But defining that box is an exercise in organizational discipline. We often mistake a “culture of innovation” for the superficial perks seen in Silicon Valley movies, free food, and games. The reality is far more demanding. As Gary Pisano articulated in The Hard Truth About Innovative Cultures, a true innovation culture requires a series of brutal paradoxes: a high tolerance for failure but zero tolerance for incompetence; a willingness to experiment but extreme discipline; and a space that is psychologically safe but brutally candid.

Without this cultural foundation, providing “the box” is useless because the organization lacks the rigor to act on it.

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What problem are we trying to solve for the organization?

Over the years, as leaders and associates challenge whether their organization has a truly innovative culture, a consistent outcome emerges: an improvement initiative. We are going to capture the ideas of all our employees; everybody can make a suggestion or propose an innovation that we will consider. But developing a culture of innovation is not about democratizing idea generation and capturing suggestions in a submission box.

When we want to innovate, the critical path is to go from idea to execution (ideas without execution are not innovation). The approach that has been tested and validated thousands of times to facilitate meaningful change does not emphasize idea generation, but rather providing the right context: guardrails, alignment on the problem to solve, and clarity on what winning looks like for the team. I have seen this process hundreds of times in organizations where Kaizen (continuous improvement) and culture are indistinguishable.

So, if a continuous improvement culture is most effective when it has the right context, why would a “maximize innovation ideas” approach result in material impact?

When the real issue is innovation, the answer is not more ideas; it is a better context, with well-defined constraints that channel creative energy toward problems the organization can actually act on.

The Math of Innovation Noise

I wrote about the commoditization of information in my Librarian Trap article . The last thing an organization needs to accelerate its innovation is a sea of ideas, with the valuable ones buried under waves of noise.

Price’s Law is a brutal reality: the square root of your headcount produces 50% of the results. In a 10,000-person organization, 100 people are driving the future. Yet corporate innovation programs are usually designed as “Suggestion Boxes 2.0,” inviting the 9,900 to brainstorm to solve engagement problems, not innovation problems. This is a strategic error. You don’t find the needle; you just build a more expensive haystack.

To increase the signal-to-noise ratio, you have to provide clear context so those working on innovation can recognize when an idea has a path to execution, the critical step of innovation.

Additionally, while AI can generate ideas, we want to keep the human in the loop on execution. Accountability is an important part of innovation decisions, and I have seen too many teams point to a spreadsheet as a justification for a decision (”the model told me so”). Accountability and ownership of execution should remain with humans, because we bring the full context of what it takes to execute an initiative successfully: we have executed ideas in the past, we know where our organization struggles with change, we have seen our competition react, and we know when a path forward requires ethical consideration.

The Human as Filter

In an AI-augmented organization, providing the right context is your differentiation and competitive advantage.

The Innovator’s DNA by Dyer, Gregersen, and Christensen introduced five discovery skills critical for disruptive innovation: Associating, Questioning, Observing, Networking, and Experimenting. The research’s core claim was that these are learnable behaviors, not fixed traits, thereby democratizing who can innovate..

In 2026, those five skills will be commoditized by AI. LLMs can associate and question more effectively and quickly than the average middle manager.

What AI cannot do, and what the 9,900 lack the experience and vantage point for , is Contextual Synthesis. Innovation is not the act of coming up with a “cool idea.” It is the act of filtering an idea through the brutal constraints of your business, which requires embodied experience and contextual judgment: the skills that cannot be prompted into existence. If an idea does not fit your commercial channel or your technical core, it is not an innovation; it is a distraction.

Today, the cost of generating an idea has effectively dropped to zero. The competitive advantage no longer lies in the knowledge stored in the organization.

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The Constraint Imperative

We have to shift our focus from generating knowledge to enabling judgment. The value of asking the right questions now outweighs providing answers, because answers have become very cheap.

Within product development, the most practical expression of that judgment is defining what a good idea looks like before a single idea is generated. Providing clear constraints or guardrails is the best path to focused innovation, not an open field.

Non-negotiable filter criteria:

- Customer Fit: it must address a documented problem of our end-user persona

- Channel Fit: the innovation must be commercializable through our existing commercial channel

- Operational Fit: it must require the same set of skills to operate as our average product

- Infrastructure Fit: it must be usable by our target customer without additional infrastructure

Constraints are the signal that focuses creativity on the problems worth solving. An idea that fails the constraints is not a bad idea; it is the right idea for someone else. Effective innovation is the art of knowing what we will not do. Mastering contextual synthesis is what elevates an innovator into the 100 out of 10,000 that Price’s Law refers to.

The most valuable innovation trade secret is the context that helps you identify the right disruptive idea to execute and avoid repeating past innovation pitfalls.

Are you spending your week sifting through a haystack of suggestions, or are you defining the context that makes the needle obvious?

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Additional Reading

• The Hard Truth About Innovative Cultures — Gary Pisano, HBR (January–February 2019) (https://hbr.org/2019/01/the-hard-truth-about-innovative-cultures)

• The Innovator’s DNA — HBR (2009) (https://hbr.org/2009/12/the-innovators-dna)

• Innovation Starts with Defining the Right Constraints — HBR (April 2021)(https://hbr.org/2021/04/innovation-starts-with-defining-the-right-constraints)

If your goal is engagement, optimizing for reach, likes, and the safety of an approving echo chamber, that is performance, not expertise.

