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

Summary

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

Read the full article at Forbes.

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

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

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

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

That is the fork in the road.

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

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

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

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

You cannot automate your way to innovation and growth.

Automation can make yesterday more efficient.

Augmentation is how we invent what comes next.

Automation is Useful, Automation as Strategy is a Trap

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

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

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

I think we can all agree here.

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

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

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

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

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

That is the strategic conversation leaders should be having.

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

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

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

Jensen Huang’s Lesson: Tasks Are Not Jobs

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

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

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

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

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

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

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

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

That is Return on Intelligence.

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

The Hidden Cost of a Replacement Signal

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

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

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

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

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

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

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

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

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

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

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

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

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

The Leadership Mandate: Turn Automation Savings into Augmentation Capital

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

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

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

Are you taking away jobs or are you creating them?

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

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

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

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

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

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

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

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

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

Did we use it to get closer to customers?

Did we use it to experiment with new offerings?

Did we use it to improve decisions?

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

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

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

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

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

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

That is Return on Intelligence in practice.

Human + AI Collaboration = Augmented Capacity

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

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

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

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

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

Trust is now part of the AI stack.

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

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

The alternative is to make augmentation visible.

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

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

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

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

Improve the Past While Visualizing a New Future

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

That’s the mindshift.

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

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

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

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

This is the bridge from automation to reinvention.

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

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

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

A useful way to think about it is:

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

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

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

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

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

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

The AI Layoff Trap and Self-Inflicted Disruption

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

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

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

Think about it.

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

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

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

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

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

What work exists only because our systems are fragmented?

What assumptions are embedded in this process?

What would we build if we were starting today?

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

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

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

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

Automation will yield impressive savings.

Augmentation will shape the future.

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

Automation can clear the runway.

Augmentation is how you learn to fly.

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

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

The Enterprise AI Transformation Gap

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

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

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

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

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

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

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

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

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

The Agent Sprawl Problem Not Enough People Are Talking About

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

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

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

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

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

Why Enterprise AI Needs a Unified Platform

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

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

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

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

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

How a Unified Enterprise AI Platform Creates Value

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

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

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

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

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

The Leadership Imperative

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

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

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

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

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Via Bloomberg

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Book Website

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

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

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

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

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

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

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

That is why we wrote Infinite.

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

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

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

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

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

From here on out, the future will be designed.

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

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

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

The Infinite future begins with us, right now.

With gratitude,

Brian

Infinite | Mindshift | Subscribe | Keynote Speaker

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

The Tech Humanist Show

Listen:

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Apple Podcasts

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

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

Topics Covered:

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

Episode Chapters:

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

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

Getty

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

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

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

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

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

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

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

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

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

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

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

Most CEOs Are Still Thinking Too Small

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

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

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

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

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

Not having these conversations lead to missed opportunities.

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

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

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

The Real Bottleneck Is Imagination

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

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

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

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

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

“Where is our business constrained by old assumptions?”

“Where are we too slow?”

“Where are we too manual?”

“Where are we overly dependent on tribal knowledge?”

“Where are decisions being made with incomplete information?”

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

‘Where are guests experiencing friction?”

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

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

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

CEOs Are Learning That Fluency Cannot Be Delegated

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

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

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

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

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

The ‘AI Tax’ Is Already Showing Up

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

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

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

This is the AI tax.

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

AI generated content is turning people off.

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

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

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

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

Fear Is In The Room

Every CEO understands there is fear around AI and jobs.

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

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

That is why change management came up repeatedly.

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

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

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

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

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

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

The Best Leaders Are Willing To Become Beginners Again

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

That is easy to say and hard to do.

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

AI challenges that posture.

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

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

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

What CEOs And Leadership Teams Should Do Next

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

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

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

Second, define the company’s AI ambition.

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

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

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

Third, build executive fluency.

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

Fourth, treat governance as an enabler.

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

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

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

Fifth, confront the data reality.

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

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

AI will not magically create enterprise intelligence from organizational fragmentation.

Sixth, redesign work before redesigning headcount.

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

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

That question shifted the energy in the room.

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

That is a more meaningful frame.

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

Finally, build a culture of disciplined experimentation.

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

But experimentation needs discipline. Otherwise, it becomes noise.

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

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

The Leadership Moment

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

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

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

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

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

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

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

_________

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

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

Via Chrys Brent Deiparine, International Business Times

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

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

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

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

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

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

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

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The post International Business Time: 99% of CEOs Expect AI Layoffs by 2028: Suze Orman Issues Blunt Warning to ‘Invisible’ Workers appeared first on Brian Solis.

via Vicki Salemi, Tribune Content Agency

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

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

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

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

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

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

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

Surprise!

