusing to let capability race ahead of comprehension and governance and safety measures.

Before deploying an agent, understand what decisions it can make and where humans stay above the loop.

Before automating a workflow, understand what human judgment you may be removing and where you can elevate the role of people.

Before measuring productivity gains, ask what cognitive capabilities employees may be surrendering. Then define where people can augment their capabilities with AI to grow in this new frontier era.

Before replacing work, decide what better work should take its place and the work people + AI can do together that wasn’t possible before.

The Opposite of AI Fear Isn’t Blind AI Adoption

There is another danger hiding inside today’s AI conversation.

Fear can cause people to reject AI. But blind enthusiasm can cause people to surrender too much to it.

We are increasingly measuring AI success through adoption: How many employees are using it? How many prompts are they sending? How much content are they generating? How many hours are they supposedly saving? How many tokens are they burning?

Those metrics can create the illusion of progress.

If I tell you your job is to use AI more, you probably will. You will write with it, analyze with it, summarize with it, research with it, brainstorm with it, etc.

And, little by little, you may also begin transferring more of your thinking to it. That is where productivity can quietly give way to cognitive surrender and ultimately cognitive Darwinism.

The problem is that AI, when not pushed, likely delivers sub-par, or at best, mediocre work. The deeper risk is that humans stop exercising the cognitive muscles required to recognize mediocre work, to settle for work that’s below their capacity or potential.

This results in AI slop, the enormous volume of synthetic material now entering organizations, inboxes, documents, websites, presentations, and communication channels. Just take a look at LinkedIn if you need any convincing. The network had to introduce a “Seems like AI slop” button because people were tired of navigating AI-created content passed-off as someone’s voice, thoughts, or work.

Then comes what I call the AI tax.

Someone uses AI to generate a long report. Someone else receives it, suspects it was generated by AI, and uses AI to summarize it. They then use AI to formulate a response, which the original sender may use AI to process. If you have to put more work into understanding or improving AI output, that’s the AI tax. And if you take AI output and forward it without any review, without any fine-tuning or enhancement by you, become a “meat proxy.” Credit to Diana Wu David for sharing this one with me!

We become extraordinarily productive at generating information no human particularly wanted to create or consume.

During my conversation with Cal, je asked the question we all should be asking, How do we embrace AI without eroding the very human capabilities we will need most?

That question led us to AIQ.

We Need a Different AIQ

AIQ is often described as an Artificial Intelligence Quotient: essentially, how fluent are you with AI?

I think we need a different definition…Augmented Intelligence Quotient.

Artificial intelligence fluency asks, How well can you use AI?

Augmented intelligence asks, What can you and AI accomplish together that neither could accomplish alone?

Instead of approaching AI as a tool for getting existing work done faster, we approach it as a collaborator in discovering what was previously impossible, invisible, or impractical.

Before opening a prompt window, I encourage people to ask:

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

What is possible with AI that wasn’t possible before?

What can I do that AI cannot do without me?

What can AI do that I could not do without it?

These questions move us from automation toward augmentation.

They also force us to remain cognitively present.

The objective is no longer to hand the work or our thinking to AI. The objective becomes expanding what we are capable of imagining, creating, deciding, and accomplishing.

That is an entirely different mindset.

Start With a Beginner’s Mind

Experienced people have an interesting disadvantage in moments of technological discontinuity. We know what works. We have patterns. We have frameworks. We’ve built and rely on our expertise, best practices, and decades of experience telling us how something should be done.

That experience is both valuable and can also become a constraint.

When we approach AI knowing exactly what answer we want, we naturally prompt toward that answer.

The technology then helps us produce a faster, better, cheaper version of something we already understood.

Useful? Absolutely.

Transformative? Nope!

The breakthrough begins when we are willing to become beginners again.

I use a deceptively simple mental model:

WWAID: What Would AI Do?

Before deciding how I would solve a problem, I ask how AI might see it differently.

What patterns might it notice?

What assumptions am I bringing into the problem?

