AI Business Case and Value Realization
AI Business Case and Value Realization
This domain tests whether you can build a defensible AI business case and prove value after deployment. Questions often target baselines, total cost of ownership, benefit attribution, and stage-gated funding.
Core rules and concepts
- Define a baseline before launch: current cost, cycle time, error rate, conversion, or risk loss. Without a baseline, benefits are unprovable.
- Separate benefit types: cost reduction, revenue uplift, risk reduction, and experience or quality. Quantify each with an owner and measurement method.
- Include full costs: discovery, data preparation, build, integration, compute, licensing, MLOps, change management, compliance, and run support.
- Use NPV, payback, and sensitivity analysis; show what happens if adoption is lower or data quality is worse.
- Benefits realization: assign a metric owner, review cadence, and stop or scale criteria. A pilot is not a benefit until it changes a production metric.
Worked example
A call-center triage model costs $600k to build and $250k per year to run. It routes 1M calls per year; baseline misroute rate is 18%, and each misroute costs $2.50 in repeat handling. Target misroute rate is 12%, saving 60k misroutes times $2.50 = $150k per year. That alone does not cover run cost. The case adds agent time savings of 40k hours times $22 = $880k per year, but only if adoption reaches 70%. At 70%, benefit is $616k; net annual benefit after run cost is $366k; payback is about 1.6 years on build cost, before discounting. Reasoning: the case must state adoption assumptions and not count full benefits at 100% adoption.
Traps
Double-counting saved time and headcount; ignoring run and change costs; claiming benefits without a baseline; pilot purgatory; and treating cost avoidance as cash unless Finance agrees.