Note: This briefing was originally published on LinkedIn on March 20, 2026. It has been migrated to our new home on Substack to create a complete archive. Multi-format features like video and audio commentary are available for all new briefings published from April 2026 onwards.

There is a ghost that haunts every boardroom and every strategic offsite: the ghost of initiatives past. It’s the collection of ambitious plans and three-ring binders that were launched with fanfare, only to be silently archived months later, leaving the business entirely unchanged. We’ve spent decades perfecting the art of the pilot project and the “skunkworks” team, yet an astonishing number of these efforts still today fail to create lasting value.

As we stand at the precipice of the AI era, we, as leaders, are at risk of repeating the same mistake on a more expensive and accelerated scale. We can feel the pressure to “do something” about AI, but the path forward often seems like a dense, impenetrable fog.

If there’s anything to take away from the briefings I’ve shared with you over the past several months, it’s that the only way out is through. The companies who will win in today’s AI climate aren’t going to be the ones with the most visionary, long-range plan. They won’t be the ones with the best AI models, or even the most compelling AI-powered use case.

Those organizations that succeed will be the ones who have mastered the art of the sprint. The ones who build a high “organizational metabolism” that lets them consistently turn small, focused wins into compounding institutional knowledge.

They understand that the aim of any transformation effort goes beyond the launch of a perfect AI system, but the development of an internal engine for change that endures long past any particular transformation initiative.

This is what I mean when I say this is “the end of the beginning.” The work to mastering AI isn’t about learning to “do AI.” It’s about learning to think like an AI-native leader – to see your business not as a collection of static processes to be optimized, but as a dynamic system of capabilities waiting to be re-imagined, augmented by AI.

It might seem like the path forward to helping your business become AI-native is mythical and mystical. I’d suggest something totally different: it’s no mystery. It’s a discipline:

• Diagnose your most critical business challenges

• Build human-centric systems that address them

• And codify your success so the entire organization gets smarter.

Then, do it again. That transformation flywheel you’re creating – watch where it takes you when it begins to turn.

Note: This briefing was originally published on LinkedIn on March 20, 2026. It has been migrated to our new home on Substack to create a complete archive. Multi-format features like video and audio commentary are available for all new briefings published from April 2026 onwards.

For years, we’ve been told that data is the new oil, especially in an AI climate where data fuels the very AI models that generate enormous capability.

It turns out this is only half true. Raw data is indeed a commodity, but the most precious aspect of it is derived as a by-product of data creation: it’s the proprietary knowledge your team generates when they turn that data into a successful business outcome that’s most valuable.

It’s the “why” behind the what – the hard-won wisdom that comes from navigating your organization’s unique challenges and learning from how such wisdom can be deployed to the organization’s benefit over time.

And most companies let it vanish into thin air.

I have worked with a number of aerospace companies over the course of my career, and all of them were deeply concerned with the loss of institutional knowledge. What would happen to their engineering prowess when their most senior engineers departed the firm? How would the organization best ensure younger engineers keep that knowledge alive in succeeding them?

All enduring organizations are subject to that challenge. When an employee leaves, along with it goes the institutional memory that this individual held. The same is true for every AI initiative, pilot or otherwise. What happens when the initiative is marked as “complete” by the team that led it? How would other teams and other parts of the organization know what was learned?

Without a system for capturing and sharing what was learned, the institutional memory gained from every AI project gets lost. Many organizations are capable of doing excellent work, but they may be poor at systematically learning from it.

This is often the default state for most businesses. These companies operate with a slow “organizational metabolism,” burning immense energy on individual projects but failing to convert that effort into lasting institutional strength.

In the AI-native era, a slow metabolism is a death sentence. But there is a remedy: building a lasting “learning organization,” where the goal of an AI strategy is not to complete a series of disconnected AI projects, but to develop an ingrained organizational capability that is a dedicated engine for change.

It’s an organization that recognizes its most valuable asset is the ever-growing AI playbook of what it has learned. It has a formal process for “locking in” the insights from every success and failure, ensuring that the entire organization gets smarter with each initiative.

This is what transforms a one-off AI project into a compounding institutional asset, and it’s the single greatest source of durable competitive advantage where every business is “doing AI.”

Note: This briefing was originally published on LinkedIn on March 13, 2026. It has been migrated to our new home on Substack to create a complete archive. Multi-format features like video and audio commentary are available for all new briefings published from April 2026 onwards.

There’s an old parable about a master cartographer. He spent his life creating the most detailed map of the empire ever known. It was a perfect 1:1 scale replica, so vast and so precise that it captured every road, every river, every single building. The map was a marvel of data collection. It was also completely useless. To unfold it was to cover the very empire it was meant to describe.

We are now entering the age of the 1:1 map.

With the power of AI, we can generate an almost infinite amount of data, analysis, and insights. We can have a 100-page report on a new market in seconds. We can have a real-time dashboard tracking a thousand different metrics. We have more “answers” at our fingertips than ever before.

And yet, many leaders could be feeling more lost than ever. They’re drowning in seas of data, experiencing a kind of analysis paralysis. The map has become the territory, instead of the guide it was intended to be.

Many organizations look at the act of data analysis as the impetus for “data science” or “AI” teams with remits to review reams of data and apply it to their organizations. This might come from the tacit belief that the challenge we have with using data to make better decisions comes from our inability to access or process information.

Here’s a thought: what if, instead of standing up “data teams,” everyone in an organization could be a “data sense-maker?”

As AI improves in its ability to pour through vast amounts of data, making sense of data and knowing where to attend to it remains a challenge. “What does the data mean?” is a question that will be familiar to any leader who’s been in an analytics presentation.

The opportunity is there for leaders to provide what AI can’t: to provide meaning to all the analysis being done by AI.

This signals a fundamental shift in the economics of leadership. For decades, a leader’s value was in the ability to make a decision based on experience and incomplete data. Now, as AI provides more and more of the data-driven “doing,” the leader’s value is shifting to the strategic “sensing.”

Strategic sense-making is the uniquely human ability to:

• Find the Signal in the Noise: To look at a thousand data points and identify the one that truly matters.

• Interpret Complexity: To understand that data can tell you what is happening, but it can’t tell you why.

• Create a Narrative: To weave the data into a coherent and compelling story about what to do next.

A junior analyst can use AI to summarize a report. A leader must use their judgment to determine what that summary means for the business.

This “sense-making” is human value and uniquely human context not amenable to automation. As answers become a commodity, the ability to ask the right questions and create a clear point of view may very well be the rarest and most valuable leadership skill of all.