But if your purpose is transformation, driving impact where ambiguity creates paralysis, the SME has never been more relevant.

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The Library is a Trap

Imagine you are isolated in a library. You’ve read every volume. You are the undisputed eminence of the archives. Then you step outside.

Have you changed a single decision or influenced a single soul? If you haven’t, then you aren’t an expert. You’re a database.

The “well-read” expert is obsolete. We generate 400 million terabytes of data daily. Every 24 hours, we create 20 million “Library of Congress” worth of noise.

Much of this is Synthetic Noise—AI-generated content that rehashes and remixes existing data into polished but derivative formats. This creates a feedback loop that increases the volume of information while diluting the signal of original thought.

Accumulated knowledge is merely Potential Energy. It sits on the shelf, seasoned by experience, capable of doing work, but doing none of it. In the modern corporate environment, potential energy is a depreciating asset.

An SME who accumulates knowledge without converting it into impact is a highly compensated librarian.

From Potential to Kinetic: Closing the Gap

Before AI, an SME’s value was the “knowledge moat”, the delta between what you knew and what the “non-experts” could find. That moat has been filled in. Today, you will always know a smaller fraction of your field than you did yesterday.

This isn’t a crisis of expertise; it’s a shift toward value realization.

Expertise must move from Potential (what you know) to Kinetic (what you do with it). In a world of infinite data, value is realized only at the moment of filter and choice. If the knowledge doesn’t change the business’s trajectory, it remains a stranded asset on the balance sheet.

Generating Knowledge Alpha

In investment, Alpha is the return generated by judgment and strategy above the market baseline. For the SME, the “market” is the infinite, commoditized information available to anyone with a ChatGPT prompt.

Knowledge Alpha is your ability to separate signal from noise. It is the judgment to see the one structural shift buried under a mountain of hype.

Take the PFAS “hype cycle.” Most of what we see is noise, rehashed regulatory updates and general “awareness.”

• The Librarian: Knows the regulations are tightening and can cite the sub-classes.

• The Kinetic SME: Tells a Leadership Team: “Don’t try to time this market. Only these three technology paths survive a ten-year horizon regardless of the next election. Here is the portfolio we need to address the ambiguity now.”

Generating this Alpha requires leaving the “knowledge chamber.” You must look into other industries or geographies to see patterns that your sector hasn’t recognized yet. How did the semiconductor industry handle similar trace-contaminant mandates? What is happening in jurisdictions where regulation is five years ahead of the curve?

The Judgment Gate: The Discipline of Slowness

Transformation is kinetic. But kinetic energy requires direction, or it’s just an explosion, energy wasted without progress.

We are entering an era of “Synthetic Optimism.” It is now trivial for a project lead to generate a polished AI-backed business case that looks comprehensive and confident. In a Product Gate review, a room quickly aligns because the AI-provided feedback feels “validated.”

This is where the SME must become the Judgment Gate.

Picture a Due Diligence meeting for a $15M acquisition of a sensor startup. The deck is flawless. The projections are synthetic and perfect. The room is nodding. The SME raises a hand:

“The sensor is disruptive, but our commercial channel doesn’t have the technical depth to sell it. We’re looking at a three-year sales-force overhaul that isn’t in this budget. Without that, this is a $15M write-off.”

In that moment, potential energy becomes kinetic. You aren’t providing a fact; you’re providing the friction that prevents a disaster.

This requires the Discipline of Slowness. In a culture of “fail fast,” the expert is the friction that prevents a disaster. Speed without direction is just a faster way to hit a wall. The SME’s job is to slow the room down until the direction is clear.

Calibrating the Filter

The danger of the Kinetic SME is becoming a bottleneck or a cynic. To generate Alpha, your filter must be calibrated, not just loud.

A kinetic SME uses AI not to generate answers, but to stress-test their own judgment. Use “synthetic noise” to identify blind spots in your experience. If AI can generate a more compelling counter-argument than you can, your “expertise” is still in the library. You can work with AI to be your thought partner, challenge your rationales, and leverage your self-awareness.

A Reflection on Alignment

We must be honest: many corporate cultures reward the Librarian. They are “easy to work with” because they provide safe answers that validate the existing plan.

If you find yourself in a low-friction culture that promotes Librarians, recognize the misalignment. I won’t offer a platitude about “fixing the culture.” But I will advise against living a professional life that isn’t aligned with your purpose. Playing the Librarian when you are built for Transformation is a recipe for well-compensated resentment.

The New Mandate

The shift for the SME is fundamental: move from having the right answers to asking the right questions.

The expert who performs in an echo chamber adds to the noise. The expert who introduces the necessary friction into the decision room—holding the line on reality even when the wind is blowing the other way—is the one who helps shape the organization’s future.

In a high-stakes environment, an SME who doesn’t influence a decision is just an overhead cost. The value was never in the years you spent learning; it was in the judgment those years earned you.

Use it.

I also admire the founder who convinces a utility to try something new, but the fact is that pilots do not build infrastructure.

In water, innovation is not complete until it becomes boringly reliable.

And that is where the real work begins.

