Myth #4: Start With the Highest-ROI Use Case.

Why Not Start With the Biggest Opportunity?

This is the fourth myth in the series, and it sounds like common sense. If you're investing in AI, why wouldn't you start with the biggest opportunity? The largest cost reduction. The highest productivity gain. The process everyone agrees is broken.

Unfortunately, that's exactly where many AI programs go wrong.

The use cases with the biggest projected ROI are usually the most complex. They span multiple functions, rely on fragmented data, involve countless business exceptions, and often require decisions that even experienced employees don't make consistently.

On paper, they promise the greatest value. In practice, they're often the least ready for AI.

Successful organizations think differently. Instead of asking, "Which use case has the highest ROI?", they ask, "Which use case has the highest probability of success?" Those are rarely the same.

Start Where AI Is Ready.

Research supports this approach. McKinsey has identified data readiness as one of the biggest constraints preventing organizations from scaling AI successfully. The challenge isn't that today's AI models aren't capable enough. It's that many organizations try to automate decisions before the underlying data, processes, and governance are ready.

That's why the first AI project shouldn't be selected on business value alone. It should be selected on AI readiness.

These projects may not deliver the largest financial impact, but they deliver something just as important: momentum.

Early Wins Compound.

A successful first deployment builds confidence in AI, creates reusable data pipelines, establishes governance, and gives teams practical experience deploying AI in production. It also creates trust among business users, making future projects significantly easier.

The opposite is also true. When organizations start with their biggest challenge, they often spend months cleaning data, debating business rules, and trying to account for every possible exception before anyone sees value. The project stalls, enthusiasm fades, and AI earns a reputation for being expensive, slow, and difficult to implement.

Early wins don't just deliver results. They create the confidence, capabilities, and momentum needed to transform the rest of the business.

Two Inventory Initiatives.

The first attempts to optimize inventory across every warehouse, every product category, every supplier, and every region. It involves thousands of SKUs, changing demand patterns, promotions, supplier constraints, and countless business exceptions.

The second focuses on replenishment decisions for a single product family in one distribution center. The data is reliable, planners already follow a consistent process, and performance can be measured within weeks.

The second project will almost certainly deliver value sooner. More importantly, it creates the confidence, capabilities, and organizational trust needed to tackle the larger opportunity next.

Think of AI transformation like climbing a mountain. You don't begin with the steepest section simply because it's closest to the summit. You establish a secure route, build confidence, and then continue climbing. AI transformation works the same way.

Score Every Use Case.

Before prioritizing AI initiatives, evaluate every use case across two dimensions: business value and implementation readiness. At blueclip, we assess readiness using six simple questions.

The best place to start isn't necessarily the use case with the highest projected ROI. It's the one that combines meaningful business value with high implementation readiness. That first success becomes the foundation for every project that follows.

Score your AI use cases with a free blueclip readiness assessment →