Stop chasing AI hype. Mix human and machine work strategically across three tracks: project flow, modality, and databases to optimize cost and quality.

Treat localization like a product: understand stakeholders’ needs, start small, iterate, and show value to drive innovation and impact.

Localization has evolved from a specialty service into a core product feature that directly impacts user trust, retention, and global market success.

Generative AI scales content fast, but quality must be designed upfront through intent modeling and validated via frameworks like MMQEval.

Quality management in localization is shifting from measuring perfection to managing risk, as AI automation redistributes rather than eliminates risk.

AI’s rapid integration into localization is overwhelming professionals, causing cognitive overload and identity crises, but regaining control through accountability and action can rebuild resilience.

Africa is building its own AI solutions despite infrastructure challenges, focusing on mobile-first, low-code tools that work locally rather than waiting for external investment.

Localization pros are irreplaceable plumbers of global business; AI changes tools, not fundamentals, but we must attract next-gen talent now.