Category Archives: Leadership

Why You Need to Leverage Shared Values in Change Leadership

GUEST POST from Greg Satell

When Lou Gerstner took over at IBM in 1993, the century-old tech giant was on its knees. Many thought it should be broken up into smaller, more focused companies. Others had different ideas. So at Gerster’s first press conference, people were curious about his strategy and disappointed when he failed to deliver one.

“The last thing IBM needs right now as a vision,” he said. What he meant was that IBM’s culture was broken. “Culture isn’t just one aspect of the game,” he would later write. “It is the game. What does the culture reward and punish – individual achievement or team play, risk taking or consensus building?”

What Gerstner saw was that IBM had lost sight of the values that had made it successful in the first place. He wasn’t “disrupting.” He was making IBM culture safe to innovate again and, by doing that, he achieved one of the most remarkable turnarounds in corporate history. If you want to achieve truly radical change, you need to start with shared values.

Making The Shift From Differentiating Values To Shared Values

IBM wasn’t Gerstner’s first stint leading a company. He’s been President at American Express and CEO at RJR Nabisco, both of which were very different from technological companies. Yet Gerstner didn’t focus on how his experiences were different, but on how they were the same—each of these businesses have to serve the customer.

“Lou refocused us all on customers and listening to what they wanted and he did it by example,” Irving Wladawsky-Berger, one of Gerstner’s chief lieutenants would later tell me. “We started listening to customers more because he listened to customers.” It was upon that simple principle that he changed the course of IBM’s future.

In a similar vein, when Nelson Mandela wanted to create a new future for South Africa, he organized a Congress of the People, a multi-racial gathering which produced a statement of shared values that came to be known as the Freedom Charter, which is still revered even today. He would later say it would have been very different if his organization, the ANC, had written it by themselves, but it wouldn’t have been nearly as powerful

When we’re passionate about an idea, we want to show how it’s different. We want to explain all its beautiful complexity and nuance, so that people can share our passion and fervor. That’s almost always a mistake. The first step to creating truly transformational change is to anchor it in what people already know and feel comfortable with.

Creating Safety Around The Change Conversation

When an enterprise is in crisis, one of the first things that often gets cut is investments in the future. So when Gerstner scheduled his first non-headquarters visit at IBM to the firm’s legendary research facility at Yorktown Heights, everybody there got nervous. Many expected there to be deep cuts and, possibly, that the entire facility would be shut down.

Actually, quite the opposite. “I saw the pain of IBM’s problems on their faces,” Gerstner remembered. “I talked about how proud I was to be at IBM. I underscored the importance of research to IBM’s future.” It was a wise move. Although few knew it at the time, scientists at IBM had just made a major breakthrough that made quantum computing possible and a few years later the company’s Deep Blue supercomputer would beat Garry Kasparov at chess.

Many change management schemes advise to create a “sense of urgency” and creating a “burning platform” atmosphere. Yet Gerstner understood that employees were perfectly aware of how dire the situation was. What they needed wasn’t more fear, but to see a path forward. Terrified people don’t make good decisions. They’re also more likely to head for the exit than to work for the future.

Don’t get me wrong, you don’t want to sugarcoat things. You need to be frank, honest and paint a clear picture. Gerstner made it plain that day that there would be changes. Yet by rooting his message in shared values, he was able to create a sense of safety around the change conversation. The scientists were able to see that they could, in fact, be heroes in the story of IBM’s future. As it turned out, they would be.

Creating A Dilemma Rather Than A Conflict

Once you start being explicit about your values you will inevitably find that not everyone shares them and that was certainly true at IBM. For example, Wladawsky-Berger told me that “IBM had always valued competitiveness, but we had started to compete with each other internally rather than working together to beat the competition. Lou put a stop to that and even let go of some senior executives who were known for infighting.”

A simple truth is that whenever we set out to make a significant impact, there will always be those who will work to undermine what we are trying to achieve in ways that are dishonest, underhanded and deceptive. Yet when that happens we need to be careful not to get sucked into a conflict, which will likely take us off course and discredit what we’re trying to achieve. Instead, we need to learn to design a dilemma.

Dilemma actions have been used for at least a century—famous examples include Gandhi’s Salt March, King’s Birmingham Campaign and Alice Paul’s Silent Sentinels—but more recently codified by the global activist, Srdja Popović. They are just as effective in an organizational context, using an opponent’s resistance against them.

One of the great things about dilemma actions is that you approach them exactly the same way you approach building allies—by identifying a shared purpose. Once you do that, you can design a constructive act rooted in that shared purpose that advances your agenda. That forces your opponent to make a choice: they can either disrupt the act and violate the shared value or they can let it go forward and allow change to proceed.

For example, I was once running a transformation project that was being impeded by a Sales Director hogging accounts. Although it was agreed that she would distribute her clients, she never got around to it. So I set up a meeting with a key account and one of our salespeople. When she tried to disrupt the meeting, she violated the shared value we had established and was dismissed from her position. Everything fell into place after that.

Forging A Shared Purpose

Change always begins with a grievance—there’s something people don’t like and they want it to change. Yet the status quo always has inertia on its side and never yields its power gracefully. That’s why it’s so important to forge a shared purpose, because people need a common mission they can believe in to see themselves as stakeholders in a shared future.

The reason so many organizations find themselves unable to pursue a purpose isn’t because they don’t want to, but because it is so hard. Purpose doesn’t begin with a single step, but with a diverging path. To honor a value we need to be willing to incur costs and constraints. We must choose one direction at the expense of another, or stay mired and lost, unable to move forward.

That’s why the change conversation needs to focus on what you value. Values are how an enterprise honors its mission. They represent choices of what an organization will and will not do, what it rewards and what it punishes and how it defines success and failure. Perhaps most importantly, values will determine an enterprise’s relationships with other stakeholders, how it collaborates and what it can achieve.

Perhaps most importantly, shared values enable a shared identity, which is what you need for change to last. The goal of a revolution, as Srdja Popović once explained to me, is not a constant state of disruption, but eventually to become mainstream, to be mundane and ordinary. That can only be done if change is built on common ground.

— Article courtesy of the Digital Tonto blog
— Image credit: Google Gemini

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Top 10 Human-Centered Change & Innovation Articles of April 2026

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Top 10 Human-Centered Change & Innovation Articles of April 2026 Drum roll please…

At the beginning of each month, we will profile the ten articles from the previous month that generated the most traffic to Human-Centered Change & Innovation. Did your favorite make the cut?

But enough delay, here are April’s ten most popular innovation posts:

Why an AI Soft Landing Might Look Like Victorian England — by Braden Kelley
The Four Psychological Disruptions of AI at Work — by Braden Kelley
Liberated to Care – How AI Can Restore Humanity in Healthcare — by Kellee M. Franklin, PhD.
The Consumption Collapse – When the Feedback Loop Bites Back — by Art Inteligencia
Four Steps to the Future – Announcing the Newest FREE Addition to the FutureHacking™ Toolkit — by Braden Kelley
Which of the Nine Innovation Roles do you play? (A Quiz) — by Braden Kelley
How to Consciously Develop More Courage — by Tullio Siragusa
Does Planned Obsolescence Fuel the Fire or Just Burn the House Down? – The Innovation Paradox — by Braden Kelley
Misunderstanding Big Ideas is Very Dangerous — by Greg Satell
Artificial Intelligence Powered Teamwork — by David Burkus

BONUS – Here are five more strong articles published in March that continue to resonate with people:

Why Networks Can Outperform Hierarchies — by Greg Satell
Leading Through Uncertain Times — by David Burkus
Layoffs, AI, and the Future of Innovation – Efficiency Breakthrough or Creative Bankruptcy? — by Braden Kelley
Connecting People in a Time of Isolation and Detachment — by Douglas Ferguson
How Good is Your Situational Awareness? — by Mike Shipulski

If you’re not familiar with Human-Centered Change & Innovation, we publish 4-7 new articles every week built around innovation and transformation insights from our roster of contributing authors and ad hoc submissions from community members. Get the articles right in your Facebook, Twitter or Linkedin feeds too!

Have something to contribute?

Human-Centered Change & Innovation is open to contributions from any and all innovation and transformation professionals out there (practitioners, professors, researchers, consultants, authors, etc.) who have valuable human-centered change and innovation insights to share with everyone for the greater good. If you’d like to contribute, please contact me.

