The Visier Blog

AI is forcing organizations to rethink how work gets done. These five takeaways from the HR Leaders panel "Redesigning Work for the Human-AI Era" cover where humans must stay in the loop, why ethical guardrails come before rollout, and which metrics actually matter.

Visier Research surveyed 50 business leaders outside HR to uncover the workforce productivity statistics that matter most: how executives define productivity, and which metrics they're missing. Biggest takeaway? 65% say inefficient processes—not bad data—are the biggest obstacle.

HR data is unlike almost any other data your organization holds—volatile, legally sensitive, and deeply contextual. Most AI deployments treat it like any other data source and pay the price. Here's what the architecture actually needs: a context layer, durable guardrails, and a semantic foundation that travels with the data wherever it goes.

The headlines say AI is eliminating software jobs. Visier's analysis of 3.6 million employee records—paired with employee interviews— tells a different story: roles are despecializing and reorganizing, and the real costs (mentorship, team cohesion, skill atrophy) don't show up in headcount data at all.

HR tech leaders are under pressure to ship AI, not just evaluate it. But most projects stall before they reach production. The model isn't wrong, but the workforce data underneath it isn't ready. Here are the three structural problems that kill momentum: data you can't trust, complexity that slows your launch, and costs that scale faster than anyone planned for.

Effective workforce planning requires HR and Finance work as a team. Here's how HR and Finance can keep in sync during three core stages.