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Every Actor in the Ecosystem Absorbs a Different Risk

The water sector has an innovation ecosystem that has been growing rapidly and diversifying over the last 10 years, from accelerators and venture capital to non-profits supporting pilots, universities mentoring research teams, and large corporations getting involved earlier in the development cycle.

Each of these players has a different role in the ecosystem:

• Early-stage investors absorb technical risk

• Venture capital absorbs growth optimism.

• Early adopters absorb experimentation risk.

• And by the time a technology reaches a large corporation, a significant portion of uncertainty has already been reduced.

But a different type of risk remains:

• Repeatability.

• Global scalability.

• Operational liability.

• Brand trust.

• Reputational exposure.

In infrastructure markets, reliability is not a feature. It is the product.

Large corporations are often the last institutional filter before innovation becomes embedded in critical systems. And at that stage, optimism is no longer the currency. Discipline is.

The Unromantic Work of Industrialization

I have been personally responsible for scaling up new ventures acquired by a large corporation multiple times in the water sector. In all cases, the technology was strong. The market need was clear. The growth expectations were embedded in the valuation.

In all these cases, the new venture team had built success through customization: “Tell us what you need, and we will build it.”

This customer's flexible approach supported growth in the early stage, but it was not designed for the next stage: accelerated growth.

To justify the investment, we needed to scale globally. That required a shift from craftsmanship to playbook, a 20/80 model where we standardize the core 20% of the applications to solve 80% of the global customer problems that drive exponential growth. This moves the new venture from solving for one to scaling for all.

As we went through the scale-up growth acceleration, the technology did not change. The operating model did.

Scaling did not require better chemistry, it required disciplined narrowing. The instinct that gets you to $10M can prevent you from reaching $100M.

That lesson is not unique to one company. It is fundamental to water innovation.

Innovation Is a Series of Trade-Offs

We often describe innovation as expansion — new applications, new verticals, new markets.

In reality, innovation is a series of trade-offs:

• Which problem is large enough to matter?

• Which customer segment is ready to act?

• Which channel can reliably support deployment?

• Which use cases do we intentionally exclude?

Early-stage narratives emphasize optionality: “This technology can solve many problems.”

Late-stage scaling requires prioritization: “Which problem will we solve exceptionally well, globally, and repeatedly?”

If we bring this “last frontier” trade-off to the front-end, we build new ventures designed for global scale instead of niche brilliance.

The fact is that not all problems a technology can solve are worth building a business around.

Disciplined exclusion is not conservatism. It is capital allocation. The early-stage innovation ecosystem often celebrates ‘Yes’, but global scale is built on the word ‘No’. In a sector with long memories, saying ‘no’ to a marginal application is the highest form of respect for the technology’s long-term potential.

The View from the Edge: Global Problem Discovery

While Corporate innovators are sometimes accused of being conservative., we bring to the ecosystem a global view on customer problems. New Ventures will usually solve a specific customer problem at point A; corporate players bring the perspective of points B through Z across different regulatory frameworks and geographies.

When a new venture reaches a stage where it can engage a corporate innovator, optimism has already played its role.

Our responsibility at that point is different; we must convert possibility into priority, promise into repeatability, and pilots into platforms.

When dealing with the front end of the technology cycle, we as a whole understand that only 1 out of 10 opportunities is worth committing global infrastructure behind. The ecosystem becomes more efficient if we can address the remaining 9 challenges, so they can pivot if they so decide and make better use of their capital and time.

Our role is not to explain to the ecosystem how to de-risk an early-stage venture; other members have that role. But we have a responsibility to share the “unfiltered reality” of what global customers are willing to pay, the changes they are open to, and the effort they will invest in maintaining a new technology.

This is not the most glamorous role in the ecosystem, but it is essential if we want innovation to become infrastructure.

A Sector Evolving Fast to Solve Long Term Problems

The water sector does have a fast-evolving and expanding innovation ecosystem.

Humility is not a weakness at this stage. It is a prerequisite for maturity.

If we want durable capital in water — patient, disciplined, long-term capital — we must respect the complexity of scaling and manage the narrative accordingly.

Transformation is not a leap. It is a sequence of aligned decisions.

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1. When technology isn’t the problem 

For years before COVID, most corporations had access to the same collaboration tools we now take for granted — Skype was available within Microsoft, intranet discussion forums, shared drives. Yet almost no one used them. Emails ruled. Meetings ruled.

It wasn’t until the global lockdown forced us to work apart that asynchronous chat finally became mainstream. The technology hadn’t changed. We had.

That episode taught me something I had missed for years: innovation doesn’t fail because the technology isn’t ready; it fails because we aren’t ready to change our behavior.

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2. The two phases of innovation

We tend to define success as soon as we can prove that a new technology works.

• We find a customer pain point.

• We design a solution that creates measurable value.

• We validate that the solution delivers that value.

Then we assume adoption will follow automatically.

But between proof of concept and scaling adoption lies a valley — a utility deficit. It’s the temporary loss of value organizations experience while people unlearn the old way and learn the new one. During this period, productivity, confidence, and morale can all dip below the pre-innovation baseline.

Only once new habits settle in does the promised utility re-emerge.

3. The real work of innovators

Inside corporations, intrapreneurs often celebrate too early. They’ve proven the technology. They’ve secured executive alignment. But the hard part — the behavioral transformation of the organization — is still ahead.