P.S. Here are our Top 40 Innovation Bloggers lists from the last five years:

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If Inertia is Not Your Friend Then Time is Your Enemy

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GUEST POST from Geoffrey A. Moore

As managers, because we are always in the middle of something, we can easily forget how much our operating model depends on inertia for its success. We count on our supply chain to deliver more or less as promised, we expect our quarterly bookings to be pretty much as forecasted, and we count on our customer churn to be within its normal range. This is the world of the Performance Zone and the Productivity Zone, one we measure largely based on its financial performance, something that is made possible by inertia, the tendency of objects in motion to continue in motion, albeit with well-timed well-directed boosts from ourselves and our partners.

Disruptive innovation breaks this pattern. When successful, it can generate spectacular momentum with early adopters, but that fizzles out when things hit the chasm. The whole point of crossing the chasm is to restart the engine of inertia, first around a single compelling use case in a single beachhead target market, then building out to adjacent use cases and segments. Wherever inertia can get established, reliable supply chains, forecastable bookings, and manageable churn will follow.

But here is the thing to keep in mind while this effort is underway: the clock is ticking! That’s why we say, when inertia is not your friend, time is your enemy. As a consequence, whenever you are managing anything disruptive, be that an external offering to customers or an internal revamping of your business model, operating model, or infrastructure model, you must prioritize time to tipping point over all other variables.

The single most valuable tactic for staying on top of your time budget is establishing a cadence of weekly commits. Each commit is tied to a change in state that will be brought about within the next seven days, each change in state representing a meaningful step towards the tipping point. You can’t afford to ignore your finances, but do not let financial metrics distract you from prioritizing time to tipping point. Until you have established inertial momentum, financial performance is ephemeral, and not a good predictor of business health.

Finally, because weekly commits is a challenging discipline, it is critical to enlist your team in the higher cause that warrants extraordinary efforts on their behalf. It does no good to shame people who have missed a commit. Rather the motto is win or learn. Either make the commit and take the next step, or understand the root cause of why you missed the commit and adjust accordingly. Do not get discouraged. Be resilient.

That’s what I think. What do you think?

Image Credit: Gemini, Geoffrey Moore

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Better to be Careful than Smart

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GUEST POST from Greg Satell

Not too long ago, I had a post about the danger of trusting your feelings go viral on LinkedIn. The reason it was so popular wasn’t necessarily that everyone liked it, but because many wanted to voice their disapproval. A surprising number of people vehemently objected to the idea that they should interrogate their feelings or keep them in check.

Make no mistake. While it is true that our emotions can alert us to dangers that our rational mind fails to recognize, they can also lead us wildly astray. Our hippocampus, where our memories reside, has a bee line to our amygdala, which plays a role in governing our emotions, circumventing our rational brain in the prefrontal corpus.

We tend to assume that good judgment is a function of intelligence and education, but often it’s not. We need to recognize that there are glitches in our neural machinery and that our gut feelings can be triggered by random events as well as by people who seek to manipulate us. That’s why we need to be careful. It’s always the suckers who think they’re playing it smart.

Why Smart People Are So Easily Fooled

For decades, the global elite revered Bernie Madoff as one of the world’s most talented asset managers until it was all exposed to be, in his own words, “one big lie.” Elizabeth Holmes’s prominent board at Theranos were so clueless that they put their reputations behind a product that didn’t exist. Anna Sorokin, the daughter of a Russian truck driver, was able to convince the glitterati that she was, in fact, a fabulously wealthy heiress.

In each case, there was no shortage of opportunities to unmask the fraud. Inconsistencies in Madoff’s records were reported to regulators a number of times, but were ignored. Holmes wasn’t able to produce a single peer-reviewed study during 10 years in business to support her claims and there was no shortage of whistleblowers from inside and outside the company. Anna Sorokin left unpaid bills all over town.

Still, many bought the ruses and would interpret facts to support them. Madoff’s secrecy was seen as confirmation that he had a proprietary method. In Holmes’ case, her eccentricities were taken as evidence that she truly was a genius, in the mold of Steve Jobs or Mark Zuckerberg. Sorokin’s unpaid bills were seen as proof of her wealth. After all, who but the fabulously rich could be so nonchalant with money?

People should have known better. Stock market regulators are trained to recognize fraud. Prominent Theranos board members like George Shultz, David Bois and Henry Kissinger, earned their reputations over decades. Hotels allowed Sorokin to stay in luxury suites for weeks at a time before demanding payment. How could they have been so naive?

But what if smart people get taken in because they’re smart? They have a track record of seeing things others don’t, making good bets and winning big. People give them deference, come to them for advice and laugh at their jokes. They’re used to seeing things others don’t. For them, a lack of discernible evidence isn’t always a warning sign. It can be an opportunity.

Gated Community Elites And TED Talk Elites

Living in a gated community necessarily cuts you off from your surroundings. People outside can’t wander in and you can’t wander out. New businesses don’t sprout up and old ones don’t die. Routines are familiar and protected, you remain in your comfort zone and any random disturbance is immediately removed.

On the other end of the spectrum, when you go to fancy conferences your imagination becomes overstimulated. You are inundated with the new and unfamiliar. The normal human experiences begin to seem passé, a remnant of a lost age, while visions of the future begin to appear more genuine than the present reality.

The truth is that both of these environments are manufactured for the tastes of the well-heeled. Gated communities are built for those who want a simple sanctuary in a messy and complex world that doesn’t always follow a linear and understandable logic. The conference world tends to overemphasize the power of imagination and possibility, ignoring the fact that the status quo exerts a power of its own.

The best indicator of what we think and what we do is what the people around us think and do. We tend to conform to the opinions and behaviors of those around us and this effect extends out to three degrees of relationships. So not only our friends’ friends, influence us deeply, but their friends too—people that we don’t even know—affect what we think.

Confirming Our Priors

Clearly, the way we tend to self-sort ourselves into homophilic, homogeneous groups shapes how we perceive what we see and hear, but it will also affect how we access information. When a team of researchers at MIT looked into how we share information—and misinformation—with those around us. What they found was troubling.

When we’re surrounded by people who think like us, we share information more freely because we don’t expect to be rebuked. We’re also less likely to check our facts, because we know that those we are sharing the item with will be less likely to inspect it themselves. So when we’re in a filter bubble, we not only share more, we’re also more likely to share things that are not true. Greater polarization leads to greater misinformation.

We’re prone to think of our brains as biological forms of computers that take in and analyze data leading to rational conclusions. That’s not true. We tend to seize upon the most easily available information, rather than the most reliable sources. We then seek out information that confirms those beliefs and reject evidence that contradicts existing paradigms.

That’s the glitch in our mental machinery that Madoff, Holmes and Sorokin exploited. The investors in Madoff’s funds felt privileged to be allowed into an exclusive investment. Theranos board members thought they were building a better future. Sorokin made those around her feel like they had access to an aristocracy of sorts.

These weren’t mere notions or passing thoughts, but assertions of identity, which is why the shills were so eager to advocate for — and actively protect — their swindlers.

Making Allowances For The Glitches In Our Mental Machinery

We all like to have opinions and like act on them. When, for instance, people were asked if they supported bombing Agrabah, the fictional hometown of the Disney character Aladdin, 30% of Republicans and 19% of Democrats said yes. Yet our urge to make judgments has nothing to do with our ability to make wise choices.

Humans tend to think in terms of narratives. We like things to fit into neat patterns and fill in the gaps in our knowledge so that everything makes sense. People who are “smart,” have a greater ability to retain and process information than most and can use their imagination to build robust visions, but that’s no guarantee those visions will conform to reality.

We need to be hyper-aware that a track record of success makes us more confident and confidence in our judgments is inversely correlated to their accuracy. That’s why it’s often better to be careful than smart. There are formal processes that can help us do that, such as pre-mortems and red teams, but most of all we need to keep ourselves in check.

Perhaps most important is to appreciate that there are glitches in our mental machinery and we are greatly influenced by our social networks. The people around us tend to have access to similar information as we do and our perceptions are colored by prior judgments we’ve made. We are surrounded by mental minefields and the only way out is to proceed with caution.

There’s a sucker born every minute and they’re usually the ones who think they’re playing it smart.

— Article courtesy of the Digital Tonto blog
— Image credit: Google Gemini

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How to Test Your Business Model

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How to Test Your Business Model

GUEST POST from Mike Shipulski

Sometimes we get caught up in the details when we should be working on the foundation. Here’s a rule: If the underlying foundation is not secure, don’t bother working on anything else.