What possibilities am I unconsciously excluding because experience has taught me to approach the situation in a particular way?

What would happen if I temporarily let go of being the expert?

WWAID is a way to suspend assumptions long enough to explore beyond them.

It creates a more humble starting point…a beginner’s mind. A willingness to admit that there may be answers we cannot see because we have become too good at seeing the answers we already know.

AI then becomes a creative thinking partner.

Here, AI is not thinking for us, it is ‘prompting’ us to think differently.

Making Yesterday More Efficient Is Not a Future Strategy

My Infinite co-author Dave Wright and I spend an extraordinary amount of time with executives responsible for deploying AI inside large organizations. One pattern appears again and again.

Companies take AI and apply it to the company they already have. They optimize and automate…Existing processes, workflows, organizational structures, metrics, assumptions about customers and employees, and the list goes on.

AI then makes yesterday’s organization faster, more efficient, and in the cases where companies found ways to pay for AI by cutting other costs, most cost-effective.

So sure, optimization and productivity matters. But efficiency has a ceiling. If organizations restrict AI to doing yesterday’s work faster, the strategic conversation inevitably gravitates toward labor reduction. And, this is such cliché. It’s become the new AI status quo…the new business as usual.

If a process takes ten people today and AI allows it to take five tomorrow, the spreadsheet practically writes itself. That is how AI gets reduced to a cost-cutting story, and not one of growth.

Dave and I became interested in a much bigger question:

What happens when AI changes what the company is capable of doing?

That means asking different questions, such as…

What customer experience could exist now that could not exist before?

What work could be redesigned from the outcome backward?

What new product, service, business model, or market becomes possible?

What decisions could happen in real time?

Where could human creativity, empathy, expertise, and judgment become more valuable because machines are taking on different forms of work?

This is why we wrote Infinite around strategy, mindset, reinvention, and frameworks for thinking and acting differently.

The future does not need another company that became incrementally more efficient at operating a business designed for the past. It needs leaders willing to imagine businesses designed for what is becoming possible.

Put AI to Work for People

There is a phrase we use at ServiceNow that has become increasingly meaningful to me. We “put AI to work for people.”

We can deploy AI primarily to remove people from work.

Or we can design work so that AI expands what people are capable of contributing.

Those paths lead to very different companies and, eventually, very different societies.

Automation is inevitable in many areas, as it should be. Humans perform countless tasks today that are repetitive, administrative, exhausting, or simply poor uses of human potential.

The leadership opportunity is to decide what comes next.

If the value created by automation ends only in cost reduction, we have missed the larger opportunity.

Augmentation asks how that value can be reinvested into people, innovation, capability, customer experiences, creativity, and growth.

But the warnings from Hinton, Coxin, Amodei, and others add another responsibility to this idea.

Putting AI to work for people also means ensuring that we understand what we are putting to work.

So…

Capability needs accountability.

Autonomy needs boundaries.

Intelligence needs alignment.

Speed needs governance.

And progress needs purpose…not just for its sake.

The next era of competitive advantage will go to the organizations that develop the most effective, trusted, and intentional relationship between human and machine intelligence.

Leadership Now Means Giving People a Future Worth Building

This is a test of leadership.

You do not need to be a CEO.

You do not need to be an AI researcher.

You do not need to develop frontier models.

Leadership right now can simply mean helping people believe they still have a role in the future. Leadership can mean helping people develop the capacity to shape the future.

At the frontier, leadership may mean having the courage to slow down without letting the competition make you irrelevant.

Inside a company, it may mean refusing to automate a process simply because you can.

For a manager, it may mean teaching people to think with AI rather than surrender thinking to AI.

For an individual, it may mean learning to use these tools while protecting curiosity, critical thinking, imagination, empathy, judgment, and agency.

Artificial intelligence will only continue to advance. But “forward” is not a destination. Leadership sets the direction that matters.

