The Future of Good: How do we build AI-Powered Global Solutions that Lift Us Up?

By Talia Zur Baruch, GlobalSaké & LocLearn Founder

“Dreams are not what you see in your sleep, they are the things that don’t let you sleep.” (Ronaldo)

On a stormy San Francisco night this week, I found myself at the Sigur Rós concert, taken by the hand to faraway Iceland, where time flows slow, dew drops hang heavy on birch, and the bellow of reindeers roll in the meadow. A signal to soften into Winter’s hibernation, appreciating Nature’s rhythm, a force that fuels the human spirit, nurturing us to be, to become, to dream, to do. 

Throughout 2025, we’ve seen how breakthroughs in multilingual, multimodal agentic AI can accelerate global-first launch cycles and amplify productivity. Every click, every decision, every creative output becomes part of a powerful feedback loop between human intuition and machine precision. The result? Smarter systems, driving new possibilities for global real-time rollout solutions.

As we now edge into the year’s end, it’s worth pausing to consider: 

• What are we accelerating productivity for? 

• How can we train next-gen AI Agents to serve humanity, not drain it? 

To answer that, we must first ask a deeper question: 

• How do we define and measure success? 

Our defined KPIs determine what we build, what we prioritize, and whom we optimize our solutions for.

From Support to Strategy: Evolving from Production Metrics to Value-Impact KPIs 

For decades, the Localization industry has measured productivity through word-based metrics—an approach that anchored Localization as a downstream production function. But multilingual, multimodal agentic AI makes “word count” metrics obsolete. The future requires shifting our focus to end-user value-impact KPIs, elevating Localization to a strategic driver of global growth rather than a tail-end service. 

This same reorientation applies to product performance. Imagine a world not organized around apps—no endless rows on a screen, no juggling logins, no switching between interfaces. Instead, imagine success defined by the user’s end-goal task completion.

In a world where AI Agents can book our flights, schedule our rides, and reserve our dinners, humans could be liberated from managing dozens of apps. We could interact with a single, intelligent voice interface, while the Agent handles the complexity beneath the surface. Success wouldn’t be measured by user clicks or screens; it would be measured by end-goal completion.

So I’ll leave you with this question:

What user-first, value-driven solutions do you hope advanced agentic AI will unlock for global good?