Why Half of AI Projects Die After the Demo

According to Gartner research, at least half of all GenAI projects are abandoned after the proof of concept. Sit with that number for a second: not half of the bad ideas, half of the ones that already cleared a demo and got someone excited. Almost every AI project starts the same way — a demo that wows the room. Then it stalls. And it usually stalls for the same reason: nobody could prove it was trustworthy enough to ship. No VP greenlights a launch they can't explain. No legal team signs off on a system when nobody can say how often it's wrong, or what happens when it is.
Here's the uncomfortable part: the gap between a demo and production has almost nothing to do with the model. Spinning up a chatbot, a RAG pipeline, or an agent takes an afternoon; knowing whether it actually works — before your users find out for you — is the hard part. This talk makes the case that the missing piece is an evaluation layer, and walks through what it takes to build one for any AI application, in any domain.

We'll start where most teams do, with an LLM grading its own outputs, and see why that plateaus around 60-70% agreement with human judgment: good enough to catch obvious failures, not good enough to bet a launch on. The real jump comes from folding in domain-expert review to sharpen what 'good' actually means and catch the edge cases a generic judge waves through. Once your criteria are solid, that judgment can move into small, specialized, cheap models that score every single interaction instead of a 1% sample — and from there, those same evaluators graduate into guardrails that stop a bad response before a user ever sees it, rather than a dashboard that politely reports the failure after the fact.

Coming from a background in evaluating an AI system on a research project, and from digging through the current research and industry writing on how the best teams actually do this, this talk lays out a practical, domain-agnostic lifecycle: how to define failure, build a labeled ground-truth set, decide when prompting is enough versus when to invest in a fine-tuned model, and turn evaluation from a one-time pre-launch checklist into a system that keeps working as your product, users, and models drift. The through-line is simple — moving your AI from 'hope it works' to 'prove it keeps working,' so it lands in the surviving half instead of the abandoned one.

Takeaways:

• Why so many AI projects stall after the demo — and why it's a trust problem, not a technology problem
• A five-stage framework for maturing evaluation, from a first LLM judge toward self-improving production guardrails
• How to fold subject-matter-expert judgment into your criteria without it becoming a bottleneck
• When it's worth training a small, specialized model instead of prompting a large one
• How to turn evaluation checks into real-time guardrails without over-blocking legitimate users
• A concrete starting point you can apply to your next AI project or prototype this week