How a distributor went from "we know AI matters" to a data agent their staff use every day, and kept going.
The client is a B2B distributor with field sales reps, warehouse and delivery operations, and a customer rewards program. Leadership knew AI mattered, but had no shortlist of where it would pay off.
Meanwhile, reporting questions queued up behind developers, and the people who needed answers waited.
We mapped the daily work of 11 roles across 8 business areas. For each role, we looked at where time went, where work waited, and what AI could do about it.
The result was 46 AI opportunities, each sized from S to XL with build-vs-buy calls and risks, and a top 10 to ship first.
Work on a conversational data agent began one week after the roadmap. Staff ask questions in plain English and get answers from live business data. The agent can also open requests on a user's behalf, so a question can turn straight into action.
Next came sales-rep dashboards built from a catalog of action-oriented widgets: stores off their ordering rhythm, the biggest buying drops, proven services a store isn't selling yet. A second agent turns a rep's plain-language request into a saved dashboard view.
Next come proactive agents that watch the data and flag what needs attention before anyone asks.
The roadmap-to-production story on a single page, with the method and the results. Easy to forward to your leadership team.
An AI agent for their website, then AI agents for sales and technical support.
Monthly fractional AI advisory to the leadership team.
An open-source harness that makes AI coding agents dependable on real, mature codebases: specs grounded in the codebase, independent fresh-context review, evidence for every claim, a human merge gate, and a weekly self-improvement loop. It's an open-source project I lead, in daily use by dozens of developers.
Thirty minutes with Daniel. No pitch deck: you talk about your business, I tell you honestly where AI fits and where it doesn't.