Prioritizing AI Use Cases

Lesson summary

Research from MIT Sloan, BCG and others keeps finding the same thing: most AI projects never deliver their intended value or make it out of the pilot stage. This lesson argues that the problem usually isn’t the technology. It’s a shortage of planning and structure in deciding which AI ideas to pursue.

Ask around any office for AI ideas and you’ll get a hundred answers, half of which have nothing to do with AI. The lesson also warns about the “illusion of progress”: AI makes it easy to build clickable prototypes that look finished but have no real data, maintenance process or path to production.

It then lays out a four-part framework for scoring use cases, with weights you can adjust to fit your goals:

• Business impact: time or labor saved, revenue gained, or costs avoided, such as replacing a third-party vendor. Usually the easiest to quantify, at least on paper.
• Feasibility: does the data the idea depends on actually exist? Gartner found that 60% of AI projects without AI-ready data are abandoned. A great idea that needs months of data cleanup may belong on a backlog.
• Risk profile: especially important in finance. Does the use case touch financial reporting or a SOX control? That doesn’t make it a no-go, but it should bring in governance and leadership early.
• Adoption likelihood: is there real demand from the people doing the work, or is it top-down? Does it slot into existing workflows, and is there a manager willing to champion it?

The lesson points out that risk profile and adoption likelihood are the two dimensions teams most often overlook, and explains why a use case with a willing manager-level owner that fits existing workflows is close to a sure thing.

Key takeaways

• There’s no shortage of AI ideas; the shortage is in disciplined selection and planning.
• Projects usually fail because of the surrounding infrastructure: unclear problems, messy data, governance gaps or top-down mandates.
• Score every idea on business impact, feasibility, risk profile and adoption likelihood.
• Big top-down transformations need extra change management; small workflow-friendly wins scale more easily.