Measuring Automation Results in the First 90 Days

If you've just deployed new bots, workflows, or AI copilots and stakeholders are asking "so, what did we get?", you're not alone. Measuring automation results in the first 90 days is one of the most common requests we see.

Featured Snippet: How to Measure Automation Results in the First 90 Days

1. Define the goal in business terms (time saved, cost avoided, revenue protected).
2. Pick 5–7 automation KPIs (cycle time, error rate, SLA hit rate, cost per ticket, CSAT/NPS, adoption).
3. Capture a baseline for 2–4 weeks pre-launch or from historical logs.
4. Instrument events now (start, success, exception, retry, human handoff).
5. Create a 30-60-90 dashboard view in your BI tool.
6. Set weekly targets and thresholds.
7. Run A/B or before/after comparisons with matched cohorts.
8. Quantify dollars: time saved × loaded hourly rate + error cost avoided.
9. Publish a one-page readout with wins, risks, and next 2 bets.

Problem: Why Measuring Automation in 90 Days is Hard

Most teams launch automation to "save time," but they don't agree on which time, for whom, or how to attribute the gain. Data is scattered across orchestrators, CRMs, and ticketing systems.

The 30/60/90 KPI Framework

Pick the right KPI families:

• Efficiency: hours saved, cycle time, queue time, tasks per FTE.
• Quality: error rate, rework rate, rollback frequency.
• Revenue/Throughput: conversion rate, pipeline velocity.
• Experience: SLA attainment, first-response time, CSAT/NPS.

Suggested 30/60/90 Targets

| KPI | Baseline | 30 days | 60 days | 90 days |

|-----|----------|---------|---------|----------|

| Cycle time (hrs) | 16.0 | 12.8 (-20%) | 10.4 (-35%) | 9.6 (-40%) |

| Error rate (%) | 5.0 | 3.8 (-24%) | 3.0 (-40%) | 2.5 (-50%) |

| Manual hours/week | 120 | 96 (-20%) | 84 (-30%) | 72 (-40%) |