AI Transformation Is Failing to Deliver Profitable Growth—and It’s a Leadership Problem

The view is clear. The decisions are not. Most leaders can see AI coming, but few know how to lead through it.

In this AI economy industries are being reshaped in real time. New winners are appearing a high speed. Old category leaders are losing ground. Past success provides no insulation from the AI shockwave now hitting every sector.

The surprise is not really AI’s speed. It’s leadership’s response: most teams still treat AI as a tech initiative—although "important" still not a competitive operating shift.

So you talk it up. Run pilots. Buy tools. Stand up a task force. Rebrand it as “transformation.” The language is right, but the system stays the same: the operating model doesn’t change, incentives don’t change, and decision velocity doesn’t change. It can look like progress. “We are doing something.” In reality, it’s an activity without conversion — governance theater designed to signal movement, not produce advantage.

And the consequence is now obvious: AI is everywhere, but the growth and profit aren’t.

The data tells the same story. In several of the latest global surveys, 88% of enterprises say their organizations use AI at least in one function, yet meaningful, top and bottom-line impact is reported missing. And the AI adoption outlook is equally blunt—at least 30% of genAI projects are abandoned after proof-of-concept, because the AI is not enterprise-wide, and the business value never becomes real at scale.

And that’s the problem. That's when new competition is looking for the opportunity to take your place.

Feedback also tells me that, most leaders don’t fully understand the power of enterprise-wide AI. Not because they’re uninformed, but because they’re using yesterday’s mental leadership models to navigate a new terrain. They struggle with enterprise AI because they view it as just tech, not a systemic shift in business strategy. In most cases, small projects on the edge of the business lead to failed pilots due to poor strategy considerations, data issues, and insufficient governance, resulting in stalled adoption, legal implications, and wasted investments.

It’s the definition of failure—and most leaders don’t know what to do about it.

Enterprise-wide AI is not about “sprinkling AI” into a few support functions or adding a chatbot to customer service. It’s what happens when intelligence moves into the core of the business: into the product, the customer experience, the commercial engine, and the systems that run the enterprise—CRM, ERP, data platforms, and the supply chain workflows where value is actually created and captured.

When that shift happens, AI stops being an experiment and starts becoming how the company competes. You accellerate relevant businesss innovation. You automate what never should have required human attention. You augment the decisions that drive performance—not just the dashboards that describe it. You adapt services, pricing, and execution in near real time. Consistently, across the enterprise, not in isolated pockets.

That’s the bar. And it’s not what I see in most companies right now.

Here’s what I see in boardrooms and executive teams.

The first pattern: AI is recognized as “important,” but it is not anchored in the company’s economic logic. Leaders can’t answer the questions that matter: Where does AI change the business model? Where does it reshape margin structure and profitable growth? Where does it create real differentiation? Where does it compress cycle time so you can out-sprint the market? Where does it create new demand?

Without those fundamental answers, AI becomes a collection of pilots—interesting, expensive, and strategically irrelevant.

Then comes the second pattern: leaders stall. They over-study. They wait for certainty. They ask for more benchmarks and more proof. That feels responsible. It is not. It is a delay disguised as rigor. The decisiveness is killing your growth and momentum. Meanwhile, faster competitors are learning in public, shipping, iterating, and resetting customer expectations.

The third pattern is the quiet killer: even when pilots work, they don’t scale. They stay trapped in silos. They never become part of the enterprise's operating rhythm or core business. There is no shared architecture, no clear ownership, no talent plan, and no governance. AI becomes “innovation,” which is another way of saying “optional.” Never hitting a P&L for impact.

The patterns of leadership AI blindspots are why I keep saying the same thing: AI is not an IT project. It is the CEO and Board's responsibility. If leadership doesn’t own it, it won’t compound. It will dilute your priorities and investments.

It's not a technology issue. It's leadership not keeping up with reality.

Here is the deeper issue and the honest truth. most leaders are trying to use AI to optimize for a world that no longer exists. The world they know. What was instead of what will be. Leading to the worst failure: irrelevance. Nothing good happens when your customer no longer cares about you. Past glory is not going to save you. Just see what happened to Kodak and Nokia with that approach.

The traditional management theories and strategy frameworks were built for a past time when change was slower than planning cycles. In the AI economy, reality updates faster than your annual strategy deck. If your decision-making system cannot sense change early, choose decisively, and adapt quickly, then you don’t have a winning strategy. You have a story about the past.

Instead, you need strategic fitness to win the fight. Strategic fitness is not efficiency. It is adaptability with direction. It is the ability to make intelligent selections—what to double down on, what to stop, what to rebuild, and what to re-invent—before the market forces those choices on you.

AI-laggers tend to lose the same things in the same order. First, they lose speed. Then they lose customer relevance. Then they lose talent. Then the capital gets more expensive. And by the time they “take AI seriously,” they are playing catch-up in a market that has already moved on.

Time for reset: Start the reinvention and transformation where the value is

I’ve sat in board and executive rooms where the hard truth was inconvenient. Uncertainty set the agenda. Hesitation slowed decisions, then momentum, then the company.

Over time, I learned to name the value-threatening issues early—and to be specific about what to do next. That became a practical playbook for becoming strategically fit in an AI-driven age: how you set direction, how you govern decisions, how you allocate resources, and how you translate intent into execution.

This isn’t theory. It is distilled from real strategy work across markets and operating environments—what holds up under pressure, what fails, and what changes outcomes.

Most companies don’t need more sporadic pilots. They need an operating model that makes decisions faster and cleaner—and a leadership cadence that turns AI from discussion into enterprise execution, at a speed and level of clarity that actually moves results.

Make sure your company becomes more strategically fit for the Age of Intelligence—before the market makes the strategic decisions for you.

Working with companies, I’ve learned a simple lesson: AI execution rarely succeeds as a slow, year-long “transformation program.” The environment moves too fast. What works is a time-bounded strategic fitness upgrade with a clear endpoint and clear decisions.

In 90 days, you define what AI actually changes in your business model and where it can create durable advantage. You make the hard calls about where AI belongs in the core enterprise—workflows, products, pricing, operating rhythm—not as an edge experiment.

You also align leadership around a small set of high-impact moves that can be implemented and monetized. And you put in place the minimum operating system—data, architecture, governance, and decision cadence—so execution stays trustworthy, compliant, and accountable as velocity increases.

The objective is straightforward: convert AI into repeatable execution that produces sustainable economic value. Not scattered experiments. Not innovation theater. Not another slide deck.

In the Age of Intelligence, winners are not the companies with the most AI. They are the companies that become strategically fit enough to use intelligence as a competitive force.

Reinvent or Expire.

Connect Now