In reality, a successful pilot will give you access to 2.5% of the market (the innovators), this is the group that are risk takers and adopting an innovative technology creates value on its own, so it compensates the pain of change. The remaining 97.5% requires change management: aligning leaders, retraining teams, re-wiring incentives, and giving people time to rebuild their routines around the new tool.

Without that commitment, even brilliant pilots stall at the edge of the lab.

4. The view from startups and investors

Start-ups often misread this same dynamic. They design for product-market fit but ignore habit-market fit. They underestimate how long customers will take to rebuild workflows — especially when adoption requires unlearning.

Investors, meanwhile, price technical risk but not behavioral risk. They forecast ROI as if the benefits start immediately, without discounting for the utility deficit that sits between prototype and scale. The result: delayed revenues, slower adoption curves, and sometimes, failed expectations for otherwise solid technologies.

5. Managing the deficit: complementary investments

The path through the deficit isn’t cultural rhetoric — it’s operational.

Adoption requires complementary investments that most business cases omit:

• Training and learning curves — the time cost for users to reach proficiency.

• Process redesign — workflows, handoffs, and data flows that must change.

• New metrics and KPIs — measuring new behaviors, not just outcomes.

• Data and infrastructure — ensuring the new system connects seamlessly with the old.

Ignoring these costs doesn’t make them disappear; it just pushes them downstream, where they appear as “resistance.”

6. Not all innovations are equal 

Incremental technologies — say, a new version of a familiar tool — create only small behavioral shifts. The utility deficit is narrow and shallow.

Disruptive or workflow-changing technologies — think electric vehicles replacing combustion engines, or AI assistants redesigning daily work — require deeper habit changes and generate a wider, longer deficit.

That doesn’t mean they’re not worth pursuing. It just means innovators and leaders must be realistic about the size of the behavioral gap they need to cross — and invest accordingly.

7. Measuring adoption: when “usage” isn’t enough

Traditional adoption metrics (log-ins, licenses, uptime) can be misleading. They measure compliance, not commitment.

Better signals that a new behavior is sticking include:

• Time-to-proficiency: how long before a user can perform key tasks confidently?

• Feature-level adoption: are users exploring advanced features, or just the minimum?

• Usage frequency thresholds: how often must the tool be used before it becomes habit?

• Workflow migration rate: what percentage of old processes have been replaced?

• Peer coaching or organic advocacy: evidence that users are teaching others.

These indicators show not whether people have the new tool, but whether they’ve integrated it into their routines — the true marker that the deficit is closing.

8. The leadership shift

Executives don’t need to become psychologists, but they do need to treat change management as a core innovation competency. 

That means embedding change leaders inside innovation teams, budgeting for behavioral transition, and celebrating adoption milestones as seriously as technical ones. 

Ultimately, innovation isn’t only about creating the new — it’s about helping people unlearn the old.

Literature Box — Where This Fits in the Canon

• The Productivity J-Curve (Brynjolfsson et al., MIT): shows that performance often dips before improving after new tech adoption, validating the utility deficit idea.

• Behavioral economics (Kahneman, Tversky): status-quo bias explains why individuals resist switching even when benefits are clear.

• Organizational theory (Hannan & Freeman): structural inertia causes organizations to lag in adapting routines and processes.

• Diffusion of Innovations (Rogers; Moore’s Chasm): identifies adopter segments but doesn’t address the behavioral cost of crossing them.

• Change management (Kotter, HBR): outlines the steps for large-scale transformation but rarely linked to innovation pipelines.

• Complementary assets & absorptive capacity (Teece, Cohen & Levinthal): quantify the hidden investments required to realize value from new tech.

• Habit formation research (Clear, Fogg, Eyal): demonstrates how repetition and friction reduction convert usage into behavior. 

Together, these literatures confirm the reality of the deficit you describe — but none integrates them as a single framework for innovation leadership. That integration is your contribution.

Closing thought

We spend vast resources proving that our innovations work.

But the real test of innovation is whether people change.

Until we learn to budget for the behavioral journey — not just the prototype — we’ll keep mistaking technological progress for transformation.

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The first was the announcement of María Corina Machado’s Nobel Prize — a recognition of courage and conviction in the face of oppression.

The second, tentative signs of progress in peace talks between Israel and Hamas — fragile threads of dialogue after months of anger and vengeance.

And the third, a Substack article, “A Stoic Meditation on Anger”, reflects on how easily our emotions can narrow our view of the world and cause us to surrender our wisdom to impulse.

At first glance, these items seemed to have little in common. Yet, as I sat with them, I realized they all pointed to the same truth — our ongoing struggle to master our emotions before they master us.

Whether leading a nation, an organization, or a team, our reactions — how we choose to respond to opposition and threat — reveal who we truly are as leaders.

From Instinct to Intention

Our brains were designed for survival, not for leadership.

Millennia ago, when our ancestors roamed open plains, survival depended on instant reactions to perceived threats. The flash of movement in the grass could be a predator; hesitation meant death.

Those instincts — fight, flight, or freeze — still live deep in our minds.

And today, they show up in far more sophisticated settings.

When a competitor makes a bold move, when a colleague challenges our idea, or when public opinion turns against us, our brain whispers: “You’re under attack.”