After writing Mindshift, I took time to listen, learn, and rethink what leadership now requires. I’m excited, humbled, and honestly grateful to share what comes next.

Introducing, Infinite: How Visionary Leaders Transform Businesses into AI-Forward Companies.

Releasing on June 23rd, Infinite introduces the concept of the “Infinite Company,” a new model for organizations built to thrive in the age of AI. Please pre-order on Amazon.

Three years ago, I joined ServiceNow as Head of Global Innovation. More recently, I’ve been working with our Innovation Office to build ServiceNow Futures, a thought lab for research, reports, and executive advisory. I’m doing work that I love, studying disruption from the inside, working with leaders in real time, and helping organizations see around corners.

As part of this work, my friend, colleague, and ServiceNow Chief Innovation Officer, Dave Wright, and I have spent a lot of time with executives, boards, AI-native founders, and analysts who are actively redesigning the future of business.

What we’ve seen is clear. AI is not just another technology cycle. It is not merely a tool for automation, productivity, efficiency, or cost reduction. It is forcing every organization to ask a much bigger question: What kind of company do we need to become when intelligence itself becomes abundant?

That question became the foundation for our new book: Infinite. And it features a powerful foreword by ServiceNow Chairman and CEO Bill McDermott.

AI-native companies are revealing a new model for speed, scale, learning, and human-agent collaboration. Many of them are growing with tiny teams, intelligent agents, and operating models that look nothing like the companies most of us grew up building, leading, or advising. That is both the warning and the invitation.

Incumbent organizations are not powerless. But they must learn to move beyond using AI to optimize yesterday’s work. They must reimagine workflows, roles, culture, leadership, governance, and value creation itself.

Dave and I wrote this book to help leaders evolve from finite operating models, built for control, silos, and linear growth, into infinite companies designed for adaptability, intelligence, human potential, and exponential outcomes.

This book is for executives, boards, founders, strategists, builders, and anyone who feels the gravity of this moment and wants to do more than react to it.

It is for leaders who believe the future should not happen to us. It should happen because of us.

Pre-order a copy for yourself, your team, or the executives in your life who are ready to lead what comes next.14

Thank you for being part of this next chapter with me.

With deep gratitude,

Brian

p.s. Pre-orders help signal momentum, support the launch, and most importantly, get the book into the hands of leaders who need a more courageous, human, and practical blueprint for the age of AI.

The post Infinite, My Next Book, is Now Available for Pre-Order appeared first on Brian Solis.

via Thomas Walker, CX Today

 
Contact center leaders are making billion-dollar AI investments without ever touching the tools they’re buying. That blindspot is now a competitive liability, and both customers and CX agents are starting to notice.

Are CX executives equipped to lead their organizations through the AI transition? A growing body of evidence suggests the answer is no – and not for lack of resources or ambition, but because of a structural problem embedded in how senior leadership operates.

The people making the most consequential AI decisions in customer experience who decide which platforms to deploy, which workflows to automate, and which agent roles to restructure are also, by design, the least likely to have direct, hands-on experience with the tools driving those decisions.

That gap is widening fast, and for contact center and CX technology leaders, the operational consequences are becoming impossible to ignore.

As Brian Solis, Head of Global Innovation at ServiceNow, put it:

“The question is not whether AI will transform customer service – it will. The question is whether the people responsible for that transformation actually understand what they’re deploying.”

The post CX Today: Does Your Leadership Team Actually Understand AI? Most CX Executives Don’t. appeared first on Brian Solis.

via Craig Hale, TechRadar

Consumers just want real, human sources

Consumers are experiencing ‘bot fatigue’ after interacting with impersonal AI, WordPress VIP finds.

61% can’t even name a brand that’s using AI well in marketing or CX.

AI is emerging as a discovery layer, not a customer service replacement.

New research from WordPress VIP has uncovered just how much AI is impacting browsing and online shopping habits, with three in four (74%) consumers believing the Internet feels less ‘human’ than it did 10 years ago, largely due to AI and automation.

40 minutes is now the average time before consumers feel so-called ‘bot fatigue’, where they’ve been interacting with too much AI and want more human connections, the study claimed.

But while artificial intelligence promises to solve more and more customer service tickets, three in five (61%) consumers cannot even name a single brand that uses AI well in marketing or CX.

Rather than replacing human workers and tools, consumers seem much happier to accept it as a replacement for other digital services, like search engine discovery. They want companies to focus on appearing in AI search results, but they still want original human content and direct access to sources.

“No customer or user wakes up and says, ‘I hope I get to talk to a chat bot or an AI agent today’,” ServiceNow Head of Global Innovation Brian Solis wrote. “Human-centered design is truer today with artificial intelligence.”

