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

WEBINAR December 17, 1pm ET|10am PT

Your GenAI pilot delivered strong results. So why does scaling it feel out of reach?

The issue is rarely the model. The real barriers are weak information architecture, inconsistent metadata, unclear ownership, and misaligned processes. Many organizations implement the latest LLMs without addressing the foundational structures required for reliable, repeatable outcomes. As a result, pilots succeed in controlled conditions, but full-scale deployments stall.

This session explains why so many GenAI initiatives fail as they move into production and what the most successful organizations do differently.

Why Listen to Us

Industry data makes the challenge clear. MIT estimates that 95 percent of enterprise GenAI pilots fail to produce meaningful financial return, resulting in 30 to 40 billion dollars in wasted investment. Gartner reports that nearly one third of GenAI projects are abandoned after proof of concept because cost, complexity, and value become unclear.

Earley Information Science has helped more than one hundred enterprises break that pattern and move into the successful five percent. Our Information Architecture Directed RAG approach has consistently delivered:

• Estimated 40 to 60 percent improvement in AI accuracy
• Estimated 70 percent reduction in hallucinations
• Estimated three to five times faster time to production
• ROI that can begin within six to nine months

We have supported organizations ranging from Fortune 500 financial services firms to major healthcare systems. We understand what separates initiatives that become strategic capabilities from those that quietly disappear. This session will show you how to position your program for the same outcomes.

You will learn:

The Context Crisis
Why GenAI systems struggle when content and metadata lack structure, and steps to correct this across large environments.

The Scaling Paradox
Why pilots benefit from manual curation, while production environments require sustainable, repeatable content operations.

The Metadata Sweet Spot
How to strike the right balance between too little tagging, which leaves AI unable to find what it needs, and too much tagging, which creates processes no one follows.

Progressive Enhancement
A four-phase model that builds from essential metadata to self-improving AI ecosystems.

Real-World Evidence
How organizations achieved measurable gains in performance through Information Architecture Directed RAG and the practices that support those gains.

This webinar is for you if:

• Your GenAI pilot worked, but scaling it has stalled
• You manage extensive unstructured content across multiple business units
• Your AI tools produce unreliable results because authoritative sources are hard to identify
• You need to show that knowledge management accelerates AI value with metrics executives care about

You will leave with a clear understanding of why information architecture is not optional. It is the foundation that determines whether your GenAI pilot remains an isolated success or becomes a scalable platform that transforms the business.

Bonus
Attendees receive a GenAI Scaling Readiness Assessment plus templates for progressive metadata improvement and concise, executive-ready elevator pitches.