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.

AI Business Cases and ROI Models

AI Business Cases and ROI Models
This course is part of IBM Enterprise AI Transformation and Consulting Professional Certificate

Instructor: LearnQuest Network
Included with Learn more
Recommended experience
What you'll learn
Structure an AI business case that separates hard return, soft return, and strategic option value by the evidence behind each.
Build an advisory-depth ROI model that prices hidden costs such as downstream rework, quality decline, and vendor risk.
Define baselines, value metrics, and two-year targets, pairing each efficiency gain with a quality check that could expose it.
Assemble a decision-ready business case that can honestly recommend approval, delay, or stopping.
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