Bottom line
Do not choose an AI tool by feature count. Judge whether it completes one recurring job reliably, under conditions you can review. Method & Miles uses a 100-point scorecard plus three hard-fail conditions.
AI TOOLS
5 things to check before choosing an AI tool
A practical way to evaluate workflow fit, repeatability, data handling, portability, and real cost before you subscribe.
Read the related Korean article →The Method & Miles 100-point scorecard
- Job fit — 25: can it complete the recurring job from useful input to reviewable output?
- Repeatability — 20: is quality variation manageable across repeated runs?
- Data control — 20: can you verify training use, retention, deletion, and access rules for your exact plan?
- Portability — 15: can the output and working data move into the tools you already use?
- Total cost — 15: does the value still hold after subscription, usage charges, review, and rework time?
- Adoption friction — 5: can the intended user learn and repeat the workflow?
This is a Method & Miles editorial framework, not a vendor or industry standard.
1. Define one job before you compare products
“Use AI in sales” or “get an AI assistant” is too broad. Narrow the evaluation to one sentence that includes the input, desired output, and the point where a person must approve the result.
For example: “Turn last week’s CRM notes into a Monday list of ten accounts that need follow-up, with the evidence behind each suggestion.” If a tool cannot finish that path, extra features should not rescue its score.
2. Repeat the same test at least three times
A polished first answer is weak purchase evidence. Use the same real material, instruction, and evaluation criteria. Record whether the job finished, how many edits were needed, what factual gaps appeared, and how long it took to reach an acceptable result.
If Method & Miles has not run a product test, we do not assign a made-up repeatability score. Treat an untested product as unverified until you can test it yourself.
3. Apply three hard fails before looking at the total
Even a high numerical score should not override an unresolved condition below.
- Unknown data terms: you cannot verify the relevant plan’s training, retention, deletion, or access rules.
- No practical exit: important output cannot be exported or moved into a usable standard format.
- The core result is not repeatable: the same job repeatedly creates enough rework to erase the benefit.
OpenAI, for example, publishes separate business-data terms. Do not assume another provider—or a consumer tier—uses the same rules.
4. Add rework to the monthly cost
A cheap subscription can be expensive if every result needs thirty minutes of repair. Use a simple model:
- Monthly total cost = subscription + variable usage + rework hours × value of your time
- Monthly value = hours genuinely saved × value of your time
- Net monthly effect = monthly value − monthly total cost
Hypothetical example: if a tool costs $30 a month, saves five hours, but adds one hour of review, the important number is the value of the net four hours—not the $30 sticker price. Replace the assumptions with your own numbers.
5. End with a conditional decision, not a universal winner
Write down the conditions that would change your choice. A team handling sensitive data may weight data control more heavily. A solo user may care more about job fit and total cost. A shared workflow may require stronger administration and export controls.
This makes the framework reusable when the market changes. You can rescore a new product without rebuilding the decision from zero.
Primary sources
Product terms can change. Recheck the exact plan documentation before adoption.
Editorial note: On 2026-09-20 we rebuilt this recovered article around the Method & Miles 100-point scorecard, hard-fail rules, and total-cost model. It is a reusable evaluation framework, not a claim that we personally tested every AI product.