An AI roadmap that paid for itself

Problem

The executive team was under pressure to "do something with AI" and drowning in vendor pitches, each promising transformation. Every department wanted a tool. Nobody could say which would actually pay off, or in what order. The risk wasn't inaction — it was spending a year and a budget on pilots that led nowhere.

Approach

We treated AI as a portfolio decision, not a technology one. Working with operations and finance, we:

• Inventoried twenty candidate use cases across the business.
• Scored each on value, feasibility, and data readiness — the same way you'd evaluate any capital investment.
• Killed the ideas that sounded impressive but lacked the data to work.
• Sequenced the survivors into a phased, fundable roadmap with clear owners and success measures.

The output wasn't a strategy deck that sat on a shelf. It was a prioritized plan finance could underwrite, starting with the three projects where the math was undeniable.

Outcome

Phase one focused on document processing and exception routing — unglamorous work that quietly removed well over a hundred manual hours a week. Because the roadmap led with provable value, it funded the more ambitious later phases out of its own savings, and the leadership conversation shifted from "should we do AI?" to "what's next on the roadmap?"