Most organizations are rushing to deploy AI tools without asking the more important question: what does it actually take to build a workforce that can use them well?

In this episode of The Talent Shift, Bryq is joined by Marily Nika, Ph.D., AI Product Manager at Google and Founder of AI Product Academy, one of the few people operating at the intersection of hands-on AI product work and large-scale corporate training. Moderated by George Kalyvas, CEO at Bryq, the conversation moves from theory to practice: how to assess where your workforce actually stands on AI readiness, why the right training has to be role-specific and use-case-driven, and what the cultural shift looks like that makes upskilling stick.

Marily shares her five-stage AI adoption framework, explains why employees are overwhelmed and embarrassed rather than resistant, and makes the case that transformation — not productivity — is the question leaders should be asking. The discussion covers how to build the ROI case for a skeptical CFO, what a labs department can do for risk-averse organizations, and why AI literacy doesn't decay as fast as most people fear.

This is essential viewing for HR, L&D, and people leaders who know their workforce needs to get AI-ready but aren't sure where to start, how to sequence it, or how to get leadership aligned on the investment.

Watch the recording and discover:

• Why "how do we get more productive with AI?" is the wrong question — and what to ask instead

• Marily's five-stage AI adoption framework and why moving up just one level in 2026 is the right goal

• Why assessing AI literacy has to come before any training investment, and what a proper intake actually looks like

• The two-tier training model: foundational AI literacy for everyone (now a regulatory requirement under the EU AI Act), followed by role-by-role, use-case-driven upskilling

• Why employees feel like they're "cheating" when they use AI — and the cultural shift that fixes it

• How to make the ROI case to a CFO, and why the right framing is about doing the actual job better, not adding a course on top of it

• What a labs department can do for organizations that aren't ready to go all-in yet

• Whether opting out of AI is a viable position for employees in 2026