Jovita Tam

In 2026, most organizations are great at talking about AI transformation. What many of them do not have is AI that produces business impact, governance that enables innovation, and people that deliver sound judgment in the AI world. This talk makes the argument most conference programs won't: that good governance, done right, makes innovation possible at scale — across data strategy, accountability in large-scale environments, and the operating models, skills, and culture that make AI transformation real. Warning: Practical, honest, and occasionally uncomfortable. Contains genuine trade-offs and may lead to possible regrets about at least one decision from last quarter. Takeaways: • How to close the gap between data and AI investment and business impact • What real accountability looks like in large-scale data environments, and why "shared responsibility" can be a detrimental design flaw • The difference between an AI skills gap and a judgment gap — and what it means for how you build your operating model, your culture, and your leadership pipeline

Behind every AI success story is a quiet set of foundations—governance, trust, and purpose-driven decisions—that most teams overlook until it’s too late. This talk reveals what the smartest companies quietly get right—and what everyone else learns the hard way. It also explores the complex, unsolved challenges on the horizon—the ones we haven’t fully mapped, but can’t afford to ignore. In AI, speed without the proper foundations isn’t a strategy—it’s a liability.