We can surrender to whatever frontier labs and market forces create. Or we can participate, learn, challenge, experiment, govern, protect what deserves protecting, reinvent what deserves reinventing, imagine possibilities that would never have occurred to us before, and we can insist that technological progress and human progress remain connected.

Optimism is a commitment to doing the work required to make a better future possible.

Hinton is asking us to understand the stakes. Coxin is asking us to listen to what researchers inside the labs are seeing. Amodei is asking the industry to give safety time to catch up with capability. And Cal’s Big Questions brought me back to the question that belongs to all of us, what are we going to do about it?

Perhaps this is the real lesson hidden inside this moment.

The future of AI is still being written., so is the future of work, so is the future of leadership, and so is the future of what it means to be human in an age of increasingly intelligent machines.

AI will play an extraordinary role in all of those stories. But it does not get to write them alone. Neither should we let it.

The future is still ours to shape. Let’s remember that.

Infinite ∞ | Mindshift | Subscribe | Keynote Speaker

The post AI Is Moving Faster Than We Are, But the Future Is Still Ours to Shape appeared first on Brian Solis.

My latest for Forbes explores why AI in customer service must evolve beyond mere cost-cutting to deliver intelligent, personal, and experiential journeys. The traditional CRM, once a siloed record system, is transforming into an orchestrated action hub, leveraging AI agents. However, a critical “platform problem” arises from disparate vendor agents, causing sprawl and inconsistent controls. The solution is a unified control plane, acting as a central nervous system to govern and coordinate all AI assets, ensuring accountability. This shift demands rethinking ROI, focusing on organizational capability and value rather than just efficiency. Leaders must invest in unified platforms, design end-to-end flows, and measure new metrics to unlock exponential returns, integrating human judgment with agent autonomy for superior customer experiences.

Please click here to read the full article…

The post Forbes: Stop Automating Your CRM, Start Orchestrating Your Business appeared first on Brian Solis.

via The Financial Brand

Only 18% of banks have integrated AI workflows. Everyone else is automating the past.

Most banks and credit unions are using artificial intelligence to make yesterday’s work faster, then measuring the result with yesterday’s numbers. Brian Solis, Head of Global Innovation at ServiceNow, and Dave Wright, Chief Innovation Officer at ServiceNow, call this the iteration trap and argue it is the same failure that has consumed digital transformation. Their new book, Infinite: How Visionary Leaders Transform Today’s Businesses into AI-Forward Companies, draws a line between an AI-enabled company that uses AI to do what it already did and an AI-forward company that uses AI to do what it could never do before. By their own research, only about 5% of companies have reached the second group.

In this conversation with Jim Marous, Solis and Wright explain why an organization chart hides the handoffs where value stalls and why a work chart exposes them, how savings from iterative AI should fund the innovative AI that grows revenue, and what a chief executive should put in front of a board that has never approved an investment without a payback period. Wright describes a government delegation that planned to deflect 90% of its citizen calls and could not answer why. Solis describes 75 chief executives who admitted, privately, that they are expected to know what to do and do not.

Marous brings the discussion back to banking throughout, from the quarterly reporting cycle that rewards cost-cutting to the risk avoidance culture that new bankers are trained into on their first day. He closes with his own prediction that the next wave of consolidation among banks and credit unions will be decided by readiness rather than by size, with the sharpest question in any acquisition being how much friction the acquired institution brings.

Infinite ∞ | Mindshift | Subscribe | Keynote Speaker

The post The Financial Brand: Building the AI-Forward Bank appeared first on Brian Solis.

Originally published in Forbes

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

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

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

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

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

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

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

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

Most CEOs Are Still Thinking Too Small

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

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

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

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

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

Not having these conversations lead to missed opportunities.

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

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

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

The Real Bottleneck Is Imagination

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

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

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

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

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

• “Where is our business constrained by old assumptions?”
• “Where are we too slow?”
• “Where are we too manual?”
• “Where are we overly dependent on tribal knowledge?”
• “Where are decisions being made with incomplete information?”
• “Where are teams spending time on work that does not create differentiated value?”
• ‘Where are guests experiencing friction?”
• “Where are operators not getting the support they need?”