Note: This briefing was originally published on LinkedIn on March 6, 2026. It has been migrated to our new home on Substack to create a complete archive. Multi-format features like video and audio commentary are available for all new briefings published from April 2026 onwards.

For the past two years, the dominant metaphor for AI in the enterprise has been AI as “co-pilot,” a comforting and brilliantly marketed concept. It suggests a helpful assistant, sitting at your side, ready to take over routine tasks and ease your day-to-day.

But I believe this metaphor, while well-intentioned, brings along with it an implicit bias that limits the way in which we think about the role that AI can play in our work. It encourages a master-servant relationship with technology.

We delegate our thinking, we passively accept the AI’s first answer, and we optimize for comfort and efficiency. This, as I’ve shared in the past, is a direct path to cognitive dependency and organizational fragility.

A far more powerful, albeit more challenging, model is to think of your AI not as a co-pilot, but as a sparring partner.

A co-pilot’s job is to make you comfortable. A sparring partner’s job is to make you better. A sparring partner challenges you, tests your defenses, and exposes your weaknesses in a safe environment, before you step into the real ring with a competitor or a customer.

This positions AI not just as an interface for delegation, but as a partner through which constructive dialogue is possible.

Instead of asking the AI, “What are the top 5 markets for us to enter?”, a leader in a “sparring partner” culture would say, “Our top market is Germany. Now, act as our biggest competitor and tell me why this is a terrible idea.” The goal of this interaction is not to get a faster answer. It’s to build a more robust strategy by subjecting it to rigorous, critical debate.

This is the “Socratic Dialogue” in a modern context. It’s an iterative process of questioning, challenging, and refining that uses AI’s immense knowledge base and capacity for analysis to sharpen our own human judgment.

Cultivating this culture requires a new kind of leadership. It means explicitly rewarding the employee who challenges the AI’s output, not the one who accepts it fastest. It means celebrating “productive friction” as a sign of a healthy, intelligent team. It means training your people not just on “how to prompt,” but on “how to question.”

The co-pilot model promises a smoother ride. The sparring partner model promises a stronger team. Choose wisely: do you want to build a business that’s merely efficient, or one that makes the best decisions?

Note: This briefing was originally published on LinkedIn on February 27, 2026. It has been migrated to our new home on Substack to create a complete archive. Multi-format features like video and audio commentary are available for all new briefings published from April 2026 onwards.

I recently spent time with a senior tech executive at an industrial B2B company who was deeply frustrated. His team had built an impressive AI prototype that would have fundamentally changed his organization’s ability to address inbound customer inquiries. Instead of serving customers on service loops that typically require hours, it might be possible to serve customers in minutes. But his business case to take the AI prototype further was dead on arrival.

“The CFO just doesn’t get it,” he said. “He keeps asking for a hard ROI, and nothing I share seems to land well.”

Sadly, this is a story that’s more common than maybe it should be. It’s a clash of two valid, but different, views of the world. On the one side, there is the innovator who sees a strategic opportunity. On the other, there is the steward of the balance sheet who sees an unquantified risk.

The problem isn’t the obsession with ROI, or what some might conclude as the debate between the quantitative versus qualitative aspects of operating a business. The problem is in the translation of innovation to measurable outcome.

In our rush to champion the transformative potential of AI, we often forget that “transformation” is not a line item on a balance sheet. AI-native leaders who are successful at gaining the alignment they need to do “big” things are more than visionaries. They’re translators – they’ve learned how to deconstruct their ambition into a financial story that a CFO (and Board) can analyze, measure, and ultimately invest in.

Arguably, the most obvious form of ROI is what is easily measurable: cost savings. As in, “This AI will automate X dollars of hourly labor.” But ROI can take other forms – and it’s in being able to tell the ROI story from multiple angles that turns a business case for AI from a mere transaction to a strategic endeavor.

There are at least three distinct kinds of value that can form the basis of a sophisticated presentation of the financial return on an AI investment:

1. Cost Efficiency: This is the classic ROI of cost reduction and automation. It’s the simplest, most direct part of such a business case.

2. Revenue Growth: This is AI’s potential to generate net-new revenue. It’s not just about making your existing business cheaper, but about using a new AI capability to create a new product, service, or business model. This is where you might quantify the size of a new market you can now address, or the new revenues that might emerge from an entirely new revenue stream.

3. Enterprise Value: This is the most abstract, but also the most durable form of value. This is the proprietary data and insight an AI bespoke to your organization that leads to long-term value from competitive or market differentiation and the strategic moat that might set your organization apart from others. This is a balance sheet asset capable of generating returns for years to come.

By separating the business case into these three parts, you can have a more honest and complete conversation by showing how AI creates near-term value, and enables a path to long-term, strategic value. Instead of asking for a budget, you’re presenting an investment portfolio.

Note: This briefing was originally published on LinkedIn on February 20, 2026. It has been migrated to our new home on Substack to create a complete archive. Multi-format features like video and audio commentary are available for all new briefings published from April 2026 onwards.

Imagine driving a high-performance car. You’re obsessed with the dashboard. The tachometer is screaming at 8,000 RPM, the speedometer says you’re doing 120 miles per hour, and the engine temperature is perfect. By all these metrics, your vehicle is operating at peak performance.

But then you look up, and you realize you’ve been driving in a perfect circle for an hour. You haven’t actually gotten any closer to your destination.

This is the state of AI measurement in many enterprises today.

We’re building beautiful dashboards that glow with green vanity metrics. We celebrate when our new chatbot handles 10,000 “conversations” in its first month. We high-five when our sales team “adopts” the new AI-powered CRM feature. It turns out we’ve been measuring the engine’s RPM instead of our time to destination.

The implications of chasing poor metrics are profound. Poorly-conceived metrics allow us to invest millions of dollars and thousands of hours into initiatives that may make us “feel good,” albeit only for a moment, while delivering zero business value.

While it isn’t wrong for an organization to be proud of having achieved a 90%+ adoption rate of a new AI capability, it must always be assessed in the context of the P&L. Perhaps uptake has been swift, but did it actually move the needle? Perhaps the team did play with the tool – once, but hasn’t touched it since. Such an AI dashboard would be green while the P&L remains red.

The antidote is to shift our focus from activity to capability.

AI-native leaders don’t ask, “Is the AI busy?” They ask, “Is our business getting better because of the AI?”

This requires a new type of metric. Instead of measuring the tool, you must measure the business capability the tool is supposed to improve.

• Don’t measure “chatbot queries handled.” Measure “customer retention rate” or “cost-to-serve.”

• Don’t measure “AI tool adoption.” Measure “sales cycle time” or “lead conversion rate.”

