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Sorting the ideas

Why good models get built for the wrong problems

Scoring and ranking a portfolio of AI ideas

Checkpoint: reading the quadrants

Feasibility for real

The data readiness audit

Build, buy, or fine-tune

Checkpoint: choosing the approach

How to choose which AI idea to fund. Score value against feasibility, audit whether the data actually exists, and decide between an API, a fine-tune, and a custom model with evidence rather than instinct.

The model is rarely the reason a project dies. It dies because it was pointed at a problem
the business did not care about, or one the data could never support.

Finding the Right AI Use Case

<div class="simple-box-wrapper"> <div class="simple-box"> <div class="column"> <h3>Description</h3> <div class="ck-horizontal-line"><hr></div> <p>I'm a paragraph. Click to start writing that will inform, inspire, or encourage your community. We know you have so many things to share with the world. Happy editing!</p> </div> </div> </div>