If we don’t pause, we respond the same way we did on those open plains — defensively, aggressively, and often short-sightedly.

The article on anger reminds us that wisdom begins in the pause — that brief space between stimulus and response when instinct can become intention.

Enemies, Competitors, and the Growth Mindset

In my 20+ years in corporate life, I’ve seen how often leaders treat competitors as enemies.

The language gives it away — defend our position, crush the competition, there is no true win if the other side does not lose.

It’s a mindset rooted in scarcity, one that assumes success is finite and someone else’s progress diminishes ours.

As an athlete, I’ve come to see competition differently.

In a marathon, the people running around you aren’t your enemies — they are your motivation, your mirrors, your measure.

You become better because they are there. Without them, you’re running alone.

When a competitor makes a move, I’ve learned to resist the urge to minimize or dismiss it.

Instead, I take a pause — to study, to understand, and sometimes, to appreciate the boldness of the step they took.

That pause often reveals insights that defensiveness would have blinded me to.

Learning to Pause — My Own Struggle

This understanding didn’t come naturally to me.

Many years ago, early in my executive career, I transitioned into a new company as VP of Product Management and Marketing.

It was a large global organization with teams in the U.S., France, the Netherlands, and Germany — and I felt I had something to prove.

In those early months, I approached every executive meeting as if it were a competition.

My goal was to win the argument, to demonstrate that I was right.

The VP of Engineering often became my counterpart in these battles, and my instinct was to prove him wrong — to establish authority by out-reasoning him.

Until one day, the company president, a Dutch leader known for his directness, called me into his office.

He said, “Daniel, you’re smart. Most of the time, you’re right. But what’s the point of proving that someone else is wrong?”

That single question stopped me cold.

In that instant, I could see all the moments where I had acted out of insecurity — where I had confused being right with being effective.

It didn’t change me overnight. It initiated a process that has taken years — and continues to this day.

I began to notice the surge of emotion before it showed up in my words.

I learned (slowly) that authentic leadership isn’t about proving your point, but about helping others be right — and building together from there.

Even now, I sometimes fail.

When I do, the best I can do is to recognize it, reach out, and apologize.

It’s humbling work — but it’s the work of growth.

That experience taught me something that no leadership course ever could:

If you’re talking, you’re not listening. If you’re reacting, you’re not understanding.

And without understanding, no argument — no matter how “right” — truly wins.

The Pause That Expands the Game

This morning’s article resonated with me because it addresses the gap between emotion and judgment — between what we feel and what we choose to do.

When we surrender to emotion, our world narrows; when we pause, it expands.

In the business world, that expansion means seeing beyond competition — to the broader ecosystem we all belong to.

In a culture that celebrates speed, disruption, and constant motion, choosing to pause can feel almost countercultural. Yet in innovation, clarity rarely appears in the rush. It often emerges from deliberate stillness — the moment when we slow down enough to connect insight, intuition, and intention.

The more I work in innovation, the more evident this becomes.

Every company, every startup, every experiment contributes to a shared future.

When we operate from fear or ego, we isolate ourselves.

But when we pause to learn from others — even those we once considered rivals — we strengthen the whole system.

And perhaps this lesson goes beyond business.

Everywhere we look — in politics, in communities, in our personal lives — we can see the consequences of unexamined emotion.

When anger and vengeance drive action, bridges burn and opportunities vanish.

But when we hold that impulse long enough to understand it, we open the possibility of peace, progress, and shared growth.

I’ve sought to build relationships across the industry not out of altruism, but because I am convinced that growth is collective.

I’m still learning how to do this better — how to pause before reacting, how to listen before defending, how to collaborate before competing.

It’s a practice, not an achievement.

Because in the long run, those who learn to pause may not always win the race — but they help redefine what winning means.

💭 Reflection for readers:

When was the last time you caught yourself reacting from instinct — and what shifted when you paused instead?

If I could revisit the past, I wouldn’t try to prevent their exposure to social media, as technological advancement is inevitable. However, I would manage it differently.

Today, we are experiencing another significant shift with the emergence of AI. In this context, it is essential to consider what might be at risk. One key concern is the potential impact on the next generation’s critical thinking skills.

To be clear, I do not oppose AI. But if we don’t use it responsibly, I anticipate Jonathan Haidt’s next book could be titled The Thoughtless Generation.

Can AI impact our critical thinking abilities?

A recent study from MIT, Your Brain on ChatGPT: Accumulation of Cognitive Debt When Using an AI Assistant for Essay Writing Tasks (read the study here), has sparked many discussions online and also prompted my own reflections.

The study compared students using ChatGPT (LLM), Internet Search, and “Brain-Only.” Each group wrote essays over three sessions, and in a fourth session, those who returned had to rewrite a topic from their earlier sessions. The key difference? Those who had used ChatGPT switched to Brain-Only, and vice versa.

Some conclusions from the study—and my takeaways:

• When the cognitive process is offloaded to an LLM, there is an undeniable reduction of friction. The process becomes more appealing because it requires less effort. However, the downside is a cognitive cost: users don’t fully internalize the ideas and are less inclined to evaluate the LLM’s output critically.

• This suggests that overreliance on LLMs, because of the low effort they require, could limit our ability to practice critical thinking.

• Moreover, you risk becoming part of the LLM echo chamber. If you don’t question its reasoning, you may end up unthinkingly following wherever the algorithm leads you.

