Evaluating AI: The Questions That Actually Matter

You have 47 AI tools to choose from. Half of them promise to "revolutionize your workflow." None of them will fit your actual workflow until you ask the right questions.

Here's what to ask instead of "Is this AI good?"

1. What Problem Does It Actually Solve?

Not what problem the vendor says it solves. What problem does it solve in your context, for your team, right now?

If the answer is "everything," the answer is really "nothing." Everything-tools are never great at anything.

2. What's the Cost of Being Wrong?

If the AI output is wrong 10% of the time, how much damage does that do?

• Customer support automation that misses context? You've got angry customers.
• Content generation with factual errors? Now you're fact-checking everything manually.
• Decision support that gives bad recommendations? You've built a confidence trap.

The lower the cost of error, the earlier you deploy. The higher the cost, the more human eyes before launch.

3. Who Actually Uses This?

Not the CTO. The person doing the actual work.

If they hate the interface, the tool is dead. No amount of backend power fixes that.

4. How Does It Change If You Scale It?

Works great for one person. What happens when the whole team uses it—whether that’s five people or five hundred?

• Does the cost per use stay flat, or does it explode?
• Does the quality degrade at scale?
• Can your team handle the increased volume of inputs?

5. What Happens When the Vendor Changes the Terms?

Because they will.

API prices increase. Features get deprecated. Service shuts down. You're betting on someone else's infrastructure.

What's your escape plan if they change the game tomorrow?

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The best AI tool isn't the one with the highest benchmark. It's the one that solves your real problem, where the cost of failure is acceptable, and your team will actually use.

Everything else is just good marketing.