
Why You Absolutely Shouldn’t Use an LLM for Workforce Data
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.
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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.

The Workforce Productivity Survey: What 50 Executives Actually Think
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.

Why You Absolutely Shouldn’t Use an LLM for Workforce Data
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 Employee Perspective on AI's Impact in Software Development: What's Really Changing
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.
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