The Work Intelligence Loop
AI That Actually Sticks: Fixing Work Before Scaling AI
In this episode of The Work Intelligence Loop, Todd Michaud is joined by Giorgio Zampirolo, Strategic Advisor and AI Governance Specialist, and Daria Rudnik, Team Architect and Executive Leadership Coach, for a direct conversation on why most AI initiatives fail and what to do about it. The core argument: before you scale AI, you have to fix the work. They break down why adoption stalls, what leadership gets wrong, and why skipping the foundational work is the most expensive mistake organizations make.
From governance and ownership to the behavioral patterns that quietly kill AI momentum, this episode is built for leaders who want results, not just a roadmap. Giorgio and Daria also share what European organizations are doing differently and what North American companies can learn from it. If you are serious about making AI stick, this one is worth your time.
From governance and ownership to the behavioral patterns that quietly kill AI momentum, this episode is built for leaders who want results, not just a roadmap. Giorgio and Daria also share what European organizations are doing differently and what North American companies can learn from it. If you are serious about making AI stick, this one is worth your time.
Me and Giorgio Zampirolo explain why most AI initiatives fail—not because of the technology, but because organizations try to scale AI before fixing how work gets done.
- Successful AI adoption starts with strong team systems and clear ownership
- Poor workflows and unclear responsibilities are the biggest barriers to AI success
- AI governance should enable safe, structured adoption, not bureaucracy
- Leadership behaviors and culture determine whether AI creates value or stalls
- Organizations that redesign work before scaling AI achieve better adoption and lasting results