Plano Prompt Engineers (ChatGPT, OpenAI, Langchain)

Designing for Failure: Why AI Breaks After the Demo

Most teams can generate impressive AI outputs.
But fewer can turn those outputs into systems that reliably deliver value in the real world.

A proof of concept works.
The demo looks great.

. . But then the project hits production and runs into edge cases, broken handoffs, unclear ownership, missing validation, and outputs that don't fit how people actually work.

In this session, we'll explore the failure modes that cause AI initiatives to stall as they move from experimentation into production, and why designing around those failures from the beginning leads to more reliable systems.

You'll leave with a practical framework for evaluating risk, uncovering hidden assumptions, and identifying where a workflow is likely to break so you can design more effective AI systems from the start.

Speaker: Preston McCauley is a fractional AI consultant, product strategist, and experience design expert with more than 25 years of experience spanning product, UX, and emerging technologies. A Dallas Innovates Top 75 AI Innovator, he helps organizations design AI systems and workflows that deliver measurable business value.