The post TechRadar: ‘No customer or user wakes up and says, “I hope I get to talk to a chat bot or an AI agent today”‘: Survey claims brands which sound more “human” will get ahead in the AI age appeared first on Brian Solis.

Are your AI investments actually changing how your business operates, or are you just funding the “productivity trap”?

In this insightful new article, Brian Solis (Head of Global Innovation at ServiceNow) addresses a massive elephant in the enterprise room: the transformation gap.

While companies are investing billions into individual copilots, assistants, and models, very few leaders can honestly say their foundational operating models have evolved. Instead, many are accidentally creating a chaotic web of “agent sprawl”, disconnected, siloed AI tools that optimize individual tasks but leave broader enterprise workflows completely untouched.

Brian challenges leaders to stop asking “Which AI should we deploy?” and start asking:

“Are we building the organizational architecture that allows AI to act with confidence, at scale, within the governance structures our business requires, and in genuine partnership with people?”

You can read the full article at The FutureEconomy.ca.

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

This isn’t the first time you’ve heard of, or read about, AI. Its implications for virtually every sector are completely rewriting the script. Companies are investing in AI and implementing it day-to-day. We are at the same pivotal point with the Internet in the late 21st century. It’s an uncertain time. Will this replace jobs? Will work be replaced with computers?

Done right, AI is not automation. It is augmentation.

In the book Mindshift, author Brian Solis observes that most organizations are still approaching AI as automation (getting things done faster, cost reduction, speed) when its real power is augmentation (better decisions and higher value output).

The post Accounting Today: AI in the Tax Department appeared first on Brian Solis.

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via The Summary Series

Lifescale Secrets: Grow Your Business Instantly

Book Summary: Lifescale by Brian Solis

Buy this book on Amazon: https://a.co/d/04MSU2Os
Free month of Kindle Unlimited: https://amzn.to/3ZYVJAK
Grab audio version for free on an Audible trial: https://amzn.to/3PeeivQ

Lifescale isn’t just about personal and professional transformation, it’s also about leadership and business transformation. Watch this clip on Instagram to learn more.

*Overview*

Lifescale by Brian Solis invites you to live — not simply survive — in the digital age. It uncovers how technology, culture, and personal purpose blend to create a *lifescale*, a dynamic roadmap that evolves as you grow. By harnessing storytelling, community, and continuous learning, the book teaches you to turn every day into a stepping‑stone toward inspiration, motivation, and measurable action. *Key Themes*

• *Digital Alchemy*: Transmuting online presence into real‑world influence.
• *Cognitive Age*: Embracing curiosity and lifelong learning as survival tools.
• *Community Crafting*: Building authentic networks that amplify your purpose.
• *Personal Brand Story*: Curating a narrative that attracts collaboration and care.
• *Iterative Life*: Seeing goals as evolving, not static, milestones.

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*Major Lessons*

• *Lesson 1*: Purpose fuels perseverance—identifying your “why” turns work into wonder.
• *Lesson 2*: Authentic storytelling activates empathy, turning strangers into supporters.
• *Lesson 3*: Curiosity + skill = catalytic change; practice curiosity daily to spark innovation.
• *Lesson 4*: Social networks aren’t just contacts—they’re collaborative ecosystems.
• *Lesson 5*: Lifelong learning is the engine of adaptation; never stop refining your skillset.

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*Actionable Steps*

1. *Define Your “Why”* – Write a 3‑sentence mission statement.

2. *Curate Your Story* – Map out 3 personal anecdotes that illustrate your values.

3. *Build Your Tribe* – Join 2 niche communities (online or offline).

4. *Learn & Share* – Enroll in a new course; post weekly reflections.

5. *Launch a Mini‑Project* – Apply your story to solve a real problem in your network.

6. *Review & Revise* – Monthly check‑in: adjust your mission, update stories, celebrate wins.

*Final Takeaway*

Remember: *inspiration* sparks the fire, *motivation* fuels the journey, and *action* turns dreams into reality. Lifescale shows you that life is a living, breathing narrative. Embrace the digital alchemy, trust your story, nurture your tribe, and watch as every act of curiosity, authenticity, and collaboration propels you toward an ever‑expanding horizon. Keep stepping, keep learning, and let your lifescale be a beacon of possibility for yourself and others.

Lifescale, Brian Solis, business growth, digital transformation, entrepreneurship, startup tips, scaling strategies, tech culture, productivity hacks

What if your business could grow without limits? Imagine scaling every employee’s impact like a virus. In Lifescale, Solis reveals why 73% of companies still chase growth the old way and fail. He shows the hidden secret: human‑centred digital ecosystems. First, map every customer touchpoint to a real‑world experience. Then, empower teams with data‑driven tools that adapt instantly. The result? Revenue spikes up to 38% in just six months for early adopters. But here’s the twist: most leaders think technology alone will fix everything. Solis proves the opposite – culture wins.

