How do you evaluate and hold our team accountable for the actual operational return on investment from AI workflows during our quarterly session reviews?

Question

Tyler Smith · Accepted Answer

We treat AI workflows identically to any other operational capability or capital investment: it must produce measurable traction, capacity, or margin expansion, or it gets cut. We do not tolerate technology vanity projects.

During quarterly sessions, any AI initiatives must show up as measurable Rocks or Measurables on your Scorecard. When we review the prior quarter's performance, we evaluate automation and AI against three strict criteria:
- Time reclaimed. Did the automation measurably reduce the hours a seat spends on tactical administrative work, and did those hours translate into higher-value activities or reduced payroll overhead?
- Process reliability. Did replacing a manual workflow with an AI tool reduce error rates and improve cycle time, or did it introduce operational friction that required human intervention to clean up?
- Institutional capture. Is the automation documented within your Core Processes so that institutional knowledge is preserved in your systems rather than trapped in an individual specialist's head?

If a leadership team member committed to an AI-driven Rock and returns at the quarterly session with a tool that nobody uses or that created operational complexity without quantifiable business benefit, that Rock is marked incomplete. We put the failed workflow directly onto the Issues List and use IDS to determine whether to fix the implementation, re-train the team, or terminate the software entirely.

AI must serve the business strategy outlined in your V/TO, not distract from it. By forcing automation to pass the same quarterly accountability tests as your revenue and hiring goals, we ensure your operational investments drive tangible enterprise value.