If you’re working on a couple new technologies, but the overall business model won’t be profitable, don’t work on the new technologies. Instead, figure out a business model that is profitable, then do what it takes (technology, simplification, process improvement) to make it happen. But, often, that’s not what we do.

Often, we put the cart before the horse. We create projects to make prototypes that demonstrate a new technology, but the whole business premise is built on quicksand. There’s a reason why foundations are made from concrete and not quicksand. It’s because you can build on top of a base made of concrete. It supports the load. It doesn’t crack, nor does it fall apart. Think Pyramid of Giza.

Because foundations are big and expensive they can be difficult and expensive to test. For example, if an innovation is based on a new foundation, say, a new business model, building a physical prototype of the new business model is too expensive and the testing will not happen. And what usually happens is the foundation goes untested, the higher level technology work is done, the commercialization work is completed and the business model fails because it wasn’t solid.

But you don’t have to build a full-scale prototype of the Pyramid of Giza to test if a pyramid will stand the test of time. You can build a small one and test it, or you can run an analysis of some sort to understand if the pyramid will support the weight. But what if you want to test a new business model, a business model that has never been done before, using new products and services that have never seen the light of day? What do you do? In this case, it doesn’t make sense to make even a scale model. But it does make sense to create a one page sales tool that describes the whole thing and it does make sense to show it to potential customers and ask them what they think about it.

The open question with all new things is – will customers like it enough to buy it. And, it’s no different with the business model. Instead of creating a new website, staffing up, creating new technologies and products, create a one-page sales tool that describes the new elements and show it to potential customers. Distill the value proposition into language people can understand, describe the novelty that fuels the value, capture it on one page, show it to customers, and listen.

And don’t build a single, one-page sales tool, build two or three versions. And then, ask customers what they think. Odds are, they’ll ask you questions you didn’t think they’d ask. Odds are, they’ll see it differently than you do. And, odds are, you’ll have to incorporate their feedback into an improved version of the business model. The bad new is you didn’t get it right. The good news is you didn’t have to staff up and build the whole business model, create the technologies and launch the products. And more good news – you can quickly modify the one-page sales tool and go back to the customers and ask them what they think. And you can do this quickly and inexpensively.

Don’t develop the technology until you know the underlying business model will be profitable. Don’t staff up until you know if the business model holds water. Don’t launch the new products until you verify customers will buy what you want to sell.

Creating a new business model from scratch is an expensive proposition. Don’t build it until you invest in validating it’s worth building.

The worst way to validate a business model is by building it.

Image credit: Gemini

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7 Questions Smart Boards Ask About the Next Five Years

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7 Questions Smart Boards Ask About the Next Five Years

by Braden Kelley and Chateau G Pato

What Questions Do Smart Boards Ask About the Next Five Years? (Short Answer)

Seven questions smart boards ask about the next five years: (1) Which landing are we buying — soft or hard? (2) What stays human-accountable as machines act? (3) Is work redesign funded with the technology bet? (4) Can the organization absorb the change portfolio we are approving? (5) How do trust and human success hold when agents and automation scale? (6) How will we know we are winning — outcomes, not theater? (7) Which capabilities will still matter when the tool stack turns over? Weak answers optimize for slides and takeout. Strong answers name ownership, dignity, capacity, and a scoreboard humans can live with.

Smart boards do not ask whether the future is coming. They ask which future the company is paying for — and who will still be able to succeed inside it.

Why Is Strategy Without a Landing Just Spend?

I have sat through five-year strategy sessions where the AI line item was confident, the competitive-parity slides were polished, and nobody could say — in one human paragraph — what kind of organization would exist if the bets worked. Efficiency takeout was clear. Accountability, redesign, and change capacity were “in the plan.”

The next five years will not be won by boards that only ask about spend and speed. Soft landings are designed. Boards either demand that design — or rubber-stamp a harder one.

Question	Weak answer	Strong answer
1. Soft or hard landing?	Compete and cut cost with AI	Named what machines absorb / humans keep
2. Human accountability?	“Ethics is being stood up”	Decision rights, undo, who answers why
3. Work redesign funded?	Training is in the plan	Budget and mandate beside the tech
4. Change capacity?	Everything is priority	Stop/start rules; what we will not ask
5. Trust at scale?	NPS/SLA still strong	Human success can stop a green review
6. Winning without theater?	Adoption and maturity KPIs	Behavior, value, transfer to BAU
7. Capabilities that age?	Upskill on AI tools	Judgment skills + time to practice

1. Which Landing Are We Buying — Soft or Hard?

Why smart boards ask: Technology strategy is not neutral. It either protects human judgment and agency or densifies leftover work.

Weak answer: “We’re investing in AI to stay competitive and reduce cost.”

Strong answer: A named soft landing — what machines absorb, what humans keep, what deep work and dignity we will protect.

Demand next: One paragraph in the strategy that defines the landing in human terms — not only the vendor roadmap. For the designed split, see The AI Soft Landing; for how common pitches land both ways, see 10 Futures Being Pitched in 2026.

2. What Stays Human-Accountable as Machines Act?

Why smart boards ask: Autonomy without ownership is liability with a demo.

Weak answer: “Governance and ethics are being stood up.”

Strong answer: Named decision rights, undo, escalation, and who can be asked why when an agent acts — for customers and for internal decisions.

Demand next: An accountability map for the highest-stakes agentic or automated paths — not a principles poster. For what leaders must own by scenario, see 5 Scenarios for Agentic Organizations.

3. Is Work Redesign Funded With the Technology Bet?

Why smart boards ask: Tools without redesigned jobs produce efficient misery and burned-out “exception handlers.”

Weak answer: “Change management and training are in the plan.”

Strong answer: Budget and mandate for job redesign, incentives, retirement of the old path, and manager development — tied to the same investment case as the tech.

Demand next: Line items and owners for redesign — not a training completion KPI as proxy.

4. Can the Organization Absorb the Change Portfolio We Are Approving?

Why smart boards ask: Strategy that stacks initiatives until humans break is not ambition — it is extraction.

Weak answer: “Everything is priority; we’ll sequence in PMO.”

Strong answer: Visible portfolio load, stop/start rules, and what will not be asked of the frontline this horizon.

Demand next: A capacity view beside the initiative list — what stops if this starts. If the organization is already optimizing for the wrong future of work, see 8 Signals You’re Preparing for the Wrong Future of Work.

5. How Do Trust and Human Success Hold When Agents and Automation Scale?

Why smart boards ask: Green operational metrics can hide red journeys. Containment is not care.

Weak answer: “CSAT/NPS and SLAs remain strong.”

Strong answer: Experience-level measures, recovery power, and customer/employee moments that matter — with owners who can change the system.

Demand next: One critical journey where human success can stop a “green” review. For the moments that outrank a scoreboard, see 8 Moments That Matter More Than Your NPS Dashboard.

6. How Will We Know We Are Winning — Outcomes, Not Theater?

Why smart boards ask: Five-year decks love activity — pilots, platforms, “AI maturity.” Boards need landings.

Weak answer: “We’ll track adoption, usage, and innovation KPIs.”

Strong answer: Adopted behavior, retired workarounds, value captured, kill rate with honor, transfer to BAU — outcomes that decide funding and promotion.

Demand next: A short scoreboard that demotes vanity metrics; theater cannot close the quarter. For the catalog, see 12 Metrics That Actually Measure Innovation Value.

7. Which Capabilities Will Still Matter When the Tool Stack Turns Over?

Why smart boards ask: Buying the next model is not building an organization that ages well.

Weak answer: “We’re upskilling everyone on AI tools.”

Strong answer: Investment in problem framing, sense-making, judgment, empathy with stakes, facilitation, repair, and meta-learning — and contiguous time to practice them.

Demand next: Hiring, promotion, and calendar design that reward those capabilities — not only prompt fluency. For the list, see 9 Skills That Age Well When Everything Automates.

How Do Smart Boards Run a Five-Year Readiness Check?

Before the next strategy offsite, run five go/no-go questions:

Can we describe our landing in one human paragraph?
Who is accountable when machines act?
Where is redesign funded beside the tech?
What will we stop so capacity is real?
Which outcome metrics — not activity — will tell us we are winning in year three and year five?

Smart boards don’t buy the pitch. They buy the landing — and the humans who must live it.