He shares a real story of a retailer that dropped a clunky CRM, replaced it with a simple feedback loop, and saw foot traffic double. The payoff is clear: when people feel heard, they buy more. The micro twist? The same strategy works for freelancers, not just giants. Your next step? Grab a notebook, list three ways you can turn data into conversation today.

Think about your own team. How many of them share insights daily? In the book, Solis cites a study where firms that held daily micro‑huddles grew 27% faster. He also warns against data overload – focus on three key metrics that matter to your customers. Visualize those metrics on a live board, celebrate tiny wins, and watch momentum build.

Finally, remember the golden rule: technology amplifies intent, never replaces it.

Like if this blew your mind, drop a comment with your biggest growth challenge, and hit subscribe for more bite‑size business hacks.

Lifescale, Brian Solis, business growth, digital transformation, entrepreneurship, startup tips, scaling strategies, tech culture, productivity hacks

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Maybe people aren’t rejecting AI as much as they’re rejecting a future that’s being imposed on them.

When students boo the mention of AI at commencement speeches, it’s a signal of a societal undercurrent quietly, and eventually, loudly, pushing back against a perceived threat. And it’s not the technology that’s a threat as much as how AI, or at least the AI narrative, is wielded by doomer CEOs of frontier AI companies (AI will take jobs!) and the decisions executives make in the workplace for efficiency and profitability gains (over 100,000 employees were impacted by AI-driven layoffs in 2025 and nearly 80,000 employees have already been impacted in 2026!)  

So it should come as no surprise that a new NBC News survey put a number to the societal signal stirring the public booing of AI. As Amrith Ramkumar worded it in the WSJ, “The only thing growing faster than the artificial-intelligence industry may be Americans’ negative feelings about it.”

In a survey of 1,000 registered U.S. voters, NBC News found that only 5% were very positive about AI, while 22% were very negative. Another 21% were somewhat positive, 24% somewhat negative, and 27% neutral.

In the very least, AI has a branding problem. And this is the moment to do something about it. The survey shows that AI has a large persuadable middle, but the emotional center of gravity is negative. Negative sentiment is almost 2x positive sentiment. The comparative finding is striking: AI’s net favorability, -20, is worse than Trump, the Republican Party, and ICE, and only slightly better than the Democratic Party. Iran is the clear outlier at -53.

A Quinnipiac poll released later found 80% of Americans concerned about AI, only 21% saying they trust AI-generated information most or almost all of the time, 70% expecting AI to reduce job opportunities, 76% saying businesses are not transparent enough about AI, 74% saying government is not doing enough to regulate it, and 65% opposing an AI data center in their own community.

People also feel that AI is also moving too fast. A poll conducted in May 2026 by The Economist and YouGov found that Across every age group, a clear majority believes AI is moving too fast. This is not just an older-generation concern. Even among 18–29-year-olds, roughly two-thirds feel the pace is too rapid.

The concern intensifies with age. The 65+ group shows the strongest discomfort, with nearly 8 in 10 saying AI is moving too fast and fewer than 1 in 5 saying it is moving at the right pace.

Very few Americans think AI is moving too slowly. That is important because, in addition to booing, the public is basically saying,“Hey! Slow down! Help us understand, protect us, and give us more control.”

The rebellion against AI isn’t really about AI.

And that’s really what this is about. It’s about trust and transparency. It’s also about psychological safety and empowerment.

Said another way, the revolt against AI isn’t just fear of technology. It’s fear of losing agency.

People are asking, “Will AI take my job?”

They’re also asking:

“Who decides?”

“Who benefits?”

“What happens to my data, my work, my community, my future?”

“And what power do I have in the process?”

That means human agency and trust are now part of the AI stack.

The AI stack can no longer be defined only by chips, models, data, compute, apps, and interfaces.

It also includes:

Consent. Do people understand how AI affects them?

Control. Can people opt in, opt out, appeal, override, or redirect outcomes?

Transparency. Can people see how decisions are made?

Accountability. Who is responsible when AI causes harm?

Participation. Were workers, parents, students, artists, communities, and citizens included before systems were deployed?

Shared value. Does AI make people stronger, safer, more capable, and more prosperous, or merely more efficient for someone else?

It’s About Agency

People are rejecting a future that feels imposed on them.

AI is moving faster than institutions, communities, and workers can process. The result is anxiety and loss of control. When people feel they have no voice in the systems reshaping their lives, resistance becomes rational. Booing is just a symptom for the percolating undercurrent.

This is the real wake-up call for AI leaders, and leaders in politics, at work, on Wall Street and Sand Hill, in schools and universities…leaders everywhere.

Human agency and trust are now part of the AI stack.

