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In the patent world, AI has made speed and volume commodities. The firms that win will be the ones that measure — and deliver — what actually matters.

Most incumbents still use metrics from a pre-AI era: attorney hours, applications per attorney, price per application. Those made sense when human effort was the bottleneck. Now they’re misleading at best, dangerous at worst.

At the same time, new entrants overpromise results but struggle to demonstrate quality in ways that matter. That gap fuels the incumbent narrative: “AI is too risky, too inconsistent.”

Both sides are flying blind because no one has built the instrumentation.

What we need:

  • Quality metrics clients actually care about — not what’s easy to track, but what drives outcomes
  • Processes that measure and adapt continuously, not annually
  • Systems where every workflow decision is driven by data, not intuition

The opportunity isn’t just to adopt AI. It’s to define what excellence looks like in an AI-enabled practice — and build the systems to prove it.

Source: LinkedIn Post by Michael Drapkin

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