• On the other hand, groups that began with Brain-Only and then used an LLM showed higher metacognitive engagement. Comparing the LLM’s suggestions with their own ideas sparked critical self-reflection.

This last point is crucial: the impact of LLMs depends on how you use them. If you outsource critical thinking to an LLM, you may lose that skill—or worse, if you start relying on LLMs too early in life, you may never develop it. This is why young people are at the highest risk.

Should we worry about losing our critical thinking?

In A More Beautiful Question (2014), Warren Berger highlights the profound impact that questions can have on individuals, communities, and societies.

He reminds us of Carl Sagan’s comments on Charlie Rose (1996): 

“If we are not able to ask skeptical questions, to interrogate those who tell us that something is true, to be skeptical of those in authority, then we’re up for grabs for the next charlatan, political or religious, who comes ambling along.”

Sagan’s words are especially relevant when we consider the power we give others through our vote, which affects global security, climate change, environmental protection, social programs, and more. We cannot delegate the critical thinking behind our voting decisions; we must make the cognitive effort required. 

Critical thinking is not optional; it is a responsibility in this time of AI emergence. It’s not just about asking questions—it’s about asking the right questions, at the right time, and with the right mindset so we can change our thinking and, potentially, the world around us. These questions help us better understand the world and the flood of information coming our way. They shape how we make both large and small decisions. 

The risk of a Thoughtless Generation is further highlighted by a 2019 study from the Stanford History Education Group, Students’ Civic Online Reasoning: A National Portrait. The study found a widespread lack of critical thinking among students: when asked to evaluate the credibility of a climate change website, 96% of students failed to investigate its connections to the fossil fuel industry. Instead, they focused on superficial cues like website design, non-profit status, and the “about” page.

Being a critical thinker is not easy. Our educational system rewards students who have the answers, not those who ask the questions. Over time, this discourages the innate curiosity of four-year-olds who constantly ask “why?” But we must reignite that curiosity. We cannot move forward without questioning why we keep discussing the same issues without seeing meaningful change. Today’s generation grows up overexposed to information curated by Facebook, TikTok, and other platforms. Their minds are shaped by the stories these companies want to tell them. Would it make a difference if they started exercising their critical thinking? I believe it would. If we raise our hands in a classroom, in a meeting, or in a community gathering and ask “why?”, and keep asking “why?”, change will happen.

My path forward with AI

I cannot say whether there is a single “right” way to use AI, but there is certainly a responsible way.

In today’s world, knowledge has become a commodity, and we are inundated with it. As knowledge grows exponentially, the portion we can fully grasp constantly shrinks. We should leverage AI’s capabilities to handle some cognitive tasks.

But now, more than ever, we also need the analytical questioning skills of a good critical thinker—someone who can sift through information and discern what makes sense and what doesn’t, what’s true or false, and what’s truly relevant.

I personally use AI as a thought partner. I develop my ideas and have AI challenge my positions, helping me find blind spots in my reasoning. I have been following without knowing the logic of the MIT study: Brain-Only first, then LLM.

With others, including my family, I am learning to ask A more beautiful question, developing a culture of critical thinking and creating a safe environment to ask “why.”

Call to Action

How will you use AI in your life?

Will you let it do your thinking for you, or will you challenge yourself—and others—to ask better questions?

Have you asked Why lately?

Share your reflections with a friend or family member and start a conversation about critical thinking.

Change starts with one question.

Stop Trying to Win Innovation

What if your best innovation strategy might be to stop trying to "win" at innovation? When viewed through Simon Sinek's infinite game lens, there is no finish line. Once you accept this truth:

• You enter a continuous learning flywheel occasionally resulting in product introductions or directional pivots

• You break the mental barrier that someone else must lose for your idea to succeed

• You see potential collaborators rather than competitors, transforming perceived threats into sources of strength

The Foundational Rule: Trust

Innovation in disruptive technologies is fundamentally a betting game. Which one of the ten emerging technologies will be adopted? The honest answer: nobody knows.

Addressing this uncertainty can only be done through a portfolio approach. With limited resources and intellectual capital, your best chance is to partner with other players in the ecosystem (new ventures, universities, and even competitors).

But, before you start thinking about the specifics of the formal partnership, you have to understand that the most essential rule in innovation partnerships isn't in the contract. Trust is foundational - without it, even detailed contracts become worthless. While protecting intellectual property matters, contracts cannot fix trust deficits.

My approach? Clear communication always includes your betting odds. Know what you want, what's negotiable, and what your non-negotiables are, and communicate them to your potential partner from day one. If trust seems questionable, walk away early.

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Playing the Infinite Game in a Short-Term World

In infrastructure-heavy sectors like water, innovation journeys typically exceed 10 years - an eternity in today's quarterly-driven business environment.

 If you want to play the infinite game, consider the following:

 For corporate innovation teams:

• Accept that disruption might happen externally, requiring diverse partnership approaches.

• Remember that reputation is currency - large corporations must engage respectfully with smaller ventures.

• Take ownership of clear communication, set the expectations and walk away if there is no alignment.

 For startups:

• Own the responsibility of understanding what each player in the ecosystem can bring to your organization (i.e., accelerator, VC, large corporation). This will help you define what you should focus on the partnership discussion

• Build relationships focusing on creating value beyond immediate revenue demands.

