First, the good news: Most digital transformation projects in the pharmaceutical industry don’t fail. At least not right from the start. The pilot works, the technology delivers, and the initial results win over management. And then, about two years later, hardly anyone is using it anymore.

The real challenge starts after the pilot

For many companies, the pilot project is the goal, even though it should literally be just the beginning. A successful proof of concept answers an important question: Can the solution work in principle? But the truly crucial question is a different one: Does it also work on a large scale—across multiple locations, with many users, under real-world operating conditions? That’s exactly where the actual transformation begins—not in the pilot project!

The pilot trap

Almost every pharma company we speak with can now boast an impressive list of successful pilot projects: AI applications, advanced analytics, predictive maintenance, digital batch records, and reporting solutions. There’s no question that many of these deliver tangible results. Nevertheless, they remain limited to specific areas. They don’t become standard practice, nor do they transform the company. They remain what they were from the start: proof that something is fundamentally feasible. It’s a shame about all the effort.

The five most common scaling traps

Based on our project experience, the same five patterns almost always recur when the transition from pilot to rollout fails.

Lack of ownership. During the pilot phase, there is usually a dedicated core team, often demonstrating a commendably high level of initiative. Once the project is completed, no one feels responsible anymore.

Local optimization. A solution works exceptionally well at one location, with the team that helped build it. However, rolling it out to other locations is never seriously prioritized. In pharmaceutical manufacturing, this is compounded by the fact that each new location requires its own qualification process—not just a new rollout plan.

New Priorities. No sooner is the pilot completed than the next initiative is already waiting. The organization moves on before the previous solution has even had a chance to take effect.

No Operating Model. Who will further develop the solution, who will finance it, and who will be responsible for day-to-day operations? Many projects answer these questions too late. Some don’t answer them at all.

Underestimated Change. The technology is implemented, but the way of working remains the same as before. This is the same mechanism we described in our first post in this series: As soon as change feels like extra work rather than a relief, it gets derailed—no matter how good the solution actually is. As a result, the benefits remain limited in the end.

What successful companies do differently

Successful companies consider scalability from the very beginning, not just after a pilot has been successfully completed. These questions are answered during the pilot phase:

  • Who will take responsibility?

  • What will the rollout look like?

  • Which locations will be next?

  • What governance structures are needed?

  • How do we measure long-term success?

This turns a single project into a fundamental capability, because innovations create new opportunities, but only transformations bring about lasting change in the organization. This distinction is often underestimated. A successful pilot proves an idea; a successful transformation changes processes, decisions, and ways of working. That requires much more than just good technology.

Meet Xenium at Pharma MES Europe 2026

Would you like to successfully scale your digitalization or data initiatives? Let’s discuss how local successes can lead to company-wide impact at Pharma MES Europe in Berlin from October 1–2, 2026.

That’s exactly what our on-stage session is all about: On October 1 at 4:45 p.m., our colleague Dr. Florian Werner will discuss the topic “Challenge Your Peers: The World’s Data Is Connected. Are You?” to explain why enablement should come before technology when scaling industrial data. We’d love to have you join us!