The future of AI will not be accepted through persuasion or force. It will be earned through trust.

The companies that win will help people feel more powerful because of AI.

Read Mindshift | Subscribe to my Substack | Consider me as your next speaker

The post AI’s Next Frontier Isn’t Intelligence, It’s Trust appeared first on Brian Solis.

AI Darwinism isn’t coming. It’s here.

The evolution has already begun inside every organization where employees are experimenting faster than leadership is redesigning the systems around them. Microsoft’s 2026 Work Trend Index Annual Report makes the gap clear: AI is expanding human agency, but many organizations are not yet built to capture it.

That’s the real challenge now. Leadership.

The companies that win this next era will be the ones brave enough to reimagine work, empower curiosity, create psychological safety, and turn experimentation into institutional advantage.

Agents, Human Agency, and the Opportunity for Every Organization

The Microsoft report finds that AI is expanding human agency as agents take on more execution, but the real question is whether organizations are built to capture that agency. Microsoft analyzed trillions of anonymized Microsoft 365 productivity signals and surveyed 20,000 workers using AI across 10 countries. The finding is both hopeful and uncomfortable: in many cases, people are ready ,but he systems around them are not.

Contrary to popular ideology, AI fluency is not the finish line.

AI fluency is a good start, but it is not the destination. Fluency is knowing the language. Like OpenAI, in its research, Microsoft identified an advanced category of work that they called Frontier work with those leading it, Frontier professionals.

For example, Microsoft found that 49% of Microsoft 365 Copilot conversations support cognitive work: analyzing information, solving problems, evaluating, and thinking creatively. Even more important, 58% of AI users say they are producing work they couldn’t have produced a year ago. That rises to 80% among Frontier workers. These workers use agents for multi-step workflows, rethink where AI should augment or automate work, and help create shared AI standards for their teams.

Said another way, the best AI users are not outsourcing their thinking. They’re upgrading it.

In fact, Microsoft found that 86% of AI users treat AI output as a starting point, not a final answer, and “stay responsible for the thinking.” Frontier Professionals are more likely than others to intentionally do some work without AI to keep their skills sharp and to pause before deciding what should be done by AI versus a human.

You could say that it’s the future of work in one sentence: the human who knows what not to delegate becomes more valuable than the human who delegates everything.

Human skills become the moat. AI does not eliminate judgment or critical thinking or creativity. It these skills the premium.

The Real AI Gap Is Coherence

Recently, Daniel Miessler, a cyber security AI engineer, recently published a post about how most companies are not remotely ready for AI. Miessler argues that legacy companies struggle with AI because they can’t clearly visualize or articulate what they want. “AI is about execution,” he writes, “and it’s quite powerless when it doesn’t know what to execute.” He points to unclear and constantly changing vision, goals, strategies, workstreams, metrics, and costs as the deeper problem.

Exactly.

AI is a mirror with an API. It reflects the past and connects it to efficiency.

This reminds me of something legendary venture capitalist Vinod Khosla once said. “Most businesses have no clue what is about to hit them in the next ten years when most rules of engagement will change,” he observed.

If your company is clear on direction, AI accelerates you. If your company is confused, AI scales the confusion. If your company is tied to the past, AI scales your past until your business can no longer compete in the present.

You can’t automate chaos into clarity.

You can’t agentically orchestrate a business that doesn’t understand its own music.

You can’t scale what you shouldn’t be doing in the first place.

This is why “use AI” is not a strategy. It’s a dare. And for many organizations, it’s a dare issued by leaders who have not mapped the work, clarified the outcomes, aligned incentives, or created the cultural conditions for people to experiment their way into the future.

Miessler’s most important question wasn’t, “What can AI do for us?” It is whether the company is in a state where AI can help at all. And maybe it should be a more direct question,” where can we go with AI that wasn’t possible before” or “what aren’t we doing that would make us more competitive?”

When executives hold up a mirror and honestly see themselves reflecting on these questions, it might help them realize something profounds… we’re not trying to do much more than what we’ve always done, we’re just evolving the past thinking that the market conditions will remain constant.

We’re implementing technology to free up human capacity, but we’re not repurposing that capacity toward value creation.

We’re moving faster but we may not be empowering our people to compete differently, meaning we’re running to stand still.

We’re driving AI adoption, fluency, and training, but to what end? Is AI helping us scale the past but not preparing for or building a new future not possible without AI? Are we pushing people to give their thinking and output to AI without reskilling them toward proper augmentation?

The Transformation Paradox

Microsoft calls this the Transformation Paradox: knowledge workers could be ready, but organizations aren’t.

