We are hired to build intelligent systems, so there is a commercial incentive to find a modelling problem in every brief. Resisting it is the fastest way to build trust — and often the fastest way to a result.

Case One · The Policy Was the Problem

A client wanted a model to improve approval rates. The existing policy had forty-one rules, nine of which contradicted each other and four of which had been added for a product discontinued in 2021. We mapped the rules, removed the dead ones, and approval rates moved more than any model would have delivered in the first year. Total effort: three weeks.

Case Two · Not Enough Outcomes

A specialist insurer had 900 claims a year in the segment they wanted to automate, with an eighteen-month outcome horizon. That is not a training set; it is an anecdote. We built a deterministic triage with explicit thresholds and a review queue, and instrumented it so that in three years there will be enough clean data to revisit the question honestly.

Case Three · The Regulator Would Never Accept It

The decision fell in a category where the client's regulator expects a fully specified basis. A model could have been made explainable, but the approval path would have consumed a year. The rules engine shipped in eight weeks and freed the team to apply models where nobody was watching so closely — pricing analysis and portfolio monitoring.

The Test We Use

• Can you state the current policy on one page? If not, fix that first.
• Do you have thousands of clean outcomes with a horizon shorter than the business cycle? If not, defer.
• Would the approval path take longer than the payback period? If so, choose the boring option.
• Is the value in the decision, or in seeing the decision? If it is the latter, you need reporting, not a model.

None of this is an argument against models. It is an argument for earning the right to use one — and for saying so early, when it still costs a conversation rather than a quarter.