How Many Successful Pilots Does it Take to Change Nothing?

A successful pilot often feels like real progress. But that feeling can be misleading. A proof-of-concept is built, the model works as intended, the dashboards light up, and the team presents promising use cases with a spark of optimism. But beneath the surface, a familiar pattern begins to take shape: nobody knows what happens next. Ownership is vague. Priorities shift. The excitement fades.

Welcome to pilot purgatory - The place where good ideas quietly stall, never quite making it into everyday operations.

In one of Intelecy’s previous blogs, Navigating the IT/OT Divide, this issue is named directly:

“Mature enterprises are (…) Getting out of ‘pilot purgatory’ and scaling realized business value ….”

But how is it actually done? But what does “mature” actually mean in this context? In our experience, maturity isn’t about technology depth - it’s about leadership readiness. Many have invested time, energy, and budget into pilots that never turned into scalable solutions.

And often, it wasn’t the technology that failed. It was the absence of a clear path forward - one that connects early experimentation with operational transformation.

According to McKinsey, fewer than 30% of digital transformation initiatives in industrial companies successfully scale beyond the pilot phase. In other words, pilot purgatory is not the exception. It’s unfortunately the norm.

In this article, I want to build on two themes I’ve explored previously; culture and workforce readiness - and bring them into the heart of the execution problem. Because pilot purgatory isn’t just a byproduct of complexity or lack of innovation maturity. It’s a leadership issue. And it’s a strategic opportunity, if we’re willing to treat it that way.

Culture Failure, Revisited

In Why Industrial AI Fails Without a Culture Shift, I argued that digital transformation efforts often break down not because of flawed technology, but because of an incompatible organizational mindset.

We see this when change is framed as something that happens to people rather than with them. When experiments are isolated from operational reality. When senior leaders say, “we’re innovating,” but mid-level managers are left wondering what it means for their teams or targets.

Culture shows up in the small details, like whether operators feel safe giving feedback, whether people are punished for failure, or whether success stories are shared and celebrated.

And most importantly: culture decides whether people will adopt something new, even when it works.

So when a pilot is completed and the technology is technically validated, what often holds us back is not technical uncertainty, but cultural inertia. Scaling a pilot means scaling belief, not just code. And that starts with trust.

The People Gap

In Bridging the Workforce Readiness Gap, I highlighted another common failure point in industrial transformation: the gap between AI ambition and workforce enablement.

We talk a lot about talent in the AI world, but too often, the focus is on hiring data scientists and technical architects, when in reality the critical constraint is much closer to the ground. The people who will actually use these tools - machine operators, engineers, shift supervisors - are often the least involved in their development.

When new solutions are introduced without adequate training, context, or purpose, the reaction isn’t resistance - it’s confusion. Or worse, quiet disengagement.

A real example:
At one industrial site, an AI-driven anomaly detection tool was piloted successfully. The models worked. But no one had trained the operators on how to interpret the alerts, or when to trust them. The IT team moved on, and the operators, overwhelmed by unfamiliar signals, quietly ignored the system. A technically successful pilot - dead on arrival.

Industrial workers aren’t anti-technology. On the contrary, they’re highly pragmatic and open to anything that makes their jobs safer, easier, or more effective. But if a new tool doesn’t speak their language - or feels like it was built for someone else - it will never become part of their routine.

This is why pilots that “work” in principle can still fall flat. Because adoption is not an afterthought. It must be designed from the beginning.

The Triangle of Scalable AI: A Readiness Tool

If we zoom out, successful industrial AI depends on the alignment of three forces:

Culture sets the tone. It’s the collective mindset that decides whether people are open to learning, comfortable with change, and willing to experiment. Without a culture that embraces new ways of working, even the best AI tools will be met with hesitation or quiet resistance.

Capability is what turns intention into action. That means a workforce that understands, trusts, and knows how to apply the new tools, not just theoretically, but in their day-to-day decision-making. Training and enablement aren’t nice-to-haves; they’re non-negotiable.

And ownership, perhaps the most overlooked factor, is the glue that holds everything together. Without someone clearly accountable for scaling, resourcing, and integrating the solution into the business, it doesn’t matter how good the pilot looks on paper.

You can think of these three forces like a triangle. Remove one side, and the structure loses integrity. Remove two, and it collapses. It’s a simple but effective diagnostic. Before you start a pilot, ask yourself: is this triangle intact?

