Productivity & daily use
AI Decision Matrix
Name your criteria, weight what matters, score each option one to ten. The maths does the rest — and shows you why it chose.
1. Criteria and weights
2. Score each option (1–10)
3. Result
Option A wins by 0.3 points. That is a tie in practice — pick on gut feel or add a criterion that separates them.
| Rank | Option | Weighted score | Strongest | Weakest |
|---|---|---|---|---|
| 1 | Option A | 6.7 / 10 (67%) | Quality of outcome | Risk |
| 2 | Option B | 6.4 / 10 (64%) | Time to value | Cost |
| 3 | Option C | 6.2 / 10 (62%) | Cost | Time to value |
How the matrix works
Every option's score is multiplied by its criterion weight, summed, then divided by the total weight so the result lands back on a one-to-ten scale. Nothing is hidden and no model is involved — the same numbers give the same answer every time.
Weights matter more than scores. If two options finish within half a point, the matrix is telling you the decision is genuinely close: either add the criterion you have been avoiding, or accept that the choice is cheap to reverse and move.
The strongest and weakest columns show which criterion contributed most and least to each total. That is usually more useful than the ranking itself, because it tells you what to negotiate or fix before committing.