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Speed Isn’t Enough:
Why Talent Decisions Need Domain-Specific AI

Speed improved execution. It didn't improve decisions.

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The first wave of AI in talent made sourcing faster and shortlists easier to build. The debates in debrief stayed just as hard.

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That gap has a structural cause. Titles compress scope. Career paths form under conditions that rarely appear in writing. Generic AI is built for language — not for the context that explains how work actually unfolded and what it signals about readiness.

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This guide explains where generic AI helps, where it breaks down, and what domain-specific AI is built to do differently — drawing on lessons from law, medicine, and engineering, where teams hit this limit first.

What you'll learn

  • Why generic AI speeds up the process without improving the decisions underneath it
  • How expert-labeled data changes what AI can actually distinguish — and why that matters for evaluating readiness
  • What separates domain-specific AI from the other four types of AI companies
  • What better talent decisions look like when evidence replaces proxies

Download the guide