These are Capability Metrics. They’re the only metrics your board, your CFO, and your shareholders actually care about. It’s the only way to know if you’re actually moving forward, or just driving in circles with the engine screaming.

Note: This briefing was originally published on LinkedIn on February 13, 2026. It has been migrated to our new home on Substack to create a complete archive. Multi-format features like video and audio commentary are available for all new briefings published from April 2026 onwards.

For the past year, the conversation around AI governance has been dominated by a narrative of fear. We talk about risk mitigation, compliance checklists, and the potential for brand-damaging errors from “hallucinating AI” or AI “leaking” data to the outside world. We have, with good intentions, framed governance as a necessary evil — a safety brake we must apply to AI to ensure we deploy it responsibly.

This might profoundly undersell the opportunity that well-conceived AI governance creates.

While your competitors are viewing governance as a burdensome cost, a new class of AI-native leaders is in the process of making it the unique selling point of their AI-enhanced products and services. They understand a fundamental truth of this new era: as AI becomes more powerful and autonomous, the customer’s decision-making process is no longer just about the features you offer. It’s about the trust you inspire.

In this environment, the way in which you enact governance becomes more than just a set of mechanics to ensure compliance. It’s a product feature. In fact, it might be your most important one.

Consider the landscape:

• Access to Regulated Markets: In sectors like finance and healthcare, the barrier to entry isn’t technology, it’s regulatory approval. A company that can demonstrably prove its AI is safe, transparent, and fair through a robust, platform-based governance model can enter these lucrative markets faster and more effectively than a competitor with a “move fast and break things” ethos.

• Winning the Enterprise Customer: The CIO of a Fortune 500 company is not just buying your AI tool. They are buying your assurance that it will not expose them to a billion-dollar lawsuit or a front-page data breach. When two vendors offer similar features, the one with the superior, more transparent governance model will win the enterprise deal every time.

• Building Brand Equity: In a world awash with “AI slop” and concerns about data privacy, a brand that can verifiably claim “our AI is trustworthy” has a powerful and rare differentiator. Trust is becoming a premium attribute, and customers will pay for it.

The most mature organizations adopting AI are doing more than simply asking “How do we get this past legal?” They’re also asking “How do we feature our superior guardrails in our next sales pitch?” These organizations are turning their SOC 2 reports and their real-time monitoring dashboards from internal compliance documents into external marketing assets.

This is a call to stop thinking of governance as a tax on innovation. Start thinking of it as the foundation for your next premium product.

Your strongest shield can also be your sharpest sword.

Note: This briefing was originally published on LinkedIn on February 6, 2026. It has been migrated to our new home on Substack to create a complete archive. Multi-format features like video and audio commentary are available for all new briefings published from April 2026 onwards.

For the past decade, one of the most powerful concepts in business strategy has been the “Data Flywheel.” Pioneered by companies like Amazon, the logic was simple and powerful: a better product attracts more users, who generate more data, which is used to make the product even better. The company with the most data wins.

That era is over.

In the age of generative AI, the value of raw data is collapsing. The Internet is being flooded with low-quality, unreliable, AI-generated “slop.” AI models trained on this contaminated data are beginning to degrade, a process researchers call “model collapse.”

Simply having “more” data is no longer an advantage. In fact, it can be a liability if the quality is poor. The strategic high ground has shifted. The new moat isn’t built on having the most data. It’s being built on having the best process for creating proprietary data.

The last generation of “Big Data”and the “Data Flywheel” is giving rise to a new, more powerful model: the Judgment Flywheel.

If the old Data Flywheel was a giant vacuum cleaner, sucking up all available data, the Judgment Flywheel is a refinery. It takes crude, generic inputs (like an AI’s first-pass analysis) and uses a deliberate, human-centric process to refine them into high-grade, high-signal intelligence.

Here’s how it works:

1. AI Output: An AI agent makes a first-pass decision (e.g., flags a transaction). This is the crude input.

2. Human Judgment: A human expert reviews the decision and, if they override it, provides the critical “why.” This is the refining process.

3. Captured Insight: That “why” — the expert’s judgment — is captured as a structured, proprietary data point. This is the high-grade fuel.

4. Smarter AI: This new, high-quality data is fed back into your AI models, making them smarter and more contextually aware than any generic system.

The cycle repeats, with each turn making your entire system more intelligent and more aligned with the unique realities of your business. This is what creates a true, unassailable competitive advantage. The winner is no longer the company with the biggest data lake, but the one with the most effective intelligence refinery.

Note: This briefing was originally published on LinkedIn on January 30, 2026. It has been migrated to our new home on Substack to create a complete archive. Multi-format features like video and audio commentary are available for all new briefings published from April 2026 onwards.

In the world of structural engineering, there’s a critical difference between strength and resilience. You could build a bridge out of a material that is incredibly strong but perfectly rigid. It would stand flawlessly for years, right up until the day a unique, high-frequency vibration — from an unusual wind pattern or a specific traffic load — hits its resonant frequency.

The bridge would shatter catastrophically. A resilient bridge, by contrast, is designed with built-in flex and dampening systems, allowing it to bend without breaking.

Many organizations today are building “brittle bridges.”

In our quest for AI-driven efficiency, we are architecting “perfect” processes that handle 99% of cases flawlessly. We automate the invoicing system to process an exact data format. We build a chatbot to answer a specific list of questions. But this pursuit of perfection creates extreme fragility. The system works, until it doesn’t.

A supplier adds a single new field to their invoice. A customer asks a novel question. Or, a small innocuous typo appears in an input table. The whole workflow grinds to a halt.

Contrary to what we might think, this isn’t a failure of technology. It’s a failure of imagination. We’re applying an industrial-era, assembly-line mindset to a dynamic, digital world.

An AI-native organization operates with a different mindset. Instead of building processes, they design systems. A process is rigid; it breaks on exceptions. A system is adaptive; it learns from exceptions.

I saw this firsthand in my time in financial services. Banks often feel compelled to ensure nothing ever fails. And in so doing, they apply major constraints on customer interactions. To verify your identity correctly, you must provide the exact full name you used when you got your account. You must know the exact date a transaction occurred. Or, heaven forbid, there’s an Internet outage – you might have to enter your carefully provided input all over again.

A more resilient approach might consider inputs as probabilities, rather than absolute truths. “Unexpected” inputs get flagged as exceptions, but they don’t kill the transaction. They might get routed to a person – or an AI sub-agent – to see if there’s a means to disambiguate what’s been provided. But they don’t penalize the user because he might have forgotten to include his middle name.

We all know from our experience that few things in life are certain. If we’re looking to build AI workflows that move the needle, the goal isn’t to build AI that never fails. It’s to build systems that never stop improving.

