Why 88% of HR AI Investments Are Failing — and How to Fix It

The data is in: most HR teams deploying AI aren't seeing results. Not because the tools don't work — but because the operating model doesn't. This episode unpacks talent engineering, the shift that turns HR from a service function into a product function, and why the teams getting it right are building structural advantages that compound over time. We dig into the three pillars that separate teams who are automating HR from teams who are engineering it: data infrastructure and the rise of talent ops, skills-based rubric design, and the closed feedback loops most organizations still skip. From sprint cycles to versioned evaluation criteria to post-hire outcome tracking, talent engineering borrows the discipline that makes software teams effective — and applies it to hiring. We also look at what AI tools like OVI's screening agent Milo make possible: configurable rubrics that improve with every hire, ranked shortlists with written rationale, and sourcing pipelines that track reply rates and feed performance signals back into targeting. The question for HR leaders in 2026 isn't whether to adopt AI. It's whether to adopt the operating model that makes AI actually transformative.