Boardrooms want agentic AI; operations wants proof. Too many pilots stall because success is defined as "the demo worked" instead of measurable business outcomes. If you can't quantify cycle time, quality, and cost, finance won't fund scale—and rightly so.
The fix is a small scorecard agreed before the pilot ships, tied to a single workflow with clear owners and baselines.
Core KPIs we recommend
- Cycle time: Median minutes from trigger to resolution vs. manual baseline (e.g. ticket triage, quote draft).
- First-pass acceptance: Share of agent outputs used without major edits—signals trust and prompt/tool fit.
- Human override rate: How often reviewers reject or rework agent actions; should fall as guardrails improve.
- Error rate on tool calls: Failed API calls, wrong records updated, or policy violations per 1,000 runs.
- Cost per completed task: Model + infra + reviewer time, normalised per successful outcome.
Design pilots for measurement
Pick one high-volume, rules-heavy workflow—password resets are too simple; full autonomous procurement is too risky. Sweet spots include L1 support classification, internal policy Q&A, and draft generation from CRM or ERP data.
Run A/B or shadow mode for two weeks: agent proposes, human executes. Compare timestamps and edit distance. Only then grant limited auto-execution on low-risk steps.
When to scale—or stop
Scale when cost per task beats manual at stable quality, override rate is trending down, and compliance sign-off covers expanded tool access. Stop or redesign when errors cluster around the same failure modes—usually missing data, ambiguous policies, or tools without idempotency.
We help enterprises define these scorecards and ship pilots with logging and dashboards baked in from day one—not bolted on after the hype fades.