[RECORDED] Beyond the Pilot: Why Most GenAI Projects Fail to Scale and How to Become One of the Success Stories

GenAI continues to advance rapidly, but many organizations are discovering that early success does not automatically translate into scalable, trusted systems. This session examines why promising pilots stall and what separates isolated successes from sustainable enterprise capabilities.

1. GenAI Failures Are Rarely Caused by the Model: They stem from gaps in information architecture, content quality, metadata, governance, and operational maturity. When these foundations are weak, AI struggles to deliver reliable results and trust erodes as organizations attempt to scale.
2. Why Pilots Succeed and Production Fails: Pilots benefit from manual curation and limited scope. Production environments require sustainable content operations, clear ownership, and automation supported by governance. Without those in place, what worked in the pilot breaks down at scale.
3. Finding the Metadata Sweet Spot: Too little metadata leaves AI blind. Too much creates processes no one follows. The goal is targeted, use-case-driven metadata that can be enhanced progressively over time through entity extraction, auto-classification, and continuous feedback.
4. Treating GenAI as a Business Program: Successful organizations anchor their efforts in clearly defined use cases, measurable outcomes, and an honest assessment of current maturity across content, governance, and operations.
5. Measuring Value, Not Just Activity: Scaling GenAI requires baseline metrics, quality monitoring, and an ongoing ability to demonstrate ROI through improved accuracy, reduced hallucinations, and better findability.