Baselines and ROI Frameworks

The company reports that the AI-assisted complaint workflow improved performance by 40%. The claim cannot yet inform an investment decision because the team never measured pre-pilot handling time. A percentage change needs a known starting point, a defined measurement period, and an accountable owner before we can treat it as evidence of value.

Baseline evidence for complaint handling

A baseline is a measured view of the current workflow before the AI change. For the complaint pilot, the operations owner should capture a representative pre-AI period, such as eight weeks that include ordinary complaint patterns. The baseline should use the same KPI definitions that will be used after the pilot.

Measure the volume of eligible complaints, average handling time, fully loaded cost per complaint, quality outcomes such as reopen rate or reviewer corrections, and customer satisfaction (CSAT). Record which complaint types are included and excluded. A pilot that handles only simple complaints can look faster while shifting difficult work to the existing team.

A before-and-after comparison is useful when the incoming complaint mix stays comparable. When feasible, retain a control group, meaning a comparable group that continues with the existing workflow during the pilot. Comparing the pilot group with that group helps separate the AI effect from other changes, such as staffing levels, a policy update, or an unusually high complaint volume.

Seasonality also matters. A holiday-return period may increase volume and complexity even if the AI workflow performs consistently. We can compare equivalent periods, normalize results by complaint type, or use the control group to avoid crediting the pilot for a change caused by the business calendar. ...