As AI systems move from experimentation into mission-critical business workflows, the old
reliance on gut checks and ad-hoc spot tests is no longer enough. Inconsistent model behavior is
more than a nuisance—it’s an operational risk that can impact user trust, decision quality,
compliance, and ultimately revenue.
In this session we explore how teams can move beyond intuition and establish structured,
reliable approaches to evaluating AI. We will outline practical use cases from real-world
deployments, highlight why even lightweight evaluation methods can make a measurable
difference, and discuss what’s at stake when organizations skip this step.
Attendees will learn:
How to detect whether AI systems are truly improving—or quietly regressing.
Common failure modes that derail AI products and how to prevent them.
Tools and techniques for building scalable evaluation systems.
How structured evaluation can guide product decisions, reduce costly guesswork, and
strengthen trust.
Whether you’re deploying autonomous agents, fine-tuning prompts, or choosing between
models, this talk will provide a framework for making AI evaluation a foundational part of your
stack.