Note: This briefing was originally published on LinkedIn on January 23, 2026. It has been migrated to our new home on Substack to create a complete archive. Multi-format features like video and audio commentary are available for all new briefings published from April 2026 onwards.

I recently had a conversation with a senior executive who was reminiscing about his early career, a time before desktop computers were ubiquitous. He confessed a nagging fear. “I worry,” he said, “that we’re losing our knowledge of first principles.”

“With AI, will we be able to solve problems on our own ever again?”

That’s a question I’ve pondered since. It’s timely to be sure – we see a version of this playing out in headlines about the “AI cheating epidemic” in colleges and universities, where course instructors are becoming increasingly reliant on oral exams because their confidence is waning around students’ willingness to do assignments and written tests unaided.

As one undergraduate student recently wrote in a powerful plea in Maclean’s Magazine, a culture of “copying, pasting, rephrasing and submitting” is creating an environment where software talks to software, while learning becomes an afterthought.

Other publications like the Harvard Business Review are talking about “cognitive offloading,” or the reliance of skilled talent at work on AI to do tasks they used to do just fine themselves, leading to burnout – or worse.

For enterprise leaders, the well-intentioned desire to show measurable results from our AI investments is a risk vector for creating this dynamic. We are building a generation of “AI-literate” employees who are exceptionally good at prompting an AI, but we might be failing to cultivate “AI-fluent” teams who know how and when to apply sound judgment to its output.

Cognitive offloading in an organization is the atrophy of the critical thinking, problem-solving, and first-principles reasoning that an organization needs to survive a true crisis. In the desire to become “AI-native” (or “AI-first,” pick your term), we’re creating teams that are poised to be incredibly efficient at scaling problems AI already knows how to solve, but that become dangerously fragile when faced with a novel challenge that requires a human to step up.

The antidote is not to reject AI. It’s to be more deliberate and strategic in how we deploy it. We must stop thinking of “AI-native” as a buzzword for “deploying AI everywhere.” A more practical and resilient definition is: building an organization around the unique and complementary skills of humans and AI.

How can leaders act on this today? Here are two practical places to start:

1. Design “Graceful Workflows.” Instead of building workflows that are fully dependent on AI, design them to be “graceful.” A graceful workflow is one that can be powerfully augmented by AI, but that could still be fully executed by your human team if push came to shove — if the cloud goes down or the model starts to drift. This builds resilience directly into your operations.

2. Trial with Humans First. Before you hand a critical workflow over to an AI agent, have your most trusted human experts run the process manually for a week (or more). This does two things: it ensures the workflow and customer experience actually make sense, and it allows a designated “red team” of your best people to vet the process and identify the real risks before automation scales them.

This measured approach ensures that AI serves the business, not the other way around. It allows us to gain the efficiencies of AI without sacrificing the human judgment that ensures our long-term resilience.

Note: This briefing was originally published on LinkedIn on January 16, 2026. It has been migrated to our new home on Substack to create a complete archive. Multi-format features like video and audio commentary are available for all new briefings published from April 2026 onwards.

For the better part of a century, the blueprint to becoming a successful leader was clear: you excelled at managing people. From the industrial-era supervisor ensuring tasks were completed to the corporate-era manager facilitating team success, the core function was always human-centric.

With AI on every leader’s mind, the end of that era of leadership could well be at hand.

AI systems are now capable of handling the very tasks that once defined management: tracking performance, monitoring workflows, and optimizing output. This automation of oversight doesn’t necessarily make leaders obsolete though. It liberates them to do more important work.

The challenge is that this new work requires a radically different skill set.

The old model was about getting things done through others. The new model that’s emerging is about achieving outcomes by orchestrating a complex ecosystem that includes both human talent and “AI talent.”

Leaders in this AI era are more like symphony conductors. Conductors don’t play a single instrument, but they have the ability to understand the unique capabilities of every section – the strings, the woodwinds, the percussion – orchestrating them to produce transcendent music that no single section could create alone.

Your human team might be your string section, unmatched in creativity, empathy, and strategic judgment. Your AI agents might be your percussion, capable of immense scale, speed, and pattern recognition. Your job is to write the score that allows them to play in harmony and manage the full orchestra as it performs live.

Successfully navigating this new landscape requires mastering a new set of core competencies:

1. Human-AI Collaboration Design: Blending the strengths of humans and AI.

2. Agent Management: Prompting, evaluating, and coordinating AI outputs.

3. Strategic Sense-Making: Interpreting complexity and maintaining focus amidst a sea of AI-generated data.

Leaders who insist on defining their value by the number of people they manage will quickly realize AI brings with it not just one, two, or ten direct reports – but the challenge of simultaneously managing dozens or hundreds of team contributors.

At that scale, the very notion of what it means to provide leadership must evolve. It’s in this way that the future belongs to leaders who see themselves as orchestrators of outcome.

Note: This briefing was originally published on LinkedIn on January 9, 2026. It has been migrated to our new home on Substack to create a complete archive. Multi-format features like video and audio commentary are available for all new briefings published from April 2026 onwards.

There’s little doubt 2025 was the year of the AI initiative. Enterprise businesses are estimated to have spent between $400 billion to an eye-watering $1.5 trillion on AI in the past 12 months alone.

That’s an unfathomable amount of investment — especially when just 6% of organizations report experiencing “significant” value from their AI investments.

What are the 94% who aren’t experiencing significant returns from AI doing?

They’re funding prediction models, building chatbots, and “AI-powered” features, declaring victory as soon as they can show off a demo. When these demos fail to garner support or the organizational momentum needed to carry on, they’re quietly shelved — or replaced by the next shiny object. The business itself remains unchanged.

The historical precedent for this age of AI we’re in isn’t the Internet, the smartphone, or even the desktop PC. It’s actually something many of us likely take for granted today: it’s electricity.

Let me explain: the companies that won the electric age weren’t the ones who simply bought dynamos to light up their existing, inefficient factories. The winners were the ones who realized the new technology allowed them to fundamentally redesign the factory itself — creating the modern assembly line and unlocking massive gains in productivity.

They didn’t just use electricity. Instead, they built their entire operating model around it.

This is actually the AI-native mindset.

It wasn’t too long ago that many businesses were building “innovation” or “digital” teams, proudly appointing Chief Innovation Officers or Chief Digital Officers with a mandate and deep pockets to lead sweeping organizational transformations. I spent time with one such organization some years ago, an insurance provider, who had made impressive investments to secure a leadership position in the early days of AI, even before the current wave of generative AI we’re all in today.

