Localization can quietly own the global customer experience while still feeling powerless when priorities shift. We’re expected to deliver a high-quality multilingual experience across acquisition, conversion, onboarding, retention, and expansion, yet the real levers often sit with different leaders across marketing, sales, product, and customer success.

Kevin O’Donnell, strategic advisor at Global 10X, gets concrete about what “influence” actually looks like inside a cross-functional, matrixed organization. Kevin explains why relying on the teams we naturally get along with can become a hidden constraint, and he offers a sharper lens. If product and sales are discussing a new region, or marketing is planning a launch, localization needs a pathway into those decisions before plans harden.

Kevin shares a simple, practical stakeholder mapping and influence map exercise you can do in an afternoon on a whiteboard. We talk through how to map real partnerships across functions, label what’s strong, what’s weak, and what’s “high potential but underinvested,” then use that visibility to change behavior and collaboration rhythms. We also preview the webinar series Kevin is hosting with Antoine Ray, The Bold Localization Leader, starting September 9, with a first session focused on stakeholder mapping and influence. [You can register here: https://info.argosmultilingual.com/the-bold-localization-leader]

If you want better localization strategy, stronger stakeholder alignment, and more impact without waiting for a reorg, hit play. Subscribe, share this with a localization leader, and leave a review so more teams can build influence where it counts.

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.