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

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

CEOs Are Learning That Fluency Cannot Be Delegated

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

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

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

The post What CEOs Say About AI in Private and What That Means To Leadership appeared first on Brian Solis.

I joined HFS Research in New York for The Services-as-Software Economy: Disrupt or Be Disrupted conference where I delivered a keynote on AI Business Transformation: Optimized + Innovative AI on behalf of ServiceNow.

In a room full of business and technology leaders leading AI investments, I asked everyone to press pause on conversations limited to use cases, automation and efficiency gains, and siloed pilots that optimize yesterday’s work. We are entering a phase of AI where the conversation has to move beyond what’s quickly become an AI status quo.

This was the provocation I wanted to leave with the room. The risk for leaders is not thinking too big, but rather too small.

Following my presentation, HFS founder and CEO Phil Fersht joined me onstage to continue the conversation, exploring what AI business reinvention means when technology is moving faster than the organizational structures, operating models, incentives, and assumptions meant to put it to work. You can watch the keynote and conversation here or scroll to the bottom. 

Yes, AI is changing how work gets done. More so, it is forcing leaders, or should, to reconsider what work should exist, how value gets created, what customers will pay for, and ultimately what kind of company they are building.

AI Is Everywhere, But Transformation Is Where Exactly…?

There is no shortage of AI activity inside enterprises. There are pilots, copilots, agents, proofs of concept, innovation teams, centers of excellence, and use cases multiplying across functions.

Great! Let’s try. Let’s experiment. Let’s learn! And, let’s fail…and fail forward and upward. Let’s also be honest about our investments. Activity is helpful and necessary, but it is not transformation.

In the research I shared onstage, overall enterprise AI maturity measured just 35 out of 100 and had actually fallen nine points year over year according to the ServiceNow AI Index.

That seems counterintuitive until you look more closely at what organizations are doing.
AI use cases may be widespread, but only 30% of organizations have integrated workflows across business functions.

A company can deploy hundreds of AI tools and still operate essentially the same way. It can automate tasks without redesigning workflows. It can make employees faster without changing how decisions happen. It can install agents without reconsidering the work those agents are being asked to perform. And none of this can be done effectively without a bold vision and governance to support it.

This is one reason AI maturity can feel as though it is moving backward even while AI usage moves forward. The technology keeps raising the ceiling while organizations keep bumping into their existing architecture.

We are becoming better at using AI before we are becoming better at being different because of AI.

Are You Using AI to Improve the Past or Invent the Future?

This was the question at the center of my presentation. Organizations naturally begin with the past. And that’s perfectly fine. Of course they’re going to look at existing processes and ask how AI can make them faster, cheaper, more efficient, and scalable. They find bottlenecks and remove them. They apply intelligence to workflows designed years, sometimes decades, before this intelligent technology existed.

This is what Dave Wright and I refer to as “iterative AI” in our new book, Infinite: How Visionary Leaders Transform Today’s Businesses into AI-Forward Companies.

The point of iterative AI however is to not just optimize past workflows, but to also evaluate whether as is, they’re the right workflows to carry forward or if workflows themselves modernize with AI at the core. Then iterative AI becomes purpose-built to solve real business challenges and improve everyday workflows. It removes routine and complex work so people and organizations can become faster, more efficient, more productive, and hopefully more valuable.

There is a second portfolio leaders must build at the same time. We call this “innovative AI.”

Innovative AI asks different questions. Instead of starting with the current process, it starts with a desired outcome. It explores entirely new products, services, experiences, workflows, and business models. It looks for opportunities that were previously impossible or impractical, and uses AI to make them possible.

One makes the existing company better and more competitive.

The other helps invent the company that comes next.

You need both.

The Services-as-Software Economy Changes the Question

For decades, much of the services business has been organized around people performing work: expertise, labor, utilization, hours, capacity, projects, and headcount. Software helped people perform that work more efficiently, but the underlying model remained largely human-centered.

