C-suite leaders agree: scaling AI to create business value is a top priority. But despite widespread experimentation, a growing “GenAI Divide” is emerging. Research shows that 95% of custom enterprise AI tools never make it to production — not because of the model, but because of brittle data workflows and a lack of contextual learning.

This strategic panel discussion goes beyond the hype to tackle the root causes that sink AI programs. Watch the on-demand session where industry leaders share a practical blueprint for building a resilient data foundation that unlocks enterprise-wide innovation.

What we covered

A crisp, strategic conversation focused on two barriers that consistently prevent AI from scaling:

Failure to Innovate

• Why endless rework, heavy compliance processes, and fragile pipelines stifle meaningful solutions.
• How to design a data framework that enables reusable, use-case‑agnostic components — so teams can move quickly without sacrificing governance.

Failure to Scale

• The real drivers of cost overruns and risk as projects move from prototype to production.
• Practical steps to create a stable, secure data environment that preserves consistency and compliance at scale.

Key takeaways

• The model is rarely the main failure point — data, pipelines, and context are.
• Build for reuse: design data assets and pipelines that serve multiple use cases instead of one-off prototypes.
• Governance that enables, not blocks: embed compliance into the flow so teams can innovate safely.
• Plan for production from day one: architecture, testing, and monitoring must be production-ready early in the lifecycle.

Speakers

• Wael Taha — VP, Enterprise Data and Analytics, Brown Brothers Harriman
• Rahul Goyal — Senior Director, AI/ML Engineering, Optum
• Terry Dorsey — Senior Data Architect / Evangelist, North America, Denodo (Moderator)