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IBM

AI Business Cases and ROI Models

IBM via Coursera

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Overview

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Most AI initiatives are approved or rejected on the strength of a single document, and most of those documents fail for structural reasons rather than financial ones. In this course, you learn to build the case a finance leader can check instead of one they have to trust. You structure a business case for AI investment approval, separate hard return from soft return and strategic option value, and build an ROI model at advisory depth: the drivers, the timing of benefits, and the assumptions finance will challenge. You also price what most models leave out, including rework that lands on another team and the quality decline that rising volume conceals. Then you define value metrics, baselines, and first- and second-year targets, and assemble a decision-ready business case tied to the readiness findings and roadmap it depends on. Your method can return an honest answer of "not on these numbers," because a case that can only say yes is a proposal, not an analysis. No spreadsheet expertise or coding is required, and every activity runs in a web browser.

Syllabus

  • Structuring the AI Business Case
    • This module develops AI business case, ROI justification, and investment approval skills for enterprise AI initiatives. Learners structure a credible business case around value drivers, benefit traceability, P&L impact, scenario analysis, sensitivity, and capital budgeting under uncertainty. They distinguish hard ROI, soft ROI, and strategic option value; identify the landing line and accountable owner for each benefit; separate efficiency gains from new-value claims; and adjust pilot results for production conditions. The module also covers executive summary logic, investment committee expectations, benefit-versus-cost ranges, and the role of assumptions in decision quality. It prepares advisors and management analysts to build decision-ready AI investment cases that finance leaders can verify, challenge, and approve—or decline—on transparent evidence.
  • Building the ROI Model
    • 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.
  • Defining Value Metrics
    • 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.
  • Making the Decision-Ready Case
    • 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.

Taught by

LearnQuest Network

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