Why Pilots Stall: It’s Not the Tech

When AI pilots stall, it's rarely because the models didn't work. More often, it's because the organization wasn't structurally or psychologically prepared to take the next step.

• Who owns the result?
• Who funds the next phase?
• Who is responsible for adoption, training, and measurement?

All too often, the answers are vague - or absent.

Pilots are frequently scoped as isolated proofs-of-concept, run by innovation teams or external vendors, with minimal integration into daily workflows. Success is defined by whether the model runs, not whether it improves the business. And when the pilot ends, everyone moves on.

In this way, pilot purgatory is not a technical failure. It’s a leadership vacuum.

How Good Pilots Go Bad

The irony of many failed pilots is that the technology itself didn’t fail. The model performed. The interface worked. The data was clean enough. But the pilot was doomed by design.

Sometimes, it's because the pilot was scoped in isolation - as a sandbox experiment, disconnected from real operations. Other times, ownership shifted so many times that no one felt accountable when the moment came to scale. Or the tools landed on the floor without ever involving the people expected to use them.

And often, teams declare success when the model runs, not when it changes anything.

These aren’t isolated mistakes. They’re patterns. And once they’ve happened a few times, they start to shape culture. Teams become skeptical. Frontline workers stop engaging. Executives hesitate to invest again.

The damage isn’t just technical - it’s psychological. Trust erodes. Momentum stalls. And the next pilot becomes harder to justify.

How to Break Out of Pilot Purgatory

What separates companies that scale from those that stall isn’t luck or budget, it’s leadership. Scaling doesn’t happen organically. It happens when someone decides, early on, that this initiative is not just a test, but a stepping stone to something operational.

That decision needs to be matched with structure. There must be a clear link between the pilot and real business outcomes - things like safety, uptime, energy efficiency - not abstract KPIs that live in PowerPoint. Operational leaders need to be involved from day one, not brought in after the fact to “roll it out.” And critically, the resources to scale must be planned upfront, not requested in a panic after the pilot ends.

Just as importantly, the solution itself must be usable. If the people closest to the process - operators, engineers, line managers - can’t interpret or trust what the system is telling them, adoption will falter. Usability isn’t an interface issue; it’s a trust issue.

There’s no playbook for perfect scaling. But there is one consistent truth: success doesn’t come from technology alone. It comes from clarity of purpose, strong leadership, and the willingness to commit beyond the comfort zone of experimentation.

A Leadership Filter - Before You Start

Before launching a pilot, industrial leaders should be brutally honest with themselves. A successful pilot should never be the goal in itself - it should be the starting point for operational transformation. That means the groundwork for scale needs to be laid before a single line of code is written or a sensor is connected.

If you can’t envision how it will scale, you probably shouldn’t start.

Before you greenlight a pilot, pause and reflect. Is someone clearly accountable - from pilot to scale? Have you identified a real operational problem, and brought the people closest to it into the conversation? Are success metrics defined in business language, not just technical performance? Do you know what happens next if it works - and are you willing to fund it? Most importantly, will the people expected to use the tool be able to trust and interpret it?

If the answer to any of these questions is uncertain, consider holding off. Starting small is smart. Starting unprepared is not.

The Arc So Far

This article is the third in a series that explores the real foundations of industrial AI success - not tools or algorithms, but people, process, and purpose.

• In Why Industrial AI Fails Without a Culture Shift, I made the case for mindset and organizational readiness.
• In Bridging the Workforce Readiness Gap, I focused on people, training, and frontline enablement.
• And now, in “From Pilot Purgatory to Scalable Impact,” I’ve explored the leadership, structure, and accountability needed to break the cycle and build momentum.
  
  

Culture, capability, and ownership aren’t abstract concepts; they’re the scaffolding of transformation. Without them, even the most sophisticated tools will fail. With them, even modest solutions can unlock real impact.

At the end of the day, AI doesn’t drive change. People do.

And people need clarity, trust, and leadership to make that change real - and to believe it’s worth doing.

This article wraps up our three-part series on industrial AI and transformation.

If you're on the journey to scale AI in your operations, I hope these reflections have offered both clarity and encouragement. Success isn't just about tools - it's about people, ownership, and a mindset that prioritizes long-term impact over quick wins.

Thank you for reading - and if any of this resonates, we’d love to hear from you.