Scale With Confidence — Operating People Analytics in the AI Era

• Show a service catalogue leaders will use — with owners, SLAs and success metrics — so the function ships decisions, not dashboards.
• Make AI safe and useful: run a Legal-ready evaluation loop and a Pilot→Production scorecard to govern quality, bias, cost-to-serve and rollback.
• Anchor targets in credible benchmarks and prove value through three anonymised mini-cases, then leave with a 90-day field guide to start next week.

• People Analytics service catalogue — 6–8 services with owners, SLAs and success metrics.
• AI evaluation loop — data prerequisites, bias/safety tests, versioning and a Pilot→Production scorecard.
• Three anonymised mini-cases — hiring speed, critical-skill risk, frontline stability — with guard-rails and outcome ranges.
• Benchmarks to set credible targets — how to talk ranges and secure Finance buy-in.
• Operate at scale — adoption telemetry, deprecation policy, and a 90-day field guide.

• Publish a clear service catalogue and align owners, SLAs and success metrics within two weeks.
• Run an AI evaluation loop Legal will support, using the Pilot→Production scorecard to govern quality, bias and rollback.
• Set benchmark-anchored targets for universal metrics that Finance accepts.
• Instrument adoption, decide kill/pivot/scale, and send a monthly value note that links decisions to capacity, cost or risk.

• Generative AI and new reporting regimes raise expectations for HR while exposing data, process and governance gaps.
• Boards and Finance expect decision-grade services with audit trails, credible targets and measurable outcomes — not experiments. Leaders need repeatable ways to scale analytics safely without inflating cost-to-serve or compliance risk.