Most enterprises have demonstrated AI's potential in pilot projects, but few have translated that early success into measurable business results. The ones that did have something in common: they started with a real use case, the data it needed, and the controls to run it in production, then expanded from there.
This session looks at why AI stalls between experiment and production, what AI-ready really means, and how data leaders can be a catalyst for AI initiatives.
Key takeaways:
- Clarity on what’s holding AI back: A clearer view of why so many AI efforts stall after the pilot, and what separates the ones that make it into production.
- A practical definition of AI-ready: What it takes for data to be ready for AI in production, beyond access and quality.
- Lessons from those getting results: What organizations seeing real business impact did differently, and how they built on early wins.
- A bigger role for data leaders: How data leaders can help drive AI initiatives from experiment to business impact.
ashduong
Published 49 minutes ago