An AI pilot program is only truly successful if it can move beyond experimentation. Once a pilot shows promising results, attention naturally turns to scale—more content, more languages, more teams. This transition, however, is where many AI initiatives struggle. The gap between a controlled pilot and a production-ready system is wider than it first appears, and closing it requires more than simply increasing volume. Scaling an AI localization solution is not a technical exer

An AI pilot program should never be treated as a “one-and-done” experiment. In practice, the real value of a pilot emerges through iteration—repeatedly refining the solution based on evidence, feedback, and measured outcomes. Iteration is not a sign that the pilot failed; it is the mechanism through which a pilot becomes reliable, scalable, and enterprise-ready. By this stage, you should already have clear success metrics in place. Iteration is where those metrics start to dr

By the time you reach execution, most strategic decisions have already been made. You have evaluated the AI request, defined success metrics, and designed a pilot that looks solid on paper. Execution is where those assumptions meet reality. This is the phase where AI stops being a proposal and starts behaving like part of your localization workflow—sometimes in expected ways, and sometimes not. Many AI pilots struggle at this stage not because the technology fails, but becaus

How to define metrics that support real business decisions

Implementing artificial intelligence in localization can dramatically speed up translation workflows and improve consistency, but success is far from guaranteed without a well-designed pilot. An AI pilot program is a controlled, small-scale experiment that allows organizations to validate AI’s impact on their localization process before committing to a broader rollout. In a previous article, Preparing an AI Pilot Program for Localization , we covered the foundational work—fro

When Evaluation Ends, Real Work Begins Many localization teams reach the same conclusion after evaluating AI: this could work for us. An AI pilot is not about proving that AI can work in theory. It is about proving whether AI can work inside your actual localization operation, under real constraints, with real people, and in a way that produces evidence leadership can act on. An AI pilot is not about proving that AI can work. It is about proving whether AI can work inside you

Introduction Artificial Intelligence is increasingly used in localization to speed up translations and manage multilingual content. AI is no longer just a side tool – it’s now at the heart of many successful global localization strategies. However, that doesn’t mean every idea involving AI will work out. Not every AI project is destined for success or will deliver real value; a poorly chosen AI use case can easily waste time and resources. Localization teams and their manager