Frequently Asked Questions

What questions should boards ask about AI and the future?

Boards should ask which soft or hard landing the AI strategy buys, what stays human-accountable as machines act, whether work redesign is funded with the tech, whether the organization can absorb the change portfolio, how trust and human success hold at scale, how winning will be measured without theater, and which capabilities will still matter when tools turn over.

What is a soft landing for boards?

A soft landing for boards is a five-year strategy where machines absorb fragmentation and low-judgment transaction while humans keep judgment, accountability, dignity, and contiguous time for meaningful work — with redesign, change capacity, and outcome metrics funded beside the technology bet. A hard landing densifies leftovers and calls it progress.

How should boards govern AI over five years?

Boards should govern AI over five years by demanding an accountability map for high-stakes automated action, funding work redesign with the investment case, insisting on portfolio stop/start honesty, requiring human-success measures that can stop green operational reviews, demoting vanity AI maturity metrics, and investing in capabilities that age past the next model cycle.

What should boards ask about change capacity?

Boards should ask whether the organization can absorb the change portfolio being approved — with a visible load view, stop/start rules, and clarity on what will not be asked of the frontline. Stacking every initiative as “priority” without capacity is extraction dressed as ambition.

How do boards measure AI success beyond pilots?

Boards measure AI success beyond pilots with outcomes that decide funding and promotion: adopted behavior, retired workarounds, value captured, honorable kill rate, and transfer to BAU — not only usage, adoption percentages, or “AI maturity” theater.

Image credits: Gemini

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Google Gemini and Cursor to clean up the article, add images and create infographics.

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9 Skills That Age Well When Everything Else Automates

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9 Skills That Age Well When Everything Else Automates

by Braden Kelley and Art Inteligencia

What Skills Age Well When Everything Else Automates? (Short Answer)

Nine skills that age well when everything else automates: (1) problem framing, (2) sense-making from weak signals, (3) empathy with stakes, (4) judgment under uncertainty, (5) taste and restraint, (6) facilitation across difference, (7) teaching judgment, (8) repair and recovery, and (9) meta-learning — unlearning and relearning as tools turn over. Automation absorbs search, draft, route, summarize, and first-pass options. What compounds is the human ability to ask better questions, connect meaning, hold dignity, choose with accountability, refuse the wrong elegant answer, convene conflict into progress, transfer judgment, mend what broke, and keep learning when the stack resets.

Tools expire. Skills that help humans make meaning, hold stakes, and choose under uncertainty compound.

Why Is Speed a Commodity — and Judgment a Skill That Compounds?

I have watched careers reorganize around whichever tool was loudest that quarter. First the dashboard. Then the chatbot. Then the agent. Each wave promised that fluency with the new instrument would be the scarce skill. Each wave made the instrument cheaper — and made the human capacities underneath more valuable, not less.

Soft landings are designed. Prompt fluency helps you use the tools. It is not the whole investment thesis for a human career or a learning organization. The skills that age well are portable: they travel across software generations, job titles, and industry costumes. They are also distinct from the work categories AI should free — insight, empathy, collaboration, and the rest — which I map elsewhere as endeavors to protect. Skills are how you practice those endeavors when the model is free and the calendar is still crowded.

Skill	Why it ages well	Costume version
1. Problem framing	Answers get cheap; wrong frames still scale wrong	Instant roadmaps that skip the question
2. Sense-making	Summaries automate; meaning needs an owner	Dashboard tourism; more slides, no insight
3. Empathy with stakes	Synthetic personas scale; lived dignity does not	Tone guidelines without contact
4. Judgment under uncertainty	Options generate; accountability does not	“The system decided”
5. Taste and restraint	Generation floods; refusal becomes scarce	Ship because you can
6. Facilitation across difference	Notes automate; trust across conflict does not	Standups without decisions
7. Teaching judgment	How-tos are infinite; transfer is relational	Completions mistaken for capability
8. Repair and recovery	Failure modes multiply; recovery needs a human	Apology scripts without power to fix
9. Meta-learning	Tools churn; unlearning compounds	One certification as a career

1. Why Does Problem Framing Age Well?

The skill: Naming the right problem, constraints, and stakes before freezing solutions.

Why it ages well: Models generate answers at volume. Wrong frames still produce wrong speed — only faster.

Costume: Instant roadmaps and “solutions” that skip the question entirely.

Practice: One crisp problem statement owned before ideation. Kill ideas that solve a different problem, even when the demo is gorgeous.

2. Why Is Sense-Making a Skill That Compounds?

The skill: Turning noise, fragments, and conflicting data into a point of view someone can act on.

Why it ages well: Summaries get cheap. Meaning still requires a human who will stand behind it.

Costume: Dashboard tourism — more slides, no insight.

Practice: Contiguous time to connect. Treat insight as a named deliverable, not a side effect of more output. For the work AI should free so this skill can grow, see 11 Human Endeavors AI Should Free (Not Replace).

3. What Is Empathy With Stakes — and Why Does It Age Well?

The skill: Understanding what a situation costs a real person — friction, fear, shame, power — not only what they click.

Why it ages well: Synthetic personas and sentiment tags scale. Lived stakes do not.

Costume: Empathy theater; tone guidelines without contact or recovery power.

Practice: Field contact. Ask what almost stopped them. Design for dignity, not only conversion.

4. Why Does Judgment Under Uncertainty Outlast Automation?

The skill: Deciding with incomplete information, naming tradeoffs, and remaining accountable for why.

Why it ages well: Options generate easily. Ownership of consequences does not automate.

Costume: Rubber-stamp approvals; “the system decided.”

Practice: Explicit decision rights. Undo. A human who can be asked why after the choice. Soft landings keep judgment human-accountable — see The AI Soft Landing.

5. Why Do Taste and Restraint Become Scarcer as Generation Gets Cheap?

The skill: Aesthetic, ethical, and strategic discernment — elegance that refuses the wrong elegant answer.

Why it ages well: Generation floods the zone. Curation and refusal become scarce.

Costume: Infinite variants; ship because you can.

Practice: Written “will not build” lists. Kill criteria. Quality standards that survive demos.

6. Why Does Facilitation Across Difference Still Matter?

The skill: Convening people with unequal power, incentives, and worldviews into a workable next step.

Why it ages well: Meeting notes and transcripts automate. Trust across difference does not.

Costume: Standups without decisions; collaboration theater.

Practice: Named outcomes for every convening. Surface winners and losers. Protect dissent.

7. Why Is Teaching Judgment a Skill That Multiplies?

The skill: Coaching others to frame, choose, and recover — multiplying capability beyond your own output.

Why it ages well: How-to content is infinite. Judgment transfer still requires a human relationship.

Costume: Training completions; prompt cheat sheets mistaken for capability.

Practice: Apprenticeship moments. Co-decide, then debrief. Measure behavior change in others — not video minutes. For related innovation practice, see 9 Habits of Human-Centered Innovators That Still Matter in the Age of AI.

8. Why Does Repair and Recovery Age Well as Automation Scales?

The skill: Restoring dignity and function when something breaks — for customers, colleagues, and communities.

Why it ages well: Failure modes multiply with automation. Recovery still needs a human who can own the seam.

Costume: Scripted apologies without power to fix.

Practice: Recovery authority. Close loops from incident to redesign. Practice after-action honesty. For org signals that shrink this skill while celebrating AI, see 8 Signals You’re Preparing for the Wrong Future of Work.

9. What Is Meta-Learning — and Why Does It Outlast Every Tool Wave?

The skill: Updating mental models, discarding obsolete craft, and acquiring new practice without identity collapse.

Why it ages well: Tools turn over. Learning agility compounds across every wave.

Costume: One certification as a career; “keeping up” as anxiety without depth.

Practice: Deliberate unlearning. Spaced practice. Reflect on what the last tool made you stop noticing — then reclaim it.

How Do You Check Skill Investment — for Yourself and Your Organization?

Before the next AI training push or hiring freeze, run five questions. If you cannot answer them, you may be funding tool fluency while starving the skills that compound:

Which of the nine are we hiring and promoting for — not only prompt fluency?
Where does contiguous time exist to practice sense-making and judgment?
Who is rewarded for restraint and repair — not only volume?
How do we teach judgment — not only tasks?
What are we deliberately unlearning this quarter?

Don’t race the model on speed. Invest in skills that still matter when the model is free.

