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Enterprise Rollout & Adoption Playbook1 / 2

2 min lesson

Run the four-step rollout measurement loop

Walk through "Run the four-step rollout measurement loop" in order, then name the proof that tells you it worked.

Step 1 of 2

Run the four-step rollout measurement loopbenchmark, find power users, layer autonomy, measure at three levels

Provisioning seats is step zero. The rollout that actually deepens usage runs as a measured loop: establish where the org starts, learn from the people already winning, raise the ceiling with autonomous workflows, and read the result at every altitude so a healthy org average can't hide a dark team.

  1. 1Benchmark. Capture the starting line - weekly-active rate, agent vs. Tab usage, requests per active user - so every later number has a before to beat.
  2. 2Identify power users. Find the high-volume, high-acceptance engineers and study how they prompt; their patterns are your training material and your next champions.
  3. 3Layer in autonomous workflows. Once a team is steady on the basics, introduce agent-driven and background workflows so the ceiling on value rises instead of plateauing at Tab completions.
  4. 4Measure at org, team and individual. Read adoption at all three levels - the org average proves the program, team-level health finds the dark cohorts and individual usage tells you who to coach or champion.
Why measuring at three levels matters

A single org-wide number is exactly what produces the "100% adoption" illusion. Break it down: the team level surfaces the cohort that stalled, and the individual level distinguishes a future champion from someone who needs a targeted intervention. Depth lives in the distribution, not the headline.

The evidence: defaulting to the agent moves output

A University of Chicago study of roughly 1,000 organizations found that after Cursor's Agent was made the default, teams produced about 39% more output and 39% more merged pull requests. The lift wasn't evenly distributed: experienced developers accepted more of the agent's work, most likely because they plan first and give it a clearer target. That's the case for layering in autonomous workflows deliberately - and for investing enablement in the senior engineers who turn the agent into throughput rather than rework.