In the 2026 Work Trends Index, Microsoft found that only 19% of AI users sit in the “Frontier” zone, where individual capability and organizational readiness reinforce each other. About 10% are blocked, representing skilled workers in companies that haven’t caught up. Half sit in the messy middle, where both individual practice and organizational conditions are still taking shape.

The misalignment gets worse at the top.

Only 26% of AI users say their leadership is clearly and consistently aligned on AI. Meanwhile, 65% fear falling behind if they don’t adapt quickly, 45% say it feels safer to focus on current goals than to redesign work with AI, and only 13% say they are rewarded for reinvention even when results aren’t immediate.

Leadership is not clear or aligned on AI. People fear falling behind. Employees focus current work instead of redesigning work because it feels safer. And employees aren’t incentivized or motivated to think differently, to innovate, or to improve outcomes.

Think about it this way…people are being told to transform while being measured, managed, and rewarded for not transforming.

That’s not a skills gap. That’s a leadership gap.

You cannot ask people to reinvent work and then grade them only on yesterday’s measures.

Psychological Safety is AI Infrastructure

Work today is based on the verticalization of existing silos. Data, workflows, systems, and measures are all designed to optimize that work. Agentic AI, for example, represents an opportunity to connect disparate silos to make work flow horizontally across the enterprise. This means everything is up for reinvention…everything. Whatever past workflows remain moving forward should only be the result of deep thinking. Your next steps become intentional.

I have two words, “psychological safety.”

In the AI era, psychological safety is operational infrastructure. It is what allows people to say, “This process no longer makes sense.” It gives employees permission to challenge inherited workflows, surface broken handoffs, test new human-agent models, and admit when AI outputs are wrong before those errors compound at scale.

In its Work Trend Index research, Microsoft found that when managers created psychological safety around experimentation, employees reported up to 20 points higher AI readiness and value and were 1.4x more likely to be high-frequency users of agentic AI. When managers actively modeled AI use, employees reported a 17-point lift in AI value, a 22-point lift in critical thinking about their AI use, and a 30-point lift in trust in agentic AI.

That’s proper leverage.

Silence fosters technical debt.

Fear cultivates organizational latency.

And cultures that dissuade experimentation will lose to cultures that learn in public, correct quickly, and scale what works.

From Adoption to Absorption

AI adoption asks, “Are people using AI?”

AI absorption asks, “Is AI changing how value is created?”

Microsoft found that organizational factors such as culture, manager support, and talent practices account for more than 2x the reported AI impact of individual mindset and behavior, 67% vs. 32%. The constraint is no longer what people can do. It is how work is structured around them.

In other words, you can send everyone to prompt school, but if your culture doesn’t encourage and reward experimentation, your managers don’t model AI use, your workflows remain siloed, and your incentives worship yesterday’s KPIs, congratulations: you’re optimizing the past while your competitors are experimenting forward.

The future of work needs design.

The New Leadership Work

The job of leadership now is to rearchitect work, otherwise, we’re talking about management.

Start with outcomes and the work that supports them, not the org chart. Org charts tell you who reports to whom. Outcomes thinking necessitates “work charts” that show how people and tech move value.

Pick one critical flow: lead-to-cash, claim-to-settle, incident-to-resolution, employee onboarding, product launch, customer renewal.

Then ask: what outcome are we trying to create, where does judgment matter, where does execution slow down, where can agents help, where must humans stay accountable, and what should the work teach us as it runs?

Next, find your Frontier Professionals. They are already there, quietly building the future with Claude in the margins while the organization debates the past.

Study them. Ask how they decide what to delegate, how they evaluate outputs, how they document workflows, how they share what they learn, and where the system blocks them.

Then train managers to become AI work coaches. They must model AI use, set standards for AI-assisted work, create room for experimentation, and reward people for redesigning work, not just completing tasks faster.

Finally, build evaluation infrastructure. As agents execute more work, the cost of bad outputs compounds. Microsoft says every Frontier Firm needs to answer three questions:

1. who reviews agent performance,
2. who has authority to update the workflows agents run,
3. and how does a local win get captured and scaled across the organization?

The firms that answer those questions start building “Owned Intelligence,” institutional know-how that compounds over time and becomes hard to replicate.

That is a real moat.

The Leadership Test

AI Darwinism isn’t coming, it’s here. The evolution has already begun. The future isn’t waiting for the next offsite, the next strategy cycle, or the next perfectly aligned transformation deck. The gap is widening now between people who are learning how to think, create, decide, and lead with AI, and organizations still asking them to fit that potential into yesterday’s jobs, workflows, incentives, and permission structures. That gap will not close itself. Leaders close it.