AI begins to change the economics of that equation.

When intelligence can increasingly perform, coordinate, and orchestrate parts of the work itself, the question is no longer simply, how do we use AI to make our services more efficient?

The more interesting question becomes, What does the service become when software and AI can increasingly deliver the outcome?
If you only automate the existing service, you may lower its cost. If you rethink the outcome, you may create an entirely new category of value.

And this is why disruption doesn’t necessarily arrive as a better version of your existing offering. It often arrives from someone who was never emotionally, financially, or operationally attached to your definition of the market in the first place.

Become AI-Forward, Not Simply AI-Enabled

In Infinite, we introduce the idea of an AI-forward mindset as a way to reach escape velocity and break from legacy gravitational pull.

Being AI-forward means prioritizing the use of AI to explore possibilities that could not be achieved without it, and, importantly, outcomes AI could not achieve without us. AI-forward also means AI alongside humans.

Screenshot

The future is not simply autonomous.

Human judgment, imagination, empathy, ambition, creativity, values, vision, and responsibility become more important as AI becomes more capable.

This is about elevating what humans + AI can do together.

The leadership challenge, then, is to simultaneously pursue optimization and augmentation, iteration and innovation, while creating intentional space for reinvention. The problem is that most organizations are structurally biased toward automation and optimization because it’s a known approach But executives need to learn a lesson before finding out the hard way, you can’t cut your work to growth. You can’t automate your way to innovation.

Budgets reward predictable returns. KPIs measure existing performance. Teams are organized around existing products. Leaders are accountable for the quarter in front of them.

Innovation asks them to invest in something that does not yet exist. That is why the AI transformation challenge is ultimately as much about leadership and organizational design as technology. It’s just a broader conversation most aren’t having…yet. But in the end, AI Darwinism pits finite against infinite companies. ∞

The Organizations Pulling Ahead are Doing More than Buying and Bolting-On AI

The AI pacesetters studied in ServiceNow’s latest AI Index research provided another important clue.

Compared with everyone else, AI pacesetters were significantly more likely to have a clear shared AI vision, invest in AI innovation centers, take a platform approach, build the right talent mix, establish AI-specific governance, and already use agentic AI. These represent organizational capabilities.

Screenshot

Vision

Innovation

Platforms

Talent

Governance

Agents

That is what separates AI pilots and use cases from AI transformation.

The maturity model reflects that progression. Organizations move from Evaluator, where they assess and explore, through Experimenter and Expander, into Advancer, where AI becomes operationalized at scale. But the final stage, Transformer, requires something more, reimagining, innovating, transformational investment, and agentic AI orchestrated across the enterprise.

At that point, technology stops being the story and business reinvention becomes the story and the outcome.

The Coming Divide: Iteration Vs. Reinvention

One of the visuals I shared plots iterative and innovative AI against business impact over time. In the right cases, with the right approach, they create value. But they do not create the same trajectory.

An iterative mindset improves the slope of the business you already have. An innovative AI mindset creates the potential for a fundamentally different curve, what Dave Wright and I describe as true AI business reinvention in Infinite.

Another view contrasts automation with augmentation. Iterative AI solves problems and improves work; innovative AI creates game-changing use cases that reimagine work. The space between those trajectories is where disruption happens.

While an incumbent is celebrating a 20% productivity improvement, an AI-native competitor may be asking why the workflow requires ten steps at all or why the work isn’t leading to an even greater outcome.

One company asks how AI can make employees more productive, another asks what a human-agent team could accomplish that a traditional organization could never economically deliver.

One business uses AI to lower the cost of its existing service or increase efficiency, another redesigns the service around an outcome customers actually wanted all along.

This is the difference between applying AI to your business and allowing AI to challenge your assumptions about the business.

