Artificial intelligence

Over the past two years, companies have rushed to “try AI.” Innovation budgets have exploded, teams have mobilized, and announcements have multiplied proudly claiming “we launched an AI PoC.”

A Proof of Concept (PoC) is an early-stage effort meant to validate the technical feasibility of an idea. The goal is simply to confirm that an AI model can work in a controlled environment, long before thinking about a pilot or a full deployment. It’s a crucial step, but it creates no value on its own.

And when you dig a little deeper, the reality is harsh. Very few initiatives move forward. According to CIOs, 88% of AI PoCs never make it to production. AI fascinates leaders, but few organizations know how to turn it into an actual business lever.

Welcome to AI PoC Hell,  the space where PoCs multiply endlessly without ever becoming real projects.

The trap of “let’s run a test”

It usually starts with good intentions: let’s validate the idea before committing.On paper, it’s a healthy reflex. In practice, it often turns into a dead end with more analysis and less progress.

Too often, the PoC gets stuck in the sandbox. Teams prove the idea could work, but not that it should exist. They test on partial datasets, in ideal conditions, far from the real complexity of the business. When it’s time to move toward a pilot or production, everything breaks. Systems don’t talk to each other. Data is missing. Processes aren’t ready. Ownership is unclear.

In manufacturing, Automation World recently reported that many projects stall not because of technology, but because operations and IT teams are poorly integrated.

The outcome is nearly always the same. The PoC becomes a shiny artifact for steering committees, but a blocker for teams.

What’s really going wrong

In most cases, the problem is not technology. It’s organizational. Many PoCs begin with vague objectives, poorly defined pain points, or solutions that don’t align with business priorities. Companies want to “do AI” because they saw something impressive elsewhere, without knowing what they are actually trying to improve.

We see this pattern constantly. Organizations arrive with an idea borrowed from another industry or competitor and want to replicate it as-is. The project starts from the solution rather than the need, which weakens it from day one.

Then comes other obstacles. Internal expertise is often insufficient to carry an AI project from start to finish. Data quality isn’t always good enough. Costs rise unexpectedly. Human resistance kicks in. Internal support weakens. And even when organizations manage to deploy a first model into production, challenges persist. 

As Hugues Foltz, Executive Vice President at Vooban, puts it:
 “The problem isn’t a lack of ideas. It’s a lack of alignment. Any AI project born from a technological crush won’t survive without cross-functional vision and strong governance.”

From proof to impact: shifting the mindset

Escaping PoC Hell requires a shift from the very beginning. Successful organizations start with the why. Which metric needs to move. Which inefficiency costs the most. Which customer experience must be improved. This clarity helps target a use case that creates both value and satisfaction.

That’s exactly what our AI Diagnostic Guide is designed for: helping leaders identify opportunities that truly match their challenges.

Then comes a fundamental principle. An AI solution isn’t a project you complete and forget. It’s an operational asset that must integrate with systems, adapt to evolving data, and grow with business needs. According to McKinsey, only a third of companies truly manage to scale AI across their entire enterprise. Those who succeed monitor their models, refine them continuously, and understand that value comes from real usage, not from prototypes.

This product mindset is the real turning point. It transforms a well-chosen first project into tangible impact and moves the organization from experimentation to value creation.

A model worth following

By now, it should be clear that the success of an AI initiative hinges on how you structure the early stages. If you need a framework, our methodology, which combines an Innovation Sprint and a Project Blueprint, was built for exactly that. It brings clarity to the irritants, exposes the real complexity of the problem, and helps prioritize the initiatives that will create value quickly.

This approach has transformed the trajectory of many organizations. Take Nationex, for example. By clarifying their operational challenges early on, they deployed an integrated AI solution that predicts parcel demand with 89% accuracy a week in advance. Their success didn’t come from luck. It came from a structured approach that connected need, solution, and impact from the very beginning.

Leadership over technology

Moving from prototype to impact is fundamentally a leadership decision. Companies that escape PoC Hell don’t let AI initiatives sit on the sidelines. They make a clear commitment to transforming how they operate. They support their teams and create the conditions needed to move forward.

Deloitte Insights notes that mature AI organizations generate up to 3.5 times more economic gains than those that stay stuck in experimentation. Their strategy isn’t a secret. They treat AI as an organizational capability, structure their data rigorously, and tie every initiative to a concrete business objective.

The difference comes not from algorithms, but from strategic discipline.

From Hell to momentum

AI PoC Hell is not a fatality. It’s a transitional phase. Escaping it requires a clear stance. A PoC with no ambition for production cannot create value.

AI must be treated as a product integrated into systems, powered by reliable data, and supported by teams capable of making it evolve. The organizations that succeed industrialize what works, abandon what doesn’t create value, and measure impact rather than activity.

Real change happens when a PoC stops being a test and becomes a promise of impact.