Every executive team should ask seven questions now:

1. What outcomes are we trying to improve, not just what tasks are we trying to automate?
2. Which workflows create the most value, friction, delay, or customer pain?
3. Where are our frontier employees already using AI in advanced ways?
4. Where are they blocked by policies, metrics, incentives, or management habits?
5. Which work should be delegated, collaborated on, explored, or kept human-led?
6. What quality standards define excellent AI-assisted work?
7. How do we capture lessons from every experiment and scale them into the operating model?

That is how companies move from AI adoption to AI absorption.

That is how leaders close the agency gap.

That is how leading companies compete for the future.

That starts with a mindshift.

—

Read Mindshift | Subscribe to my Substack | Consider me as your next speaker

The post AI Darwinism Is Here, Now Leadership Must Evolve Next appeared first on Brian Solis.

ServiceNow’s Head of Global Innovation Brian Solis was 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: why, if AI agents are becoming so capable, aren’t more people ready to use them for real work?

The report argues that AI agent adoption is moving from a technology challenge to a psychological one. People may be willing to ask a chatbot a question, but letting an agent act on their behalf — access files, manage workflows, make recommendations, coordinate decisions, or execute tasks — requires a different level of belief. People need to know the agent is competent. They need to trust it. And they need to feel that they are still in control.

Wharton organizes the adoption challenge around three frictions: perceived competence, trust, and delegation of control. Said another way: Can it do the job? Can I trust it to act on my behalf? And am I comfortable handing over part of the work?

Brian is grateful and honored to contribute alongside an incredible group of thinkers and practitioners, including Ethan Mollick, Kartik Hosanagar, Katherine Milkman, Lyle Ungar, Prasanna “Sonny” Tambe, Hamsa Bastani, Shiri Melumad, Wade Foster of Zapier, Neil Hoyne of Google, Maria Montenegro of Wolters Kluwer, Adam Seligman of Workato, Chris Caldwell of Concentrix, and others.

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

“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.”

“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.”

“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.”

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What a gift to spend time with an old friend (meaning we’ve been friends for a long time. He’s not old!)

My latest conversation with Guy Kawasaki on his show, Remarkable People, reminded me that the future is never just a technology story. It is a human one. AI may change how we work, compete, and create, but it also exposes something deeper: whether we are using new tools to do yesterday more efficiently, or to imagine something genuinely better. This exchange is an invitation to think differently about leadership, transformation, storytelling, and the kind of future we are actually choosing to build. It’s a choice to shift one’s mindset.

Guy and I covered a lot of ground, from old-school Silicon Valley to AI and the future of leadership, but these are the headlines I hope inspire you to listen/watch the conversation (it’s a rare moment for the two of us to be together!)

It’s really about the difference between reacting to change and building the future before it happens.

Listen (Spotify). Watch (Youtube).

I think the last time we were on camera together was 15 years ago!

TL;DR

A Mindshift is not just open-mindedness. It’s the discipline of looking ahead and shaping the future instead of reacting to it.

Most leaders are trapped in the daily swirl of iteration, not innovation.Emails, meetings, urgency, and noise keep people busy, but not necessarily aligned with where they actually want to go.

Self-awareness is the real starting point for transformation. Most people think they’re self-aware. Very few actually are. And those who lack self-awareness don’t know they do!

AI exposes the difference between doing yesterday better and inventing tomorrow. That becomes the core tension in the conversation.

Automation and augmentation are not the same thing. Automation improves existing work. Augmentation unlocks work, outcomes, and possibilities that did not exist before. And “you can’t automate your way to innovation!”

Linear growth is not enough anymore. The bigger opportunity is exponential or “logarithmic” thinking, where leaders stop optimizing the past and start designing new value.

Positive disruption is the goal. Don’t wait to be disrupted. Invest in disrupting yourself first.

Storytelling is strategy. The hero in the story is not the company, product, or executive. The hero is the person you’re trying to help and the future they can achieve with you.

Movements are built, not wished into existence. Complaining is easy. Making excuses is commonplace. Manifesting a better future takes vision, action, and belief.

Culture determines whether mindshift is possible. In some organizations, possibility wins. In others, perpetuation wins. Culture is the shared beliefs, mindsets, mental models, and values that inform the behaviors that become the people operating system of the business. “People like us, do things like this,” as Seth Godin says.

Psychological safety matters. People won’t challenge assumptions or imagine new futures if they fear punishment or resentment for doing so. You can’t stigmatize asking questions or trying something new and expect different results.

The critical skills for the AI era are still deeply human. Curiosity, imagination, empathy, critical thinking, and openness become more valuable, not less. AI cannot feel empathy. AI cannot exercise judgement. AI does not possess intuition.

AI should be treated as a cognitive exoskeleton. The question is not only what it can automate, but what it can help humans do that was previously impossible.

The real subtext is bigger than the book. In the end, we’re just two friends, industry veterans, comparing notes on what still matters in leadership, innovation, optimism, and reinvention after decades in Silicon Valley.