• Recognize that partnerships with established players may be more valuable for navigating complex industry frameworks (i.e., regulations, customer agreements, etc.) than for short-term gains.

From Philosophy to Practice

So how do we move these concepts from philosophy to practice? Here are the approaches I've found most effective: 

• Create psychological safety zones where teams can take risks and share failures

• Mix internal R&D with external partnerships and ecosystem participation

• Practice radical transparency about goals and constraints

• Develop systematic ecosystem intelligence about what's working and failing

The Water Connection 

There's something about water that makes these principles especially resonant. Water connects us all. It flows across boundaries. It sustains life.

The water challenges we face—scarcity, quality issues, infrastructure gaps—require ecosystem approaches that the infinite game philosophy encourages.

By embracing collaboration over competition, learning from failure, building trust-based partnerships, and maintaining a long-term perspective, we contribute to our shared water future.

Isn't that a game worth playing?

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Picture a disruptive technology killed before it is assessed for its potential application because the innovation team could not present a high-level revenue forecast.

In this article, I explore how Kahneman's thinking systems offer a powerful lens to bridge innovation and execution in business- and unleash the creative engine within the organization.

Kahneman, a Nobel Prize-winning psychologist, introduced in his book "Thinking, Fast and Slow" (2011) the concept of two thinking systems operating in our mind simultaneously :

• System 1:

  • Fast, intuitive, automatic, and emotional.

  • It helps us make quick decisions but is prone to biases and errors.

• System 2:

  • Slow, deliberate, logical, and effortful.

  • It's more accurate and rational, but it takes more energy.

Innovation and Execution Through Kahneman's Lens

When we introduce the role that language plays in the interaction between Systems 1 and 2, we start to see the parallel with business organizations:

• System 1 generates a creative idea, but

• System 2 struggles to put it into language right away.

• The result: we dismiss or ignore insights simply because we can't explain them yet—even though they're valid and potentially powerful.

Translating this framework to a business organization's reaction:

• The Innovation Team (System 1) proposes a disruptive idea

• The Execution Team (System 2) rejects them too early simply because they're not fully articulated, modeled, or proven.

• The Execution Team forces innovation into a System 2 box too soon, killing divergence before it can mature.

And yes, Leadership will most likely hold the Execution Team accountable for bringing a wrong idea to the market. However, if you want to avoid all errors, don't do anything—but that's the biggest mistake.

On the other hand, when the Innovation Team operates unchecked (e.g., pursuing ideas without any validation rigor or potential application), it can significantly waste organizational resources.

Tension between the two systems is not the problem; it's productive. The challenge is when there is no effective communication to advance towards the organization's objectives.

I have been on both sides of the table and have operated many times across it. I remember the frustration building as I tried to translate the potential of a breakthrough technology into a revenue forecast that didn't yet exist. The executive didn't reject the idea because it was terrible—he rejected it because his System 2 couldn't process it. It got to the point when I gave up; there was no effective communication across the table.

Bridging the Gap between System 1 & 2:

To succeed in innovation and execution, organizations must consciously manage the language and communication between The Innovation and Business Execution teams.

1. Create a common language that both Systems can use to communicate.

   1. Focusing on customer problems and pain points allows both sides of the organization to listen and speak in the same terms, removing the usual barriers.

2. Leaders must act as translators across borders.

   1. Leaders on both sides must act as translators—helping ideas evolve from intuition to articulation so the core business can understand and assess them.

   2. The Executive Team should communicate a growth narrative so the rest of the organization can align. Without clarity on the overall direction, you will only experience frustration.

3. Train Executives to Tolerate Ambiguity

   1. Innovation fluency means recognizing early-stage ideas as seeds and not dismissing them because they lack data or the ability to explain their potential fully.

   2. Encourage the business's execution side to suspend judgment, at least temporarily, when evaluating early-stage innovation.

4. Protect Divergence Before Convergence

   1. Leaders should create space (time, funding, psychological safety) for System 1-style exploration.

   2. Driving ideas convergence too early may kill those at a seed stage, requiring nurturing before being assessed against a business case.

5. Be deliberate in moving ideas from System 1 to System 2, and provide a path for tension release.

   1. The Innovation team must understand that ideas will remain an intellectual exercise unless the organization executes them and they solve a customer problem.

   2. Leaders must explain in a language both sides understand why not all ideas move into execution. Failure to do this will only increase tension within the organization, limiting the ability to bring new ideas to the market.

The next time you hear an idea that feels raw or unproven, ask yourself: Are you rejecting it because it lacks value—or simply because your System 2 can't yet see it? Don't silence System 1 before it speaks.

For many years, I have associated innovation with younger generations. We continuously see images of young entrepreneurs on social media and Fortune's 40 under 40 list of professionals representing "the next generation of leaders shaping the future.". However, after reading multiple books and reflecting on my journey, I have come to a different point of view.

I am an endurance athlete, specifically a long-distance runner, and I have been training and running for over 16 years. Throughout this journey, I have become aware of several life skills I developed. I have built grit, learned to be calm and smile during tough times, and appreciated those volunteers who made my adventure possible. I have also realized that I am not getting faster and have shifted my racing strategy to leverage my new strengths! While I may have a limited chance of beating a mid-30s athlete in a 5k race, the story changes if I get in a 50-mile trail run in the mountains.