Frequently Asked Questions

What skills will still matter with AI?

Skills that still matter with AI include problem framing, sense-making, empathy with stakes, judgment under uncertainty, taste and restraint, facilitation across difference, teaching judgment, repair and recovery, and meta-learning. These compound as automation absorbs draft, search, and routine options.

What human skills age well with automation?

Human skills that age well with automation are portable capacities that help people make meaning, hold dignity, choose with accountability, refuse the wrong elegant answer, convene conflict into progress, transfer judgment, mend failure, and keep learning when tools reset — not only fluency with the current model.

Should I learn prompting or soft skills?

Learn both — but do not confuse them. Prompting helps you use tools. Soft skills that age well — framing, judgment, empathy with stakes, facilitation, teaching, repair, meta-learning — are what remain valuable when prompting itself becomes cheaper and more automated. Invest in both without treating prompts as a career strategy.

How do you develop judgment skills?

Develop judgment by practicing decisions with incomplete information, naming tradeoffs out loud, keeping decision rights explicit, reviewing outcomes with after-action honesty, and coaching others through co-decide-and-debrief cycles. Judgment grows with accountable practice — not with more option generation alone.

What skills should companies invest in as AI automates work?

Companies should invest in hiring, promoting, and giving contiguous time for problem framing, sense-making, empathy with stakes, judgment, taste and restraint, facilitation, teaching judgment, repair, and meta-learning — alongside tool training. If only volume and prompt fluency are rewarded, the skills that age well atrophy.

Image credits: Pixabay

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We Must Think Less Like Engineers and More Like Gardeners

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We Must Think Less Like Engineers and More Like Gardeners

GUEST POST from Greg Satell

In February, 1919, the famous philosopher Bertrand Russell received a card from his former student, Ludwig Wittgenstein, who was at that time in an Italian prison camp. “I’ve written a book which will be published as soon as I get home,” he would say in subsequent correspondence. “I think I’ve solved our problems finally.”

The “problems” he spoke of had to do with a foundational crisis in mathematics and logic that defied the efforts of the world’s greatest minds. The book, Tractatus Logico-Philosophicus, was an attempt to engineer a perfectly logical language from first principles. It would become enormously influential, leading to the Vienna Circle and the logical positivist movement of the 1920s.

Yet Wittgenstein would later disown the idea and it was, in the end, found to be unworkable. There are limits to what we can engineer. The world is a messy place. Rules inevitably have exceptions, which is why every system will always crash. That’s why we need to think less like engineers making machines and more like gardeners that grow and nurture ecosystems.

The Death of the Secular Gods

The problems Russell and Wittgenstein were working on were part of a larger paradigm shift. By the late 19th century, many intellectuals had begun to question ideas passed down from the ancient Greeks, such as Aristotle’s Logic, Euclid’s geometry and the miasma theory in medicine, overturning two thousand years of conventional wisdom.

It’s hard to overstate the seismic shift that this represented. Aristotle’s use of the syllogism, in which conclusions necessarily followed premises, Euclid’s postulate that parallel lines never intersect and Hippocrates theory that bad air causes disease, were considered to be the basic foundations upon which western thought was predicated.

Yet as human knowledge advanced, people began to see flaws in these precepts. Strange paradoxes called Aristotle’s logic into question. Mathematicians like Gauss, Lobachevsky, Bolyai and Riemann began to imagine curved spaces in which parallel lines did, in fact, intersect and scientists such as Robert Koch, Joseph Lister and Louis Pasteur established the germ theory of disease.

These would be, practically speaking, incredibly positive developments. The rise of non-Euclidean geometry made Einstein’s general theory of relativity possible and the germ theory of disease paved the way for antibiotics and much longer lifespans. Yet they created an unwarranted optimism about what the human mind could achieve.

A New Religion

In the early 20th century, science and technology emerged as a rising force in western society. The new wonders of electricity, automobiles and telecommunication were quickly shaping how people lived, worked and thought. Physicists like Einstein and Bohr became celebrities. It seemed that there was nothing that scientific precision couldn’t achieve.

It was against this backdrop that Moritz Schlick formed the Vienna Circle, which became the center of the logical positivist movement and throughout the 20’s and 30’s. At its core was Wittgenstein’s theory of atomic facts, the idea that the world could be reduced to a set of statements that could be verified as being true or false—no opinions or speculation allowed. Those statements, in turn, would be governed by a set of logical algorithms which would determine the validity of any argument.

Yet even as this logical movement was growing, the foundational crisis in logic continued. To solve the problem, David Hilbert the greatest mathematician of the era, proposed a program to solve the crisis that rested on three pillars. First, mathematics needed to be shown to be complete in that it worked for all statements. Second, mathematics needed to be shown to be consistent, no contradictions or paradoxes allowed. Finally, all statements need to be computable, meaning they yielded a clear answer.

Then things took a surprising turn. A young logician named Kurt Gödel would prove that every logical system is flawed with contradictions. Alan Turing would show that all numbers are not computable. The Einstein-Bohr debates would be resolved in Bohr’s favor, destroying Einstein’s vision of an objective physical reality and leaving us with an uncertain universe.

The Rise Of Faux Scientists

The verdict was in. Facts could never be absolutely verifiable, but would stand until they could be falsified. We could, after thorough testing, increase our confidence, but never be completely sure. Ironically, the demise of logic led directly to the era of digital computing and a new, technological age. Just as we learned that systems would always be fallible, the machines we built became unimaginably powerful.

At the same time, human agency was increasingly called into question. It was, after all, subjective judgements that led to the Great Depression of the 1930s and the enormous wars that followed it. As the Baby Boomers came of age in the 1960s, it seemed like everything was up for debate. All of the fuzziness and uncertainty of relying on human judgment increasingly seemed impractical.

Much like Wittgenstein and the Vienna Circle, a number of thinkers sought to engineer systems that would harness natural forces to create better outcomes. The Austrian School of economics eschewed government regulation in favor of consumer preferences. Neorealism in foreign relations argued that competition and conflict could govern that international order.

Yet unlike the original logical positivists, these ideas wouldn’t stay confined to academia, but would seep into the affairs of everyday people. The consumer welfare standard insisted that market price signals, not government bureaucrats, would decide if a transaction should be permitted, while the principle of shareholder value demanded that the stock market, not managers, should govern business decisions.

The results are clear. Too little antitrust regulation has increased concentration in the vast majority of American industries and strangled competition, which has decreased business dynamism and lowered productivity. Our economy has become markedly less productive, less competitive and less dynamic. Purchasing power for most people has stagnated. By just about every metric, we’re worse off.

We Need To Manage Ecosystems, Not Machines

We like to think of ourselves as rational actors, weighing each piece of evidence before making a decision. Yet our brains don’t work like that. We build up our perspectives through synapses in our brain and through our social networks, which form complex webs of influence. Once we adopt a point of view, we rarely adapt it to new evidence.

Engineers believe in laws that can be understood and put to specific use, so they build machines to perform specific tasks. Gardeners believe in complexity and emergence. They don’t design their garden as much as tend to it, nurture it and support its surrounding ecosystem. They don’t expect the same results every time, but understand they will need to adjust their approach as they go.

We need to think less like engineers and more like gardeners. For most important purposes, we manage ecosystems, not machines. We need to think more in terms of networks that grow and less in terms of nodes whose behavior we can predict and control. Our success or failure depends less on individual entities than the connections between them.

In a world driven by networks and ecosystems, we can no longer treat strategy as if it were a game of chess, planning out each move with near perfect precision and foresight. The task of leadership is to make decisions with full knowledge that many will be wrong and that you will need to make them right.

There’s no system to do that for us, no impersonal forces that will point the way. In the end, we have to put trust in ourselves. There isn’t anyone else.

— Article courtesy of the Digital Tonto blog
— Image credit: Google Gemini

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5 Scenarios for Agentic Organizations

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And What Leaders Must Own in Each

5 Scenarios for Agentic Organizations

by Braden Kelley and Chateau G Pato

What Must Leaders Own in Agentic Organizations? (Short Answer)

Five scenarios for agentic organizations — and what leaders must own in each: (1) Agentic Front Door — customer-acting agents; leaders own consent, undo, escalation, and brand accountability. (2) Agentic Operating Model — work-orchestrating agents; leaders own fairness, EX, and who gets meaningful work. (3) Agentic Decision Systems — agents that approve or choose within policy; leaders own decision rights, undo, and who can be asked why. (4) Agentic Innovation & Change — agents that accelerate experiments; leaders own problem framing, kill criteria, and adoption. (5) Agentic Ecosystem — agents that act across vendors and partners; leaders own seam promises, liability, and recovery across the handoff.