They had an innovation team including outstanding PhDs in AI and data science. They also had a strong portfolio of AI initiatives. But, the team was disconnected from those who owned P&L. They became a “cost center,” arms length from the actual business. Ultimately, they proved unable to generate discernable value — and were quietly disbanded.

They were a company with AI, but they were not an AI company.

A company that embraces the AI-native mindset looks at AI very differently. They view their core business processes as AI products themselves. A shipping company’s product isn’t “moving boxes,” but rather, is a dynamic engine for “logistics optimization,” for example. These businesses don’t look at AI like it’s a technology you buy, but more like AI is an integral part of the very infrastructure that enables the business to operate.

That 6% of organizations who are seeing significant growth in their EBIT from AI also happen to be the ones actively seeking the AI-native mindset. They’re the ones redesigning processes and workflows, targeting areas where they can scale quickly, and making investments to transform the organization, not just their tech stacks.

Adopting an AI-native mindset means making a fundamental shift in how you invest in AI, who you hire, and where you create value. The call to action is surprisingly simple.

Stop funding demos. Start rewiring your factory.

Note: This briefing was originally published on LinkedIn on December 19, 2025. It has been migrated to our new home on Substack to create a complete archive. Multi-format features like video and audio commentary are available for all new briefings published from April 2026 onwards.

There’s a cynicism gripping the market, and it’s not hard to understand why.

For years, the narrative of business has been dominated by a singular focus: delivering shareholder returns by extracting maximum value for minimal effort. This has led to a consumer landscape where it’s easy to feel constantly nickeled and dimed, sold products that feel less durable, and served by systems that prioritize cost-cutting over customer experience.

Now, into this environment, we’ve introduced AI. It maybe isn’t a surprise then that in some corners of the market, the reaction to AI has been one of skepticism.

When companies deploy AI to automate an already frustrating customer service experience, consumers react accordingly. They ask the question: “What exactly am I paying for?” They may see AI not as an innovation, but as the next logical step in a long history of companies trying to do as little as possible.

This is today’s great, unspoken tension. And it’s why the mainstay playbook of business could well be on the cusp of implosion.

I’m reminded of a lesson from early in my career in financial services. A wise senior leader shared with me that heavily regulated industries, such as banking, are sometimes slow to change because those in charge have become stewards of a system they grew up with. Such leaders have a hard time envisioning a world where the system they were taught to be “the way things are done” might one day no longer exist.

That same dynamic isn’t unique to banking. It’s playing out across every industry today. Leaders are clinging to an outdated playbook, not because it’s effective, but because it’s familiar.

But a new playbook is being written. Its central tenet is not efficiency, but value creation.

The old playbook asked: “How can we optimize this process?”

The new playbook asks: “What new form of value can we create that makes the old process obsolete?”

This is the strategic shift that AI enables. AI is not just a tool for optimization; it’s an engine for imagination. It offers us the chance to partner with a non-human intelligence, a creative collaborator that can help us reframe problems we once thought impossible to solve. It allows us to offload the work we are merely competent at, freeing us to flourish in the areas where we are truly brilliant.

As we head into 2026, the question is not whether we will face challenges — we will. The question is what direction we’ll choose to pursue.

Will we continue to defend pre-AI ways of thinking that are becoming increasingly irrelevant? Or will we embrace the disruption, carve out new ways of doing things, and, in the process, discover new ways to define success?

The future that awaits us will be decided by the clash of these two playbooks. Will we believe the world can never be any different? Or will we believe that the future ahead is what we make of it?

Our only limitations will be the ones we place on ourselves.

Note: This briefing was originally published on LinkedIn on December 12, 2025. It has been migrated to our new home on Substack to create a complete archive. Multi-format features like video and audio commentary are available for all new briefings published from April 2026 onwards.

There’s an old joke in computer science that the way to solve any complex problem is simply to abstract it and make it the next layer’s problem.

For decades, the corporate world has applied this same logic to organizational design. When a business becomes too complex for one leader to manage, we “abstract” the problem by hiring a layer of managers. When those managers get overwhelmed, we hire directors to manage them. We solve the complexity of scale by building a human hierarchy.

It works, but at a steep cost. We trade agility for control. We create a “frozen middle,” a thick layer of translation where strategy gets diluted and ground truth gets distorted before it ever reaches the C-suite.

But today, in this era of AI we’re in, we’re seeing the signals of a profound reversal.

In October, Amazon announced it was cutting 14,000 corporate roles in a bid to build flatter hierarchies across its corporate organization and invest more heavily in AI. Life sciences giant Bayer AG was also recently in the news touting its operating model, one of “Dynamic Shared Ownership,” where up to 95% of the organization’s decision making has been pushed to the front lines in a trend that some are calling the “Great Flattening.”

This is organizational thinking that goes beyond layoffs or efficiency. It is a statement of belief in the potential for AI to enable an architectural upgrade of the corporation itself.

The New Abstraction Layer

What’s obvious to a computer scientist isn’t always obvious in business: the goal of abstraction isn’t to add more people. It’s to hide complexity so you can focus on the result.

In the past, “abstracting complexity” meant hiring a middle manager to summarize weekly reports from ten junior employees. The manager was the API.

Today, agentic AI is becoming that abstraction layer.

Instead of relying on a deep organizational structure to filter information, leaders can now insert AI into their business models to observe results and take action directly.

• The Way It’s Done Today: A CEO waits for a quarterly report, synthesized by three layers of management, to know if a product launch is working.

• The “Founder Mode” Way: An AI agent monitors customer sentiment, sales velocity, and support tickets in real-time, flagging anomalies directly to leadership the moment they happen.

This achieves more than the flattening of an organization. It compresses the distance between the leader’s intent and the organization’s action. It allows a $500 million enterprise to operate with the visceral, hands-on agility of a $5 million startup.

The Small Business Giant

This shift frames the future of work we are heading toward. The “AI-native” organization of 2026 will look less like the sluggish incumbents of the Fortune 500 and could look more like a massive network of small, agile businesses.

These organizations will be leaner, yes. But more importantly, they will be “sludge-free.” They will use AI not just to automate tasks, but to automate the coordination of tasks — the very thing that middle management was invented to do.

For leaders, this is the ultimate AI unlock. It means you may no longer have to choose between scale and speed. You could have the reach of a giant with the soul of a founder.

Note: This briefing was originally published on LinkedIn on December 5, 2025. It has been migrated to our new home on Substack to create a complete archive. Multi-format features like video and audio commentary are available for all new briefings published from April 2026 onwards.

I once had a customer, a senior leader on the cusp of retirement, who told me, “I’m sure our company must change and embrace new technologies. But not while I’m still here and have anything to say about it.”

