For years, global discoverability followed a familiar playbook. Create content, translate it, localize keywords, and optimize for regional search engines. If the SEO work was done well, users would find the content. That model is starting to break down. As AI-driven search and answer engines become more prominent, content is no longer discovered only through keyword matching. Increasingly, it is generated, summarized, and surfaced by AI systems that interpret intent, context,

When companies talk about localizing an AI product, the conversation often starts and ends with language. The UI needs to be translated. The model needs to respond in another language. That is important, but it is only the baseline. For AI products, real localization begins where translation stops. Cultural and content adaptation is about ensuring that what your AI says, how it says it, and what it chooses not to say actually makes sense to users in a specific market. For C-l

When AI companies talk about going global, the conversation often starts with market size and revenue potential. But very quickly, successful teams realize that regulatory compliance and content restrictions are not a legal afterthought—they are a core product concern. For AI products in particular, globalization means operating across very different legal frameworks, cultural norms, and content expectations, all at the same time. Unlike traditional software, AI products acti

For a long time, marketing localization has been viewed as a necessary cost. Content was created at headquarters, handed off for translation, and released weeks later in other markets. Success was measured in cost per word and turnaround time. If everything went smoothly, localization was considered “done.” That model no longer fits the reality of modern marketing. Global audiences move faster, campaigns evolve constantly, and growth increasingly depends on how quickly brands

AI has quickly moved from a niche experiment to a boardroom topic. For marketing leaders, the promise is compelling: faster localization, lower costs, and the ability to reach more markets with fewer constraints. It’s no surprise that many CMOs are asking the same question: Can AI finally fix the speed and scale problem in global marketing? The short answer is yes—but only if it’s used deliberately. AI is not a universal solution for every type of marketing content, and treat

As AI becomes embedded in marketing localization, one assumption often surfaces early: if machines are doing more of the work, then localization should become simpler and cheaper to manage. In reality, the opposite is often true. AI changes not just how content is localized, but how decisions are made, risks are managed, and teams collaborate. This shift brings governance and cost back into the spotlight—and elevates the role of the localization program manager from coordinat

When startups talk about “localizing” an AI product, the conversation often starts and ends with language. The UI needs to be translated. Buttons, menus, onboarding screens, error messages, and maybe a help center. That work is necessary, but it is rarely sufficient. For AI products in particular, language and UI localization sit at the intersection of user experience, product design, and system behavior. If done well, it makes the product feel intuitive and trustworthy in ev

Once AI localization is up and running, a familiar question quickly follows: How do we know if this is actually working? For many organizations, this is where momentum slows. AI pilots may look promising, but without the right metrics and iteration model, it becomes difficult to prove value, make improvements, or justify continued investment. Measurement is not just about reporting results. In AI-driven localization, it is the foundation for trust, learning, and scale. Why Tr

By the time an AI product reaches the testing phase in a new market, many teams feel they have already done the hard work. The interface has been translated, the core workflows localized, and compliance risks reviewed. It is tempting to see local testing as a final checkbox before launch. In practice, this is where many global AI launches either succeed or quietly fail. Local testing is not just about finding bugs. It is about validating whether the AI behaves the way local u

As AI becomes part of everyday marketing conversations, many organizations feel pressure to “use AI” without a clear sense of what that actually means. Machine translation engines, large language models, and AI-powered transcreation are often grouped together, as if they were interchangeable. In reality, they solve very different problems. For marketing localization, choosing the right AI is less about adopting the newest technology and more about matching the right approach

By the time an AI product is launched in multiple markets, many teams feel they have “finished” localization. In reality, this is where the real work begins. Unlike traditional software, AI products evolve continuously. Models are retrained, prompts are refined, content sources change, and regulations shift. Localization, therefore, cannot be treated as a one-time project. It must become an ongoing, cross-functional discipline embedded in how the organization operates. Contin

By the time marketing teams begin implementing AI for localization, the conversation usually sounds optimistic. AI promises faster launches, broader language coverage, and reduced operational strain. From a distance, it looks like a straightforward efficiency upgrade. In practice, implementation is where brand risk and compliance exposure quietly surface. AI does not fail loudly at first. It fails subtly, through small inconsistencies, misaligned claims, and tone shifts that

When AI startups talk about “going global,” the conversation often starts with languages. Which markets need which languages, how fast translations can be delivered, and how much it will cost. That discussion is necessary, but it is not where a global localization strategy should begin. For AI products, localization is a product and business decision before it is a linguistic one. The choices you make at this stage shape how your AI behaves in different markets, how much risk

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