An agentic organization authorizes AI systems to act — route, refund, rebook, assign, escalate, negotiate, trigger workflows — not only to chat or recommend. Autonomy for the model is not a strategy. Ownership for the human who can still be held to account is.

Why Don’t Agents Automate Ownership?

I have watched rooms celebrate “agentic” the way they once celebrated sticky notes and digital transformation: as if naming the technology were the same as designing the landing.

Agents that act are a different animal from chatbots that reply. They refund. They rebook. They assign work. They approve inside a band. They negotiate across a seam. That is not a UX upgrade. It is a transfer of authority — and authority without designed ownership is a hard landing waiting for a Tuesday.

Soft landings are designed. In every agentic scenario, something remains non-delegable: consent, mandate, accountability, fairness, and the promise the brand will still keep when the agent gets it wrong.

Scenario	Agents do	Leaders must own
1. Agentic Front Door	Act for customers (refund, rebook, route)	Consent, undo, escalation, brand accountability
2. Agentic Operating Model	Orchestrate internal work	Fairness, EX, judgment time, meaningful work
3. Agentic Decision Systems	Approve or choose inside policy bands	Decision rights, why on Tuesday, undo/appeal
4. Agentic Innovation & Change	Accelerate drafts, demos, pilots	Problem frame, kill criteria, adoption owners
5. Agentic Ecosystem	Act across vendors and partners	Seam ownership, liability, cross-entity recovery

1. What Must Leaders Own in an Agentic Front Door Scenario?

The scenario: The primary customer path is an agent that does — refund, reschedule, rebook, update, route — not a FAQ that chats. Multi-step journey work moves at machine speed.

Agents do: Execute customer intent across systems without waiting for a ticket to crawl through three departments.

Hard landing: Autonomy without consent, undo, or human handoff. Containment becomes the KPI. The brand is optimized; dignity is optional. Customers are trapped in loops that photograph well in the steering committee.

Leaders must own: Clarity of what the agent may do; competence thresholds before it acts; customer control to stop, reverse, and escalate; care at the moment of failure; a named human recovery path. Soft landing is delegated action with a trust contract — not a cheaper call-center costume. For the customer trust design behind this scenario, see When AI Agents Act on Your Behalf: Designing Agentic Customer Experience That Earns Trust.

2. What Must Leaders Own When Agents Orchestrate the Operating Model?

The scenario: Internal work is assigned, scheduled, triaged, and progressed by agents across tickets, cases, and handoffs. Glue shrinks. Throughput rises. The calendar looks free — until it does not.

Agents do: Route, prioritize, draft, chase, and assemble the next step so humans spend fewer hours on fragmentation.

Hard landing: Humans become faster exception handlers. Saved minutes refill with denser interruptions. “Efficiency” strips judgment time and frontline agency. People become the leftover of automation — not the point of it.

Leaders must own: What work agents absorb versus what humans keep; protected contiguous time for judgment; employee experience as a design constraint, not a wellness poster; fairness in routing (who gets the grind, who gets growth); metrics that name human success, not only cycle-time vanity. Soft landing means glue automated, meaning kept. For the designed split of human versus machine work, read The AI Soft Landing.

3. What Must Leaders Own in Agentic Decision Systems?

The scenario: Agents approve, deny, price, allocate, or recommend within policy bands — finance, risk, access, offers, exceptions. High-volume choices move without a meeting. Edge cases escalate — in theory.

Agents do: Decide inside declared rules at a speed no committee can match.

Hard landing: Humans as rubber stamps. Unowned model choices. “The system decided” as a career-safe shrug. No undo when the band was wrong — and nobody left who can be asked why on Tuesday.

Leaders must own: Explicit decision rights (what the agent may decide alone); auditability so a human can reconstruct the choice; undo and appeal; a named person accountable for outcomes, not only for “the model’s accuracy”; the mandate to narrow or widen the band when reality contradicts the policy. Soft landing is speed with someone who can still be held to account.

4. What Must Leaders Own When Agents Accelerate Innovation and Change?

The scenario: Agents draft personas, roadmaps, prototypes, pilots, and change artifacts before lunch. Innovation velocity looks historic. The room fills with demos.

Agents do: Generate options, simulate, document, and accelerate demo day until the portfolio looks busy.

Hard landing: Faster wrong. Synthetic empathy. Pilots without mandate. Adoption theater. Innovation cosplay at higher RPM — more output, less impact that lands on Tuesday.

Leaders must own: Problem framing before acceleration; match of method to decision rights; behavior falsification (not applause demos); kill criteria with social permission to stop; adoption owners after the markers dry. Soft landing means agents own the draft; humans own the design and the landing. For habits that protect this ownership, see 9 Habits of Human-Centered Innovators That Still Matter in the Age of AI.

5. What Must Leaders Own in an Agentic Ecosystem Scenario?

The scenario: Agents representing the company, customer, and partners negotiate, book, fulfill, and escalate across organizational boundaries. Email chains and war rooms shrink. Multi-entity orchestration becomes normal.

Agents do: Cross the seam that used to require humans with calendars, contracts, and courage.

Hard landing: Orphan seams. “Not our agent” blame. Liability that lives nowhere. A brand promise broken between two green systems — each vendor’s SLA fine, the customer’s Tuesday ruined.

Leaders must own: Who owns the seam when agents disagree; contractual and ethical boundaries for partner agents; recovery power that spans entities; customer-visible accountability when the multi-agent path fails; stop conditions when optimization conflicts with dignity. Soft landing is multi-agent speed with a single human promise that still holds.

How Do Leaders Check Ownership Before an Agentic Investment?

Before the next agentic program, run five go/no-go questions. If you cannot answer them, you are buying autonomy without a landing:

Where will the agent act — not only chat or recommend?
Who owns consent, undo, and escalation in that scenario?
What human work are we freeing — and what contiguous time will we protect for judgment?
Who can be asked why when the agent chooses?
Who owns the seam when agents cross teams or partners?

For the work humans should keep when glue shrinks, see 11 Human Endeavors AI Should Free (Not Replace). For how the agentic enterprise pitch lands soft or hard, read 10 Futures Being Pitched in 2026 — Soft Landing vs Hard Landing. For signals you are preparing for the wrong future, use 8 Signals You’re Preparing for the Wrong Future of Work.

Agents can own the action. Leaders own the landing — consent, mandate, accountability, fairness, and the promise after failure.

Frequently Asked Questions

What is an agentic organization?

An agentic organization authorizes AI systems to act — route, refund, rebook, assign, escalate, negotiate, and trigger workflows — not only to chat or recommend. It is an operating model with non-human actors holding authority, which makes designed human ownership of consent, mandate, accountability, and recovery essential.

What must leaders own in an agentic enterprise?

Leaders must own what agents cannot: consent, undo, and escalation at the front door; fairness and judgment time in the operating model; decision rights and accountability when agents choose; problem framing, kill criteria, and adoption in innovation; and seam ownership, liability, and recovery when agents act across partners.

What are scenarios for agentic AI in organizations?

Five common scenarios: agentic front door (customer-acting agents), agentic operating model (work orchestration), agentic decision systems (approvals inside policy bands), agentic innovation and change (accelerated bets and artifacts), and agentic ecosystem (cross-vendor and partner action). Each needs a different leadership ownership list.

How do you avoid a hard landing with AI agents?

Design the landing before you scale autonomy. Name where agents will act, who owns consent and undo, what human work you are freeing and protecting, who can be asked why when the agent chooses, and who owns the seam across teams or partners. Autonomy without ownership is a hard landing on Tuesday.

What is the difference between agentic CX and an agentic organization?

Agentic CX focuses on customer-facing agents that act on a customer’s behalf — and the trust contract of clarity, competence, control, and care. An agentic organization is broader: agents also orchestrate internal work, decide inside policy, accelerate innovation, and act across partner ecosystems. CX is one scenario; the organization has five places where leaders must own the landing.