It was a bold, candid admission. It’s also a perfect illustration of the perilous, personality-driven environment in which many organizations operate, where real transformation opportunities often go to die.

Today, we see this kind of human friction creating a new stalemate in the face of organizations seeking to wrangle and reason about AI. A polarized, all-or-nothing debate around AI has emerged, trapping organizations in a cycle of inaction. This isn’t an “AI divide” born from technology, but a leadership divide born from hubris.

On one side, we have “AI shamers.” These are the folks who proudly point out the perceived tics of AI-written content, as if spotting an em-dash or a certain prosaic pattern makes them a guardian of authenticity. They’re also the ones who fear most the decline of work quality through reliance on AI as a crutch.

On the other, we have “AI evangelists” who insist AI must immediately take over all “low-value” work, a dangerously subjective term. Who, exactly, gets to decide what’s “low-value?” They’re the ones who fear most about becoming irrelevant and being left behind.

This binary thinking — this “my way or the highway” mindset — is a failure mode. It’s the same impulse that drives leaders to tear down the ideas of others just to assert their own authority. Much has been said in the past year about the importance of executive alignment in driving successful AI adoption, and yet, many organizations struggle to achieve said alignment because debates around AI can be so charged they turn into sparring matches over who’s right and who’s wrong instead.

This is where the real villain of AI transformation is exposed: the people-driven impulse that values being right over getting it right.

Pragmatism teaches us neither polar position is correct. Leadership wisdom suggests resisting a simple “yes” or “no” to AI gets us to wrangle a more difficult, nuanced answer: “It depends.”

In this context, “it depends” isn’t an act of avoidance but the start of a strategic diagnosis about what role AI plays inside an organization, and where and how it can be employed to achieve the best outcomes. And equally important, what areas are off-limits to AI:

• It depends on the problem you’re solving.

• It depends on the risk of the workflow.

• It depends on the data you have.

• It depends on the business outcome you need.

For example, using AI to co-author a low-risk internal memo is a completely different strategic decision than deploying an agent to autonomously handle a high-risk financial compliance workflow.

One outcome values speed and “good enough” efficiency while the other demands 100% accuracy, where a single error or hallucination could be catastrophic. Without asking when and why, leaders are just blindly adopting technology (or just as blindly, refusing to consider it), rather than strategically deploying it.

The most valuable leaders in the next decade won’t be the ones who can spot an AI-generated email. They will be the ones who fight hard for the alignment that requires teams, leaders, and organizations to get past hubris and force a dialogue that confronts the real issues at hand.

The answer to “Should we use AI for...?” isn’t “yes” or “no.” It’s “Here’s when.”

Note: This briefing was originally published on LinkedIn on November 28, 2025. It has been migrated to our new home on Substack to create a complete archive. Multi-format features like video and audio commentary are available for all new briefings published from April 2026 onwards.

There’s a philosophical debate raging right now, sparked by articles like one I recently read in the New Yorker: Is AI really thinking, or is it just a cleverly complex mimic?

It’s a captivating question. For those of us leading a business, maybe the real question isn’t “What is AI?” but “What do we believe it is?”

How your organization answers this question or, more likely, how it avoids answering it could define your entire approach to AI. Over the past year, I’ve observed that the business world has fractured into at least three distinct camps, each defined by its own implicit point of view on what AI’s role in business is:

1. The Status-Quo Champions: These are leaders who claim to champion AI but are really only interested in maintaining the status quo. They use AI as “innovation theater.” They’ll fund a small, contained chatbot pilot that goes nowhere. For this group, AI isn’t a tool. It’s a topic for a press release. Their unstated belief is that AI is a mild force, one that can be managed and contained within an existing way of working, broken processes and all.

2. The Exploiters: These are the people seeking to exploit AI because they think it’s a path to a quick dollar. They see AI as a crude automation tool, a way to cut costs and replace headcount. They aren’t interested in transformation, only in extraction. Their unstated belief is that AI is a blunt instrument, and they’re all too happy to wield it, often damaging customer trust and employee morale in the process.

3. The AGI Worshippers: These are the people waiting for divine inspiration. They believe AI’s true homecoming will arrive with the emergence of Artificial General Intelligence (AGI). Until that day of rapture, they argue, it’s all just “wait and see.” Their unstated belief is that AI is a spectator sport. They are paralyzed, waiting for a future event rather than acting in the present.

The one thing all three camps have in common? They’re all, in their own way, passively waiting to be told what AI is. They’re letting the hype, the fear, or inertia dictate their strategy.

This is a failure of leadership. To paraphrase Cisco’s John Chambers: “Disruption waits for no one.” While it may feel prudent to wait and see how things play out, leaders must be asking these questions now, not later in response to a disruption that’s already taken place.

So, what’s the alternative?

The alternative is to decide. The best leaders aren’t waiting to be told what AI is. They’re actively deciding what role AI will play in their organization and building a strategy to match.

Here are three things you can do today to position AI effectively in your organization and evergreen your business for the disruptions we don’t even know about yet:

• Open Your Eyes: Encourage experimentation. Assume the next disruption could happen tomorrow. The goal isn’t to find a single, perfect use case. The goal is to build your team’s “AI fluency.” Give them the permission and the safety to explore, to test, and to fail small so they can learn fast.

• Tune Off the Hype: Come to your own conclusions. Ground your strategy in the reality of where your organization is today and what ultimate value it could be creating. The market-at-large doesn’t know your business, your customers, or your team. Stop listening to the hype that tells you what AI “should” be, and start defining what it will be for you and your organization.

• Be the Disruptor: Don’t wait for change to come so you can react to it. Take the initiative and forge the path that lets your organization be the disruption, rather than the one being disrupted. Identify your own most valuable, complex process and ask, “What if we were the ones to make this obsolete?”

We started this article by asking what AI really is. Here’s the bottom line: AI is what we make of it.

We can either be told by others what AI is, or we can, as leaders, decide. The choice is ours.

Note: This briefing was originally published on LinkedIn on November 21, 2025. It has been migrated to our new home on Substack to create a complete archive. Multi-format features like video and audio commentary are available for all new briefings published from April 2026 onwards.

In the face of massive technological change, every business leader is eventually forced to answer one very unexpected question: Is your organization a Nokia or a Nintendo?

Nokia is, arguably, the quintessential case study in complacency. Once so dominant that “Nokia” was synonymous with “mobile phone,” the company’s roots were actually in 19th-century paper and rubber goods. They were, for a time, masters of reinvention. But at the peak of their success, they fell into a trap: they dismissed the first iPhone not as a revolutionary ecosystem, but as a niche, expensive toy.