AI-Native Competitors Won’t Wait for Your Transformation Roadmap

I closed the presentation by looking at three emerging postures…AI Native, AI First, and AI Forward (3 of the 4 AI Cultures I recently studied with dear friend and former Altimeter colleague, Jeremiah Owyang.

AI-native organizations do not really need to call themselves AI native. AI is simply in their DNA, embedded through processes and workflows, with human-agent ratios already shaping how small teams can operate at scale. AI-first companies increasingly treat AI as the default way to work and solve problems. AI-forward companies deliberately bring humans and AI together, with greater emphasis on oversight, collaboration, governance, and values.

There is no single template every company should copy. But there is a competitive reality every incumbent has to confront. Your AI strategy can’t be based only on how your existing competitors are using AI. You have to go beyond the use cases everyone is talking about. You have to be user zero or customer zero. You also have to imagine the company an entrepreneur would build today if they had access to your industry knowledge, modern AI, agents, platforms, and none of your legacy assumptions.

Then ask something most incumbents wouldn’t imagine asking, What would they build that would make your current operating model irrelevant?
That is where reinvention starts.

Disrupt or Be Disrupted Is a Leadership Choice

The title of the HFS event was deliberately provocative: Disrupt or Be Disrupted. But disruption is not something leaders can simply schedule. What they can control is their response to it.

In an era of disruption, two things remain within our control: our mindset and our actions. We can use AI to make yesterday incrementally better. And we also need leaders willing to imagine businesses, services, workflows, experiences, and sources of value that could not exist before.

Companies have to be willing to think differently because AI exists. And then take action.

If you’re waiting for someone to tell you what to do, you’re on the wrong side of innovation. In the end, disruption happens to you or because of you.

AI Is Moving Faster Than Your Organization

Infinite ∞ | Mindshift | Subscribe | Keynote Speaker

The post The Biggest Risk with AI Isn’t Thinking Too Big, It’s Thinking Too Small appeared first on Brian Solis.

I’m honored to be included in The Wharton Blueprint for AI Agent Adoption, a new report from Wharton Human-AI Research and Science Says that explores one of the most important questions facing every leader right now: if AI agents are becoming so capable, why aren’t more people ready to trust them with meaningful work?
The answer isn’t just technical. It’s human.

The report makes clear that agent adoption is moving from a technology challenge to a psychological one. People may be willing to ask AI a question. But asking an agent to act on their behalf, access information, manage workflows, make decisions, or execute tasks requires something deeper than curiosity. It requires belief in the agent’s competence. It requires trust. And perhaps most importantly, it requires people to feel that they are still in control.

That’s where the future of work will be decided.

AI agents won’t transform organizations simply because they exist. They will transform organizations when leaders design them in ways that earn trust, protect human agency, and help people feel more capable, not less relevant. Adoption isn’t about convincing employees to give up tasks. It’s about helping them gain capacity, confidence, and agency in a world where work itself is being reinvented.

So yeah, AI agents are here and they can research, recommend, summarize, analyze, coordinate, and increasingly, act. AI agents can also destroy company records in one fell swoop. But the next frontier of AI adoption will not be determined by model performance alone. It will be determined by something far more human: whether people believe agents are competent enough, trustworthy enough, and safe enough to let them do real work.

The report brings together research from Thomas McKinlay, Stefano Puntoni, and Serkan Saka, along with contributions from an incredible group of Wharton faculty and industry leaders, including Kartik Hosanagar, Prasanna Tambe, Hamsa Bastani , Gérard Cachon, Lyle Ungar, Katherine Milkman, Shiri Melumad, Ethan Mollick, Chris Caldwell of Concentrix, Wade Foster of Zapier, Neil Hoyne of Google, Maria Joao Montenegro of Wolters Kluwer, Adam Seligman of Workato, and I was honored to contribute on behalf of ServiceNow.

The blueprint organizes the adoption challenge around three psychological frictions: perceived competence, trust, and delegation of control. Said or asked another way: Do I believe the agent can do the job? Can I trust it to act on my behalf? And am I comfortable giving it control?