Image credits: Cursor

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Winning with Artificial Intelligence in 90 Days

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Exclusive Interview with Charlene Li

The rapid evolution of artificial intelligence (AI) has shifted the technology from a futuristic curiosity to the primary engine of modern organizational growth. In an era defined by data-driven decision-making, the ability to effectively harness machine learning and predictive analytics is no longer just a competitive advantage; it is a fundamental requirement for long-term viability. However, the path to integration is rarely linear. Many organizations find themselves caught between the urgent need for transformation and the daunting reality of legacy infrastructure, talent shortages, and the cultural shifts required to move beyond small-scale pilots toward true enterprise-wide intelligence.

While the potential for increased efficiency and innovation is clear, the execution remains a significant hurdle.

The organizations that thrive in this new landscape are those that treat AI as a core strategic pillar rather than a plug-and-play software update. This requires a rethink of how human talent and machine intelligence coexist, ensuring that the technology enhances human capability rather than simply automating existing inefficiencies. Overcoming these challenges involves not just technical prowess, but a disciplined approach to change management and a clear vision for how intelligence will redefine the value the organization provides to its customers.

Today we will dive deep into what it takes to quickly achieve success with artificial intelligence with our special guest.

Creating a 90-Day Blueprint to Win with Artificial Intelligence

I recently had the opportunity to interview Charlene Li, a New York Times bestselling author, keynote speaker, and AI transformation strategist. Her latest book, Winning with AI: The 90-Day Blueprint for Success, co-authored with Dr. Katia Walsh, gives senior leaders a practical framework for moving from AI experimentation to measurable business value. Her prior books include The Disruption Mindset, Open Leadership, and Groundswell. Fast Company named her one of the most creative people in business, and she has worked with global organizations including 14 of the Dow Jones Industrial 30 companies. She is the founder of Altimeter Group (acquired by Prophet) and currently leads Quantum Networks Group.

Below is the text of my interview with Charlene and a preview of the kinds of insights you’ll find in Winning with AI: The 90-Day Blueprint for Success presented in a Q&A format:

1. What confusion is being created by speaking of “AI” as one thing when there are different kinds of AI, and how does this hold back AI adoption?

When people say “AI,” they’re usually thinking ChatGPT. But ChatGPT is generative AI — and that’s just one of three types of AI showing up in business today. There’s also predictive AI, which has been quietly running in your CRM, your fraud detection, and your streaming recommendations for years. And there’s agentic AI, which takes autonomous action toward a goal rather than waiting for a prompt.

The Oracle (predictive), the Creator (generative), and the Agent (agentic) — that’s how Katia and I describe them in Winning with AI. They do fundamentally different things, and they require fundamentally different things from you.

The conflation matters because it leads to bad decisions. Leaders see a generative AI demo, get excited, and ask their teams to “do something with AI” — when the actual business problem might be better solved with predictive AI (and probably already could’ve been three years ago). Or they hear “agentic AI” and assume their organization is ready to deploy autonomous agents when they haven’t even gotten generative AI into their workforce yet.

The winners aren’t choosing among types — they’re using all three strategically, in combination. A customer care transformation might use predictive AI to route inquiries, generative AI to draft responses, and agentic AI to handle routine cases autonomously. Once you can see the three distinctly, the question stops being “what can I do with AI?” and starts being “what can AI do for me?” That’s the question that actually unlocks value.

2. What are some of the key characteristics of AI inertia and some of the best ways to break free?

We call it pilot purgatory — and almost every organization we work with is stuck there. The signs are easy to spot: dozens of disconnected pilots, lots of conference attendance, lots of slide decks, no measurable financial impact. An MIT study found 95% of AI initiatives fail to scale. That’s not a technology failure. It’s a failure of leadership and culture.

The classic characteristics:

- Use cases as a strategy. Many use cases equals procrastination. A long list of pilots is how organizations look busy without committing to anything.
- Diffused accountability. When the CIO, CFO, and CMO all “share” responsibility for AI, no one owns the outcome.
- Waiting for the foundation to be perfect. Clean data, the right platform, the perfect org structure — these become reasons to delay rather than constraints to solve through.
- Confusing motion with progress. Running pilots feels like progress. It isn’t, unless those pilots are tied to your most important business problems.

To break free: pick your biggest strategic problems, figure out how AI solves them, invest heavily in those solutions, and move with urgency. Appoint one AI value owner who lives, breathes, and dreams AI outcomes. Kill pilots that aren’t on a path to scale. And replace “fail fast” with “learn fast” — nobody actually rewards failure, and the language of failure lets people walk away from things that should be pushed through.
Speed is the new moat. The companies that win aren’t the ones with the best technology. They’re the ones that adapt faster than their competitors.

3. There are still a lot of people out there not using AI (or not realizing that they are). What are some of the best ways for people to get started with AI?

Most people are already using AI — every spam filter, every Google Maps route, every recommendation on a streaming service is AI. So the real question is: how do you get started with the kind of AI that’s reshaping work right now, which is generative AI?

My advice is genuinely simple. Pick one of the major tools — Claude, ChatGPT, Gemini, Copilot — and start using it for one real task you do every week. Not a toy task. A real one. Drafting an email. Prepping for a meeting. Summarizing a long document. Brainstorming an approach to a problem you’re stuck on.

Two practical tips that make a big difference:

Write better prompts. A good prompt has a role (“Act as a marketing strategist”), instructions (what you want done), context (the background the AI needs), and an output format (memo, table, slide outline). Then refine through dialogue. Most people give AI two sentences and judge it on the result. Give it two paragraphs and you’ll be amazed.

Try the flipped interaction. Instead of asking AI for an answer, ask it to ask you questions until it has enough context to give a good answer. For example, at the end of a prompt, add this sentence: “Ask me any clarifying questions you may have.” It turns your prompt into a conversation.

I think of AI fluency as learning to eat with chopsticks: at first you’re concentrating on every motion, and eventually it’s just how you eat. You won’t get there by reading about it. You get there by using it. Every day. On real work.

4. Does AI safety really matter? It seems like all of the major AI players are just focused on speed and getting to AGI before China, am I wrong?

You’re not wrong about what the AI players are doing. But you’re probably not playing that game – more on that below. First, I’d push back on the framing that safety and speed are opposites.

Think of Formula 1. The drivers who win championships have absolute confidence in their brakes, their crash structures, their fire suppression systems. That’s why they can push so hard on speed. Safety is what makes speed possible. The companies moving fastest on AI adoption aren’t the ones cutting corners on responsibility — they’re the ones with the highest ethical standards, because trust eliminates friction. When your team knows where the guardrails are, when your customers trust your intentions, when your board has confidence in your approach, you can move at the speed AI demands.

The 2024 Edelman Trust Barometer found that 43% of people would reject AI in products and services if they don’t believe the innovation has been thoroughly scrutinized. That’s not a PR problem — it’s a revenue and competitive position problem.

On the AGI race specifically, the geopolitical framing oversimplifies what’s actually a much more textured conversation about how AI is deployed within companies, governments, and communities. Most leaders I work with aren’t worrying about AGI — they’re worrying about whether their AI customer service tool is treating customers fairly, whether their AI-driven hiring screen is introducing bias, and whether their data is being used in ways customers didn’t consent to. Those are the safety questions that matter for the next five years, regardless of what the frontier players are doing.

5. Where is the government being too hands off with AI and its impacts, and what conversations should governments and societies be having about AI and its impacts that they’re not?

I’ll be careful here because I’m not a policy person — I work with the leaders implementing AI inside organizations. But from that vantage point, a few things stand out.

The conversation we aren’t having enough is about workforce transition. Not “will AI take jobs” — we’ve been arguing about that abstractly for three years. The real question is what happens to the millions of people whose roles will substantially change in the next five years, and who’s responsible for helping them adapt. Right now, that’s mostly being left to individual employers, and the gap between what enlightened employers are doing and what the median employer is doing is enormous. That gap will become a societal problem long before regulators catch up.

The second underdiscussed conversation is about education. We’re training a generation of students with curricula designed for a pre-AI world. By the time we figure out what AI fluency looks like in K–12, the kids who needed it most will be in the workforce.

Third — and this is where I’d actually like to see governments lean in more — is data. Most AI regulation focuses on the models. The leverage is in the data: who owns it, how it can be used, what consent looks like in a world where data collected for one purpose can be repurposed for AI training that wasn’t imagined when it was collected.

That said, regulations always lag technology. Anchoring your responsible and ethical AI policy in your organization’s values rather than waiting for rules is the right move, regardless of what governments do.

6. What are the key pillars that form the basis of a strong AI foundation for those who seek to take full advantage of AI in their organization?