My journey of innovation has evolved alongside my journey as a runner. As I have become a stronger runner, I have also realized that the experiences I have accumulated over the years enable me to drive change and make a meaningful impact in ways I couldn't have imagined at the start of my career.

Arthur C. Brooks' From Strength to Strength introduces a paradigm-shifting framework for professionals navigating the second half of their careers. I found it relevant for those seeking to leverage life experience into innovative breakthroughs. Brooks dismantles the myth of inevitable professional decline by revealing how shifting your cognitive focus can unlock unprecedented creative and impactful potential

Brook introduces two intelligence modes:

• Fluid Intelligence - Your Early Career Innovation Engine: the capacity for rapid problem-solving, abstract reasoning, and novel pattern recognition - which peaks between the late 20s and early 30s before entering a gradual decline. This neural horsepower drives the explosive innovations characteristic of young disruptors in fields like technology startups (average founder age: 42) and Nobel prize-winning insight scientists (peak discovery age between 30s and 40s).

• Crystallized Intelligence - The Alchemy of Experience: As fluid intelligence decreases, crystallized intelligence—the accrued repository of knowledge, contextual understanding, and cross-disciplinary connections—enters its ascendant phase. Brooks identifies this as the "second curve" where professionals can achieve what IDEO's Brendan Boyle terms "high-fidelity brainstorming" through pattern recognition honed over decades.

Brooks shares several stories about individuals trying to hold onto their fluid intelligence and get their "runner high" operating as they did when they were young instead of embracing change and taking full advantage of their new strengths.

Brooks' key message is that you have to own your journey. You have to decide to evolve your purpose from being a disruptor in your field to becoming an innovation architect (which can lead to disruption, too!). You can achieve this by leveraging your crystalized intelligence and maximizing your impact.

By deploying your new capabilities, you will be able to create exponential value by:

• Accelerating others' breakthroughs via strategic mentorship (e.g., late-career involvement with tech accelerators, VC advisor roles, etc.)

• Aligning executive and investor support to pursue high-risk initiatives (e.g., you have gained credibility over the years, made mistakes, and learned from them).

• Leveraging the network you have built over many years to explore and connect ideas across multiple knowledge silos.

For the 50+ innovator, the playbook now exists: transition from disruptive sprinter to marathoner, leveraging hard-won wisdom to solve humanity's most complex challenges. This new phase of your innovation career is not about sustaining your previous successes but pioneering new frontiers.

𝟭. 𝗜𝗻𝘃𝗲𝘀𝘁 𝗧𝗶𝗺𝗲 𝘄𝗶𝘁𝗵 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀

𝗕𝗿𝘂𝘁𝗮𝗹 𝗙𝗮𝗰𝘁: If you don't invest time with your customers, your priorities may be misplaced.

• You can't delegate customer insight. Your direct customer interactions must shape your perspectives.

• Over the years, transitioning from commercial to strategic roles, I've learned the value of forming and developing hypotheses by actively listening to customers. These interactions allowed me to understand better the actual problem (instead of its symptoms), the appropriate type of solution that solves it, and the barriers to its adoption.

• While surveys and other tools can provide statistical validation to hypotheses, your personal experience in the field is irreplaceable. It can help avoid wasting valuable resources on solutions not addressing customer needs.

𝟮. 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗟𝗲𝗮𝗱𝗲𝗿 𝘃𝘀. 𝗠𝗮𝗻𝗮𝗴𝗲𝗿

𝗕𝗿𝘂𝘁𝗮𝗹 𝗙𝗮𝗰𝘁: Your organization doesn't need another cheerleader. It needs someone brave enough to speak up when the emperor has no clothes.

• Here are some indicators of an innovation organization that is not led but managed:

- Nobody recalls when recent difficult decisions were made around innovation.

- Presence of "zombie projects" with no progress or clear kill criteria.

- Lack of passionate discussions in meetings.

• True innovation involves the risk of failure; avoiding difficult decisions is a sure path to innovation failure.

• Being an innovation leader means creating a culture of trust, listening to different stakeholders, amplifying quiet voices, and ensuring impactful decisions are made independently of how unpopular they may be.

𝟯. 𝗔𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗳𝗼𝗿 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝗳𝗼𝗿 𝗻𝗼𝘁 𝗺𝗮𝗸𝗶𝗻𝗴 𝘁𝗵𝗲𝗺 𝗲𝗮𝗿𝗹𝘆 𝗲𝗻𝗼𝘂𝗴𝗵

𝗕𝗿𝘂𝘁𝗮𝗹 𝗙𝗮𝗰𝘁: As an innovation leader, you must be comfortable with uncertainty and inherent risks.

• Over the last 15 years, I've seen the rise of complex analytical tools for prioritizing innovation investments in high-stakes innovation meetings. AI will likely increase their use.

• Despite these new tools, you are accountable for your decisions. You can't hide behind processes or tools.

• This is why being with customers and developing personal insights is priority #1.

As you enter the new year, you can embrace these brutal facts and lead with courage and conviction. Your organization's future depends on it.

Do you want to share any other brutal facts for Innovation Leaders?

I would love to hear your thoughts!🌟