This isn’t just a historical case study for me. I saw this firsthand during my time there.

From the inside, one would have seen that there wasn’t a single moment of failure, but a continuous cultural shift that played out every day. Nokia had become trapped by its own success. Internal teams focused on optimizing what the organization believed was most important to consumers: hardware durability, call clarity, and battery life. The internal narrative, reinforced in meeting after meeting, was that the iPhone’s “flaws” — a fragile glass screen, poor battery, and no physical keyboard — made it a non-starter for “serious” users.

Nokia was executing a perfect plan for a world that was about to disappear. Data and past successes didn’t so much blind the organization rather than cause it to focus on the wrong things. At times, that’s what complacency feels like: a failure of imagination, borne from the comfort of success. The company was disrupted because it stood still.

Then there’s Nintendo. Their story is not one of flawless, linear victory. They also started in the 19th century, making playing cards. They dominated the 80s and 90s, but their history since has been a mix of blunders (like the Virtual Boy) and industry-defining successes (like the Nintendo Switch). Earlier this year, Nintendo launched the Switch 2, which claimed the record of being the fastest selling home video game console of all time.

The key difference is this: Nintendo never stopped trying to reinvent. They embody imperfect, relentless action.

This distinction is the entire lesson for leaders navigating the age of AI. The anxiety so many feel doesn’t come from the technology itself. It comes from the organizational paralysis in the face of it. It’s the fear of being a Nokia.

The prevailing winds today suggest AI may excel at taking exams but struggles with basic arithmetic. It’s tempting to see these limitations as a shield, a reason to wait and see, just as Nokia waited to see if the smartphone vision of the iPhone would bear out. But history shows this is a fallacy. Relying on today’s limitations to predict tomorrow’s capabilities is how incumbents get blindsided.

The antidote isn’t a perfect, multi-year strategic plan. It lands on the wisdom that “a good plan executed today is better than a perfect plan next week,” to paraphrase World War II General George Patton.

Execution, however imperfect, is the only antidote to disruption.

Pragmatic leaders who internalize this share three core habits. They exemplify the disciplined, daily work of fighting complacency.

1. They Actively Reject the Status Quo. “It’s the way we’ve always done things” is a warning sign. These leaders choose, instead, to embody the spirit of a George Bernard Shaw quote, most famously used by Robert F. Kennedy: “Some men see things as they are and ask, ‘Why?’ I dream of things that never were and ask, ‘Why not?’“

2. They Never Rest on Their Laurels. Intel’s legendary co-founder and CEO, Andy Grove, built his entire leadership philosophy on this idea, famously captured in the title of his book: “Only the Paranoid Survive.” This isn’t about anxious fear, but rather the relentless, productive vigilance against the inertia that success breeds.

3. They Master the “Art of the Possible.” Bill Gates’ vision of a “PC on every desk” seemed absurd at the time. It didn’t happen overnight. It was the result of pairing a huge ambition with “eminently doable” next steps, executed relentlessly. This is the very definition of pragmatic innovation.

The conversation today is about AI. Tomorrow, as the famed futurist Ray Kurzweil notes in what he calls the “Law of Accelerating Returns,” the next disruption will likely emerge even faster, and from a direction we don’t expect.

There is one memorable footnote worth sharing about Nokia. In a powerful testament to this very idea of reinvention, the Nokia of today has found new life, pivoting entirely from mobile phones to become a leader in network solutions. This itself is the point: reinvention, even after a catastrophic failure, is always possible.

The ultimate takeaway for pragmatic leaders is not to predict the future. It is not to wait for the “perfect” future. Instead, “it is,” as the visionary computer scientist Alan Kay once famously said, “to invent it.”

Note: This briefing was originally published on LinkedIn on November 14, 2025. It has been migrated to our new home on Substack to create a complete archive. Multi-format features like video and audio commentary are available for all new briefings published from April 2026 onwards.

One of my most enduring lessons from graduate school came from the education I received in basic research methods.

As a grad student, you learn that bias is an ever-present phenomenon. Bias is impossible to eradicate completely. The best we can do is seek to minimize it, recognizing that being vigilant and responsive to bias begins with the difficult admission that we all possess it, regardless of how objective and rational we think we are.

Today, as AI assistants, tools, and workflows are being rolled out, our ability to stay on top of bias is being outstripped by the pace at which data is being consumed to deliver it. In our rush to deploy more AI, the cultural assumptions, blind spots, and historical patterns of our world are being embedded directly into the foundational models we’re depending upon.

Two popular, well-cited studies of several of OpenAI’s GPT models published in PNAS Nexus and Harvard University showed that even the most advanced LLMs exhibit a significant cultural slant toward English-speaking, western countries (the Harvard study refers to this as a “WEIRD” - Western, Educated, Industrialized, Rich, and Democratic - bias), a reflection of the values where these models were created and the training data that was used to build them.

Given how these and other models have so quickly become the basis for most commercial AI solutions, AI bias has crossed the chasm from being an intellectual curiosity to a real, pressing problem that organizations must now face, with real financial and legal implications.

Consider the collective action lawsuit against Workday, which made headlines earlier this year. The suit alleges its AI-powered screening tools systemically discriminate against applicants based on age, race, and disability.

Or look at the SafeRent case. In November 2024, the company agreed to a $2.3 million settlement after its tenant-screening algorithm was found to disproportionately reject applicants who were using housing vouchers.

In both examples, AI wasn’t just “unfair.” It was rejecting qualified candidates and credit-worthy tenants. It failed at its one job, turning away potential business and creating massive financial and legal liability in the process.

Let me be clear: I’m not making a moral judgment on any specific set of values. Rather, this is about the risk that gets created when any single, dominant perspective creates operational blind spots. It means the AI you’re seeking to deploy may be fundamentally misaligned with the very customers, employees, or partners you’re trying to serve in a global market.

This is why bias is an issue that requires strong leadership. If bias has become a concern for you, here are a couple of suggestions to consider:

First, mandate transparency from AI vendors. Ask for governance reports, independent audit results, and clear explanations of data they used for training. Look to AI standards like the NIST AI Risk Management Framework to understand some of the key considerations that play into the trustworthiness and reliability of AI models.

Second, make your AI team cross-functional. An enterprise “red team” composed solely of AI engineers can’t recognize bias in all its forms. A team that includes functional areas such as HR, legal, operations, and sales brings a broader view of real-world blind spots that a team of developers may never see.

Ultimately, the problem isn’t that AI is biased. It’s that we are. AI is simply a mirror that scales our own blind spots. Our job as leaders is to be honest about the reflection it shows us and to be vigilant in managing the cracks it reveals.

