What is AI value tracking?
AI value tracking is the ongoing measurement of whether a deployed AI system delivers what its business case promised. It compares three kinds of figures with a baseline recorded before launch. Outcome measures are business results, such as resolution time or error rates. Usage measures, such as how often staff accept its suggestions, show whether people rely on the system. Cost measures cover hosting, model usage, and the staff time spent reviewing output. Model monitoring is a separate discipline that checks technical health, such as accuracy and drift.
AI value tracking in practice
A managed service provider deploys an agent that categorizes incoming help desk tickets, routes them, and drafts a first reply for the technician. Before launch, the provider records a quarter of misrouted tickets, response times, and tickets closed per technician. A monthly report then adds how often technicians accept the suggested category and how heavily they edit each draft. Routing improves quickly, but drafts for one client are rewritten almost every time. The cause is missing documentation, and adding that client’s runbooks to the agent’s knowledge base fixes it.
What to watch for
- Record the baseline before launch, because it cannot be reconstructed reliably afterward.
- Seasonal swings and staffing changes can hide or mimic an AI effect, so compare like periods or a group without the system.
- Retire use cases that miss their targets after a fair trial, and move the budget to the next candidates on the list.