AI in talent acquisition is no longer experimental—but the legal and risk landscape remains unsettled. With limited case law, incomplete regulatory guidance, and high-stakes labor-law exposure, many TA leaders face internal and external objections that slow or stop adoption.

Jeff Battinus, Vice President of Talent Attraction & Selection at ChenMed, has lived this tension while scaling agentic AI across high-volume clinical and non-clinical hiring. He shares a practical operating approach that treats risk mitigation and forecasting as core TA capabilities rather than compliance afterthoughts.

The session walks through how to quantify and communicate residual risk, design forecasting models that anticipate legal and operational friction, and convert objections into structured decision frameworks. This is not a high-level “AI is coming” talk. It is a working case study on moving from roadmap enthusiasm to governed, defensible deployment when the courts and regulators have not yet caught up.

Attendee Takeaways:

A practical risk-mitigation framework for AI recruiting tools that maps known legal exposure, residual risk, and compensating controls—so you can answer “what if we get sued?” with data instead of hope.
How to build and socialize simple forecasting models that surface likely objection points (bias claims, adverse-impact challenges, disclosure failures, vendor liability) before they become deal-breakers.
Specific language and decision criteria for overcoming stakeholder resistance—legal, HR, business leaders, and the recruiting team itself—when case law is sparse and precedent is still forming.
The “prove it or park it” test: a repeatable method for deciding which AI use cases move forward under active risk monitoring versus which stay on the shelf until the legal environment clarifies.