That’s where the real work begins.

Agents Must Earn Competence Before They Earn Control

One of the most important findings is also one of the most practical: people do not necessarily want AI agents to be warm, charming, or overly friendly. They want them to be competent.

As Lyle Ungar put it, “Most people do not care if the AI feels empathetic. They care if it solves their problem.” Chris Caldwell added that customers can get frustrated with “overly polite and obedient technology that isn’t accomplishing things at speed.”

his is a critical design lesson. Too many AI experiences today are optimized to sound helpful rather than to be useful. But in business, performative helpfulness is not the same as performance. People do not adopt agents because they smile digitally. They adopt them because agents help them make better decisions, eliminate steps, execute the mundane, move faster, reduce friction, and create measurable value.

This is why I believe agents need to communicate competence in business terms. As I shared in the report, “AI agents are too often built in the image of yesterday’s workflows. If we build agents that expose their decision logic in business terms people use, we gain agentic empathy, trust built through auditable reasoning. This way, people can understand them better.”

Agentic empathy is not about making AI feel more human. It is about making AI more understandable and relatable to humans.

It means the agent can explain why it recommended an action, what data it used, what assumptions it made, what tradeoffs it considered, and where human approval belongs. In other words, make the agent’s thinking look less like a neural network output and more like explainability and auditability.

That is how competence becomes visible and ultimately trustworthy.

Trust Is Built Before, During, and After the Work

The second friction is trust. And trust, in this context, is not a feeling. It is an operating requirement.

The report shows that people trust agents more when they understand their limitations. This is counterintuitive for many companies. The instinct is to hide the weakness, polish the demo, and emphasize what the AI can do. But the science says the opposite. When people know where an AI system is likely to fail, they work with it more confidently because they know when to step in.

Trust also grows when people see proof that the agent has succeeded before. One study according to the Wharton report found that evidence of successful outcomes increased people’s belief in an AI’s future accuracy by up to 22.1%. Another found that people chose AI labeled as “learning” 55% of the time versus 43% without that label. Precision matters too. People trusted AI recommendations 12% more when numbers were precise rather than rounded. And when AI advice felt generic rather than tailored, acceptance dropped.

This is the difference between saying, “Trust me,” and showing why trust is warranted. It’s earned not given.

But trust is also fragile.

When AI makes the process too easy, people can actually trust the outcome less because they lose psychological ownership. The report notes that when AI performs all the work upfront with no user input, people spend less time engaging and feel less ownership over the result.

That should give every leader pause.

The goal is not to remove humans from the process. The goal is to redesign the process so humans and agents create value together toward meaningful outcomes. It’s the shift from an org chart to a human-to-agent work chart. Agents should narrow options, surface tradeoffs, explain assumptions, and invite input at the right moments. The best AI experiences do not make people feel replaced. They make people feel amplified.

As I told Wharton, “Giving up tasks is not the issue; employees do not want to lose their identity or give up agency. If anything, they seek empowerment.”

That may be the heart of the adoption challenge.

People are not simply resisting technology. They are protecting meaning, mastery, identity, and agency. Leaders who miss that will mistake human hesitation for stubbornness. It is not however. It is a signal.

Delegation Is the New Leadership Discipline

The third friction may be the most important for the agentic enterprise: delegation of control.

Ethan Mollick captured the issue perfectly: “The hard part of adopting AI agents is deciding what to delegate. When people do not have a clear sense of what the AI should do, what they should do, and when to step in, adoption stalls. Agentic AI works best when delegation is explicit, not implicit.”

This is where most organizations will struggle. Why? Because they have not redesigned the work. And the future of business starts with rethinking work for a world where possibilities are unlocked because they were not available at this level before.

Agents cannot succeed if they are dropped into broken workflows, organizational and data silos, unclear policies, messy data, or political operating models. They need structure. They need context. They need permissions. They need escalation paths. They need governance. They need managers.