In Winning with AI, Katia and I lay out four building blocks. They develop together, not sequentially.

Mindset — the cultural ability to move at AI’s speed. Speed, focus, customer-centricity, experimentation, and learning from setbacks rather than treating them as evidence that the technology doesn’t work. Without the right mindset, you can have the best tools in the world, and they’ll sit unused.

Skillset — AI fluency across the workforce, not just in IT. Everyone needs to understand what AI can and can’t do, how to use it responsibly, and how to apply it to their actual work.

Toolset — the technical foundation. We tell leaders to build with LEGO, not cathedrals. Modular, interchangeable components you can swap as the technology evolves, sitting on top of data that’s good enough to start with.

Decision-set — the governance and decision-making structures that let you move fast without breaking things. Who decides what, how quickly, with what oversight.

The mistake organizations make is treating these as a sequence — first we’ll fix the data, then we’ll train people, then we’ll deploy. That sequence will take you a decade. The right approach is to build the blocks while delivering value, using each AI application to strengthen multiple blocks at once.

And one piece that wraps all four: leadership. Without active, visible commitment from the top, the four building blocks don’t compound. With it, they accelerate.

7. Of all the outcomes that the different types of AI can achieve, which activities create the most value for organizations?

We frame the value AI creates in three areas: engagement, efficiencies, and reinvention.

Engagement is about deepening relationships with customers and employees through personalization, prediction, and proactive service. Anticipating what someone needs before they articulate it.

Efficiencies are about doing what you already do, faster and cheaper. This is where most organizations start — and where most get stuck. Efficiency gains are real, but they’re easy for competitors to replicate, which means they don’t create lasting advantage.

Reinvention is the most transformational and the most uncomfortable. It’s not asking “how can we do what we do faster?” — it’s asking “what becomes possible now that the old constraints are gone?” New business models. New revenue streams. New markets that were never economical before.

The trap is thinking efficiency is AI’s value. We call it the efficiency trap. Companies that limit themselves to efficiency are using a strategic weapon as a cost-cutting tool. The real competitive advantage comes from engagement and reinvention.

A great example: Coursera. Translation used to cost about $10,000 per course, which made global expansion economically impossible at the scale of their 5,000+ course catalog. Generative AI eliminated that constraint overnight. CEO Jeff Maggioncalda saw it immediately and launched Project Genesis by the end of 2022. That’s reinvention — AI removing a constraint that defined the business model.

If I had to pick one activity that creates the most value, it would be: using AI to remove a constraint that has shaped your industry’s economics for so long that nobody questions it anymore.

8. There was a lot of talk for a while about becoming an AI-first organization. Is this something that companies should be trying to do?

No. Be AI-ready instead.

“AI-first” is a technology company’s framing. It puts the technology in the driver’s seat, which sounds visionary but in practice produces dozens of disconnected pilots with no strategic impact. You end up chasing AI because it’s shiny rather than because it solves a real problem.

“AI-ready” is a business leader’s framing. It puts strategy in the driver’s seat. You’re building the culture, the skills, the decision systems, and the technical foundation that let AI create real value against the strategic priorities you already have.

Said simply: AI-first is a technology mindset. AI-ready is a business mindset.

You don’t actually need an AI strategy. You need a business strategy that uses AI. Anyone selling you on an AI strategy is selling you the wrong thing.

9. What should people be doing as individuals to maintain their value to their organizations and to grow their careers?

Three things, in order.

One: develop genuine AI fluency. Not “I’ve used ChatGPT a few times” fluency. Real fluency — the kind where AI is woven into how you think, prepare, decide, and communicate. The people and organizations who get to AI fluence in 2026 will pull dramatically ahead of those who don’t, and the gap will be very hard to close once it opens.

Two: deepen what’s uniquely human. AI can amplify cognition at speeds and scales no individual can match. What it can’t do is exercise empathy, self-reflection, intuition, judgment, and wisdom. These five traits — the foundation of what Katia and I call “superhumans” in the book — become more valuable, not less, as AI handles more of the cognitive work. The leaders who pair AI’s reach with these distinctly human capacities are the ones creating the most value.

Three: build a lifelong learning practice. The shelf life of any specific skill is shrinking. The skill that doesn’t depreciate is the ability to learn — quickly, repeatedly, with intellectual humility. Normalize not knowing. Embed reflection into how you work. Treat curiosity as a professional asset, not a side hobby.

If you do those three things, you’ll be more valuable in the future than you are today, regardless of what happens to your specific role.

10. What have organizations gotten wrong about rolling out AI and what can the early adopters do to recover from botched initial rollouts?

The biggest things organizations get wrong:

Treating AI as a technology project. It’s a business initiative for value creation that happens to use technology. When IT owns it, it stays small.
Use cases instead of strategy. A laundry list of pilots is procrastination dressed up as progress.
Diffused accountability. Without a single AI value owner, the work fragments.
Skipping the people work. Throwing tools at employees without addressing the fear underneath. Until fear is replaced by trust, no amount of training will change behavior.

If you’ve already botched the rollout, here’s the recovery path:

Stop and audit. What’s actually scaling, what’s not, what’s draining resources without producing value? Be honest. Sunset the dead ends.

Appoint one accountable AI leader. If no single person is accountable for AI value creation across the enterprise, fix that this quarter. Not part-time, not committee-led — one person whose performance is measured on the value that AI creates.

Pick one strategically meaningful problem and go after it. Not the easiest problem. The one whose solution would matter most to the business.

Learn from Ally Bank. When generative AI emerged, Ally’s CIO Sathish Muthukrishnan deliberately chose the most resistant audience — customer service agents — and a low-stakes problem: summarizing customer calls. The result was so valuable that the agents who’d been most skeptical became the loudest advocates: “Don’t take this away from me.” Targeting the skeptics with a real win is one of the most powerful change strategies we’ve seen.

A botched rollout isn’t a death sentence. It’s actually a useful clearing of the underbrush — assuming you learn from it.

11. Several studies have come out recently about the negative effects of AI on human cognition. Any tips for how to best use AI without degrading your brain?

This is a real concern and worth taking seriously. The risk isn’t AI itself — it’s lazy AI use. Using AI to skip thinking rather than to enhance it.

A few habits I’ve found useful:

Think first, then prompt. Before going to AI for an answer, write down what you think. Coursera’s Jeff Maggioncalda calls this cognitive bootstrapping — write your perspective on a decision, then ask AI to challenge it: “What are the strengths and weaknesses of this view? What are my blind spots? What would you recommend I improve?” AI sharpens your thinking instead of replacing it.

Treat AI outputs as drafts, not deliverables. Read critically. Push back. Ask why. Verify facts. The moment you stop questioning AI’s outputs is the moment your thinking starts to atrophy.

Protect deep work. Schedule time for thinking that doesn’t involve AI at all. Reading, writing, reflecting, walking — the unstructured time where your brain consolidates what it knows. AI can compress research, but it can’t compress wisdom. That still has to come from lived experience, integrated over time.

Notice the difference between using AI to accelerate something you understand and using AI to substitute for understanding. Acceleration is healthy. Substitution erodes you.

The promise of AI isn’t to do our thinking for us. It’s to help us think better. The discipline is staying on the right side of that line.

12. Any question you wish I had asked but didn’t?

Yes — I’d love a question about the human possibility on the other side of this.

Most AI conversation is about risk, displacement, and disruption. Those are real. But the conversation Katia and I get most excited about is what becomes possible when AI handles the cognitive work that has been depleting people for decades — the synthesis, the routing, the routine analysis — and frees up human capacity for what only humans can do.

We call those people “superhumans” — not because they’re enhanced by technology in some sci-fi sense, but because they finally have the room to be more deeply human. To exercise empathy, self-reflection, intuition, judgment, and wisdom at a level that’s been crowded out by cognitive overload.

The first companies to deliberately develop and organization filled with superhumans won’t just have a competitive advantage. They’ll be creating an entirely new form of value — one we haven’t fully named yet. That’s the future I want leaders thinking about. Not “how do I survive AI?” but “what becomes possible for my people on the other side of this?”

Dream it. Then build it.

Conclusion

Thank you for the great conversation Charlene!

I hope everyone has enjoyed this peek into the mind of one of the women behind the insightful new title Winning with AI: The 90-Day Blueprint for Success!

Image credits: Charlene Li, Pexels

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