A 'game-changing AI tool' goes viral every few days, yet only a handful ever stay in our daily workflow. The issue isn't supply — it's that most people decide by 'looks cool' instead of 'solves my problem'. We use a 6-dimension scorecard; only above the line do we consider adopting.
Dimension 1: Onboarding cost
A tool that needs 3-day approval, an API key, and 20 pages of docs before the first result will likely rot in bookmarks. We score high for 'first result in 5 minutes' and deduct for 'needs coding' unless it targets developers.
Dimension 2: Output consistency
Run the same prompt three times; huge variance means it doesn't belong in a standard pipeline. For copywriting or code, consistency beats 'occasionally brilliant'. We test one fixed prompt three times.
Dimension 3: Scenario fit
Ignore feature lists. The real question: does it solve a task you face this week? If it's 'maybe someday', bookmark it, don't adopt now. More tools means more switching cost.
Dimension 4: Privacy & compliance
- Will your inputs be used to train the model?
- Does it offer enterprise-grade handling (e.g. no logs)?
- Safe to use with customer or confidential data?
Minor for hobbyists, but for client or internal data this dimension is a veto.
Dimension 5: Price & value
Is the free tier enough for daily use? Does pay-as-you-go get pricier than a sub at scale? We value a 'use-more-without-regret' curve over 'first month free'.
Dimension 6: Lock-in risk
Can you export your content? Open API? Active community? If the vendor raises prices or shuts down, how fast can you leave? Treat 'able to leave anytime' as safety.
How to use the scorecard
Score each 0–5; below 18 weighted we don't adopt. We pin it in team docs — when someone pitches a tool, score first, then meet. Cuts wasted debate.
A good tool isn't defined by what it can do, but by what it lets you stop doing.