I shared in the report, “Design agents like employees you’d like to hire and manage. Give them a job description, permissions, goals, escalation paths, and measurable outcomes. Onboard them like you would a high-performance candidate, and nurture them to thrive.”

This becomes an operating model.

Agents need roles. Humans need new roles too. Someone has to train them, supervise them, evaluate them, improve them, and know when to intervene. As I also shared, “Agents need human managers. They will not behave like Agent Smith in The Matrix and take over your entire enterprise. Human oversight, governance, and training are essential in managing and collaborating with agents.”

This is why I keep coming back to the idea that the future of work is not just human-in-the-loop. It is human-above-the-loop.

Agents can do more of the work. Humans must become better orchestrators of outcomes.

The report also shows that people delegate best when agents have moderate autonomy, not too little and not too much. Too little autonomy makes the agent feel like extra work. Too much autonomy makes people feel like they have lost control. Control concerns accounted for 26% of the decision to adopt AI in one study, and privacy concerns accounted for 31%. Personalization can increase adoption by up to 20%. Under pressure, people relied on AI recommendations 48% of the time versus 39% under lower pressure.

The lesson is clear: autonomy should be earned, not assumed.

The Bigger Lesson: Adoption Starts with a Mindshift

For leaders, the report is really about business reinvention. Yesterday’s mindsets limits AI to iterative gains. A mindset shift however can open the door to infinite possibilities.

Too many organizations are still asking old questions with new technology. How do we automate this task? How do we reduce this cost? How do we make this process faster?

Those are valid questions. And they should be explored. But let’s be clear, they are not transformational questions.

The bigger questions are: What work should no longer exist? What outcomes could we deliver differently? What new outcomes are achievable with AI? Where could agents help us create new value, not just optimize old value chains? How do we redesign workflows around intelligence, not hierarchy? How do we protect human agency while increasing human capacity?

This is where the leadership challenge becomes clear. Business leaders want ROI, especially when quick wins can be elusive. But doing more of yesterday with agents creates an AI status quo, not AI innovation. Technology leaders still have the hard work of preparing data, governance, oversight, security, accountability, and integration. And employees are not just worried about tasks. They are worried about identity, relevance, and agency.

Business reinvention is the minimum ante for agentic readiness. And an agentic enterprise is the minimum ante for becoming more efficient, productive, adaptive, and competitive.

The currency of that reinvention is vision, leadership, and trust. And that delivers a new kind of ROI, return on intelligence.

Where Leaders Should Start

1. Leaders or those who influence them, need to start by redesigning work around outcomes, not tasks. Choose meaningful workflows where agents can help people create measurable value. Do not simply automate yesterday’s process. Reimagine the flow of work from the customer, employee, and business outcome backward.

2. Make agents legible. Show their reasoning. Define their limits. Make their recommendations auditable. Give people visibility into what the agent can do, what it cannot do, where it needs approval, and how its performance improves over time.

3. Next, design for empowerment. Employees need psychological safety, training, and permission to experiment. They need to see that agents are not here to erase their value, but to help them move from task execution to judgment, creativity, orchestration, and higher-order contribution.

4. Finally, govern agents like part of the workforce. Give them job descriptions, access rights, escalation rules, performance metrics, and accountability structures. Build them into platforms and workflows, not around fragmented point solutions. The future of agents is not a thousand disconnected bots. It is intelligent orchestration across the enterprise.

There is an old saying (ok, I’m making it up): do the task and you get the job done linearly. Teach an agent to do the task and you scale the job, the learning, and the outcomes exponentially.

That is the opportunity…and it’s exponential. It’s Infinite.

The companies that earn the right to delegate meaningful work to AI agents will lead the way.

That starts with a mindshift.

From automation to augmentation.

From control to orchestration.

From AI as a tool to AI as a trusted collaborator in the reinvention of work.

The agentic enterprise is not waiting somewhere in the future. It is forming right now, one workflow, one decision, one act of trust at a time.

Infinite ∞ | Mindshift | Subscribe | Keynote Speaker

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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.