AI initiatives don’t fail because of the technology

Most AI initiatives don’t fail because of the technology. They fail because teams jump straight to building. A use case gets picked. A solution starts taking shape. A pilot gets spun up. But the hard questions haven’t been answered yet:

• What problem are we actually solving?
• Where is the real business value?
• Do we have the data to support it?
• How would this work in a real workflow?

That’s where things break down. At Anevra Consulting, I’ve been using a simple approach to avoid that trap: The AI Opportunity Sprint

Before building anything, we take a step back and structure the opportunity. The goal isn’t to slow things down. The goal is to make sure what gets built actually works. A typical sprint focuses on four areas:

1. Opportunity Discovery:  Identify and prioritize use cases based on real business impact.
2. Value Mapping:  Define how the solution creates value, where it fits in the workflow, and what success looks like.
3. Data & Feasibility Analysis:  Assess data availability, constraints, and whether the use case is realistically achievable.
4. Pilot Architecture:  Design a solution that is not just a prototype, but something that can evolve into production.

The difference is subtle but important. Instead of asking: “What can we build with AI?”  We ask: “What should we build, and why?” That shift is what turns experimentation into real capability.