Note: This briefing was originally published on LinkedIn on November 7, 2025. It has been migrated to our new home on Substack to create a complete archive. Multi-format features like video and audio commentary are available for all new briefings published from April 2026 onwards.

For the last two years, the business world has been captivated by AI as a new front-end. Owing to the success of chat services like ChatGPT and Google Gemini, we’ve collectively focused on the chatbot as AI’s definitive application, treating AI’s ultimate purpose as a “knowledge worker” we can talk to.

This has been reinforced by recent news. There’s a new wave of agentic browsers from companies like OpenAI (who recently announced “Atlas”) and Perplexity (who earlier this year launched their own browser called “Comet”). These browsers let AI chatbots like ChatGPT sit alongside you as you browse the web. They can see the pages you’re browsing, help you do tasks on web pages, and even autonomously browse the web for you.

But this entire focus on the browser — the human-facing web — is missing the bigger picture. The web was built for human consumption. The real opportunity lies in the data and execution layer beneath it, which was never designed for humans in the first place.

This new autonomous “execution layer” shifts AI’s role from a “knowledge worker” that talks about work to a “strategic implementer” that silently does the work, enabling new customer experiences and enterprise opportunities.

Here are just three examples:

1. Beyond “Checkout”: The Rise of Agentic Commerce. We’re used to thinking of web transactions as a human clicking “buy.” With agentic AI, these transactions can now occur autonomously. Payments giants like Visa and Mastercard are building foundational “Agent Pay” infrastructure. This delivers the plumbing for AI agents to be registered, verified, and given the authority to act on a consumer’s or a business’s behalf.

Think of an agent that can be a virtual buyer, not just finding the best price for a complex B2B component, but negotiating the terms, executing the purchase order, and scheduling the logistics with the supplier’s agent. This represents a foundational new layer of commerce infrastructure, operating far beyond a simple “buy” button.

2. Beyond APIs: Intelligent System Integration. For decades, connecting core systems like an ERP and a CRM has been a brittle, expensive, and time-consuming process. It’s been the “dirty work” that cripples execution. Organizations have traditionally had to rely on rigid APIs and manual data mapping, hoping nothing breaks, and making data migrations and data transformation an enormous, capital-intensive endeavor.

Agentic AI reframes this entirely. Some organizations are finding success cutting multi-month (or even multi-year) integration projects down to initiatives completed in weeks. Underlying these integrations are AI agents capable of autonomously analyzing the data schemas of systems, mapping fields, and managing the flow of data. Instead of integration as a one-time process, AI agents are becoming the integration, adapting to context and changes in real-time.

3. Beyond Databases: Autonomous Data Migration. Ask any CIO about their biggest execution nightmare, and “data migration” will be near the top. It’s a high-stakes, high-risk process defined by manual validation and the constant threat of data corruption. This is perhaps the most powerful example of AI creating value “beyond chat.”

AI startup DualEntry boldly claims to use agentic AI to complete a full migration of a company’s legacy financial data to their platform in 24 hours, an expensive process that is usually thought of as taking months, or even longer. In agentic platforms such as DualEntry, AI agents are tireless “execution workers” that can validate, migrate, and remediate data at scale in ways that are hard to replicate with human teams alone.

Here’s the bottom line: there’s a web of possibilities for your organization that extend well beyond the AI chatbot.

As a starting act, the AI chatbot is a compelling parlor trick. But there’s a significant opportunity in AI’s next act, moving from AI that assists knowledge work to autonomous AI that solves the implementation and coordination challenges that used to only be possible through manual intervention.

That’s the real revolution. And it’s already happening in the plumbing.

Note: This briefing was originally published on LinkedIn on October 31, 2025. It has been migrated to our new home on Substack to create a complete archive. Multi-format features like video and audio commentary are available for all new briefings published from April 2026 onwards.

In the race to deploy AI, the market is fixated on the wrong prize. Leaders are pushed by their boards to find and implement the “best” model, believing the algorithms and insights that emerge from these models are the keys to unlocking AI’s value.

Put simply: they aren’t.

Access to powerful foundation models is rapidly becoming table stakes. Your competitors can (and will) buy access to the same technology. The real, lasting competitive advantage — your competitive moat — isn’t the AI you buy, but the unique intelligence you build.

This is where many AI game plans fall short. We try to plug a generic AI into a rigid, existing process. That process, built for human predictability, inevitably breaks when it meets a complex, real-world exception.

The most AI-fluent organizations don’t build processes. They architect decision systems.

A process is rigid. It breaks upon encountering exceptions. A system is adaptive. It learns from exceptions.

This is the true purpose of a “Human-in-the-Loop” (HITL) workflow. Contrary to enterprise intuition, HITL isn’t a safety brake. Rather, it’s the engine of your decision system. It’s the mechanism that systematically captures the irreplaceable, nuanced judgment of your human experts, which is critical wherever high-stakes business decisions are made.

As the Harvard Business Review shared, AI isn’t yet ready to make decisions that necessarily involve many additional nuanced elements beyond data and algorithms and where the consequences of an error are significant. For any decision with material legal, financial, or brand risk, human oversight is non-negotiable.

But — and this is the key — this oversight cannot be passive.

The strategic flaw in most HITL designs is that they inadvertently promote “automation bias,” a well-documented phenomenon where human reviewers become overly trusting and simply “click yes” on AI-generated suggestions. This is the worst of both worlds: you pay the cost of human review without gaining the benefit of their expertise, and you fail to catch the very errors the system was designed to prevent.

A properly architected decision system reframes the human’s role. You aren’t hiring them to be a passive rubber stamp. You’re promoting them to be a “strategic reviewer.” Their job is not to approve the AI’s good decisions, but to hunt for its bad ones.

This is where the moat is built. In data science, this is called “Active Learning.”

• When an AI flags a fraudulent transaction in a process, a human clicks “approve” or “deny.” The AI learns little, and the expertise is lost.

• When an AI flags a transaction in a system, the human expert’s override (e.g., “Approve: This matches the seasonal shipping pattern for this specific client”) is captured as priceless, proprietary data.

That single data point — that “why” from your expert — becomes the unique “data deposit“ of your business. It’s an asset no competitor can replicate.

When you architect your AI workflows as a system, every human interaction becomes a data deposit. You’re creating a proprietary feedback loop that methodically distills your team’s collective expertise, training an AI that becomes deeply, contextually aligned with your business, not your competitor’s.

Stop worrying about which AI model to buy. Start architecting the decision system that will capture, scale, and monetize your human expertise. That’s the asset that scales. That’s your real competitive moat.