I sit down with Augstin Da Fieno Delucci, co-founder of Trilogica Global and former Director of Global Data and AI at Microsoft, to unpack a shift that changes how global teams should think about AI: treating context as a system, not a prompt.

We’ve all gotten better at generation, better models, better prompting, better fine-tuning. Agustin argues that progress is real, but it hits a ceiling when the surrounding architecture has no memory of what “good performance” looks like across markets. When context gets rebuilt from scratch each cycle, content performance becomes unpredictable, even if everything passes QA. We explore how to move beyond linguistic correctness and brand compliance as the finish line, and start aiming for resonance: the words, framing, and emotional register that actually drive action for a specific audience.

Then we get practical. We talk about “simulation before shipping” using tools many teams already have, including semantic analysis, cultural inference, sentiment analysis, social listening, and vector databases that store market knowledge for retrieval. We also dig into what’s still emerging: the feedback loop that continuously refines audience models with real outcomes so your system gets sharper over time.

Your translation management system might not be failing, but it can still be quietly throttling your localization program. Stephanie and Giulia Greco unpack why many client-side localization professionals feel stuck right now: TMS platforms that looked “end to end” in the sales cycle start showing real product gaps once you add more content types, more stakeholders, tighter release cycles, and more languages. The result is a mix of stalled automation, awkward workarounds, and the sense that you’re always one workaround away from breaking something important.

We get concrete about what to do next without pretending there’s a perfect answer. We talk through the three paths most teams face: stay and cope, migrate and brace for cost plus politics, or build solutions alongside your TMS and figure out how to sustain them. Then we shift into a practical strategy that helps either way: think like a product manager. Document the painful use cases, write crisp requirements, quantify impact, and take your vendor a business case instead of a complaint. We also get candid about influence, including the uncomfortable truth that vendor attention often tracks with spend and how smaller teams can still move the roadmap through clearer arguments, better storytelling, and showing up as a beta partner.

Finally, we explore why AI localization has changed the build-versus-buy equation. Giulia shares a smart pattern for using an LLM translation workflow safely: start with a narrow slice of content, use native-speaker linguists to correct output, feed those corrections back, and iterate until quality is ready for production. If you’re wrestling with TMS limitations, vendor roadmaps, and the future of language operations, this one will give you a clearer next step. Subscribe, share with your localization team, and leave a review with the biggest TMS gap you want solved.

Translation is no longer a single lane that runs through one department. We are watching localization spread into marketing stacks, product releases, support tools, and AI features like chatbots, sometimes without any coordination at all. That shift can feel empowering and fast, but it also creates a new question that companies cannot dodge: who owns quality when everyone can ship multilingual content?

We dig into the forces behind “shadow localization,” from executive pressure for velocity to the growing ease of plugging AI translation into any workflow. When teams can route work around traditional processes, the old model of centralized control breaks down. The risks are not just technical fragmentation or duplicated effort. The bigger problem is governance: inconsistent terminology, unclear accountability, and unmanaged risk that stays hidden until it becomes a customer facing failure.

We also talk about what actually works in practice. Instead of trying to re centralize everything, we explore connective governance: shared standards, clearer rules of engagement, and an assessment layer that helps teams move quickly while still getting feedback on quality. We discuss where a human in the loop matters most, how to think about content rubrics by risk level, and why localization is becoming distributed infrastructure rather than a standalone service. If you are seeing AI localization pop up across your org, subscribe, share this with a teammate, and leave a review. Where is shadow localization showing up in your world?