The most expensive AI project is the one that should never have started. In our experience the failure is rarely technical: the model works, but it solves a problem nobody measured, or it sits on top of a process that was broken long before AI arrived.
That is why our engagements start with a fixed-fee Discovery package: four meetings, one summary report, a prioritized roadmap. Here is the thinking behind it.
1. Inventory before ideas
We list the software, platforms and tools already in place, then map the workflows and the repetitive tasks around them. Most companies discover duplicated tools, manual exports between systems and reports nobody reads. Fixing those is often the first win, and it costs nothing in AI.
2. Score by impact and complexity
Every candidate initiative gets two scores: potential impact on cost, revenue or risk, and complexity of data, integration and change management. The first project should sit in the high-impact, low-complexity corner. Forecasting on data you already collect is a classic example; a chatbot on a knowledge base you do not have is not.
3. Define the KPI before the model
If we cannot name the number that will move, the project waits. Response time, stockouts, review time per contract: pick one, measure it today, and agree on the target.
4. Plan the road, not the destination
The report ends with a preliminary roadmap: what to do first, what to prepare next, and what to leave alone for now. Development and integrations are quoted separately, so you decide with the facts in hand.
Four meetings. Clear priorities. No wasted investment. That is the whole point.