This module develops practical skills for building an AI ROI model and AI business case that finance and executive stakeholders can trust. Learners examine AI costs, benefits, timing, payback period, discounted cash flow, net present value, staged investment, sensitivity analysis, and benefit realization at advisory depth. They learn to identify hidden costs of AI adoption, including verification and rework cost, change management cost, human review cost, inference cost, and cost transfers between teams or customers. The module also covers total cost of ownership, cost registers, AI spend governance, stage gates, and ROI assumptions finance will challenge, helping learners distinguish value genuinely created from effort merely relocated before recommending investment.

Define AI value metrics, AI KPIs, baselines, benchmarks, and first- and second-year targets that make AI ROI measurable and accountable. This module explains how to separate adoption and usage metrics from business value metrics, connect AI success metrics to indicators budget owners already review, and distinguish hard-dollar benefits from softer productivity gains. Learners practice measuring AI productivity with pre-intervention baselines, paired quality metrics, comparison designs, and attribution levels that support defensible claims. They also learn how leading and lagging indicators evolve as adoption matures, how to use proxy measures without overstating value, and how to build an AI measurement plan that proves AI value to finance while avoiding vanity metrics and unsupported ROI claims.

Build the executive decision-making skills needed to turn AI analysis into a decision-ready business case for investment approval. This module covers AI business case structure, executive communication, advisory-depth ROI, roadmap and readiness alignment, evidence maturity, conservative scenarios, cost and risk of inaction, and clear decision requests. Learners practice shaping a CFO-ready business case, investment committee memo, or board pre-read so executives can locate the return, timeline, assumptions, dependencies, and verification plan quickly. They also learn to separate strategic rationale from quantitative analysis, connect financial claims to evidence, and preserve a credible null recommendation when the numbers do not support proceeding. The result is stronger business case development for enterprise AI investment decisions.