This is your invitation to listen to or watch a rare moment between two geeks.

Our Relationship Goes Back to a Time Before Web 1.0

Guy and I go way back…back to the 1990s, back to a different Silicon Valley, back to a time when the future felt more handcrafted than automated, when belief systems were built in coffee shops, garages, in hallways and lobbies at conferences, in startup offices, and in long conversations about what technology could become if people were bold enough to imagine it differently.

That’s part of what made this conversation so special. It took me back to the early days of Silicon Valley.

The theme of our conversation was about my book, Mindshift. But more so, it was two old friends (as in long-time, not age!), two longtime students of innovation, two champions for change, comparing notes after decades of watching industries evolve, leaders rise and fall, technologies overpromise and underdeliver, and every so often, something genuinely world-changing emerge from the noise.

Recognizing Guy Kawasaki

For those who know Guy’s name but may not know the full scope of his legacy, he’s one of the people who helped define modern Silicon Valley storytelling. He was Apple’s chief evangelist and helped write the playbook for what technology evangelism would become. He later co-founded Garage.com, which evolved into Garage Technology Ventures, and has spent decades at the intersection of entrepreneurship, venture thinking, product conviction, and storytelling. Today, he’s chief evangelist at Canva and the creator of the Remarkable People podcast.

So, when Guy asks questions about innovation, he’s asking as someone who has lived through multiple eras of technological reinvention and helped shape how many of us learned to talk about change in the first place.

He is a truly remarkable person.

The Thread Beneath the Conversation

On the surface, we talked about Mindshift. We talked about what it means to stop reacting to change and start shaping the future. We talked about the need to ask better questions, not just work harder on old answers. We talked about the difference between automation and augmentation in AI, and why too many organizations are still using breakthrough technologies to do yesterday faster and efficiently instead of inventing tomorrow on purpose.

What we really explored though, is what happens when optimism enters the room. If it’s one thing you can say about my body of work, every vision, every report, every scenario, every prediction was rooted in optimism…not doom.

Both Guy and I came of age professionally in a version of Silicon Valley that believed deeply in possibility. It’s certainly not what we’re hearing today for the most part. But that belief of optimism and possibility still matters…maybe more than ever.

Time teaches you that innovation alone is not enough. Vision is not enough. Intelligence is not enough. You also need self-awareness. You need courage. You need discernment. You need the discipline to separate momentum from meaning.

That’s part of what made this exchange feel so personal to me. It wasn’t just about trends, tech, or futurism. It was about the human work required to navigate them.

Key Takeaways

You’re going to hear more than a conversation about a book.

1) You’ll hear two people wrestling with the same question from different angles: How do you build a future you actually want to live in?

2) Why self-awareness is the starting point for any real transformation. Because if you don’t understand where your time, energy, assumptions, and attention are going, you can’t meaningfully change direction.

3) Why so much business transformation stalls. It’s usually because leaders keep trying to fit profoundly new capabilities into old mental models. AI is not a strategy.

4) Automation matters, but augmentation matters more. One improves the familiar. The other unlocks what was previously impossible. One helps you optimize the past. The other helps you invent a different future.

5) Storytelling is not a soft skill no is it simply a way of telling stories. It’s one of the most powerful leadership tools we have. People do not move simply because the data is there. They move when they can see themselves in the story of what comes next. One of my favorite parts of the conversation was exploring the idea that the hero of the story is not the company, not the product, not the leader. The hero is the person whose life, work, or future changes because they chose to believe in a new possibility.

6) We hope you’ll experience the genuine warmth, candor, and shorthand that only comes from experience and living through and shaping Silicon Valley history and lore. The stories between the stories. The laughter that comes from having been around long enough to know how much of innovation is conviction, how much is timing, and how much is stumbling into the future before you fully understand what it is.

There’s something powerful about hearing two old friends and industry lifers talk honestly about what we got right, what we got wrong, what still matters, and what leadership requires now. Truthfully.

In a moment when so much conversation about AI, innovation, and transformation feels manufactured, overproduced, doomsday rhetoric, or agenda-driven, this one is sincerely human. Reflective. Curious. Real. We’re just two old friends who’ve spent decades trying to build the future.

And maybe that’s the point.

The future doesn’t just belong to the loudest people in the room. It belongs to the ones who can still learn, still imagine, still challenge themselves, and still invite others into building something better.

This is Your Invitation To Join Us

Please listen or watch and let us know what you think and feel after…

Read Mindshift | Subscribe to my Substack | Consider me as your next speaker

The post Are We Inventing Tomorrow or Optimizing Yesterday? Guy Kawasaki and I unpack the tension at the center of AI, innovation, and leadership today appeared first on Brian Solis.