Evaluating AI Tools & Vendors

Every AI vendor’s demo is dazzling, because demos are built to dazzle. The real question isn’t “is this impressive?” — almost all of them are. The question is “will this still be a good decision in two years, on our data, at our scale, with our risks?” Evaluating an AI tool is a skill you can learn, and most of it is just knowing which boring questions to ask before the excitement carries you to a signature. Get a handful of them right and you’ll dodge the expensive surprises that show up months after the contract.

Build or buy?

The first fork is build vs. buy: do you make the tool yourself or pay someone for theirs? Buying is faster, cheaper to start, and someone else handles the upkeep — but you depend on their roadmap and their priorities. Building gives you control and a tool shaped exactly to your needs, but it costs real engineering time and you own every bug forever. The honest answer for most teams is “buy, unless this capability is a genuine competitive advantage.” You don’t build your own email server; you probably don’t need to build your own chatbot either. Save building for the few things that are truly core to what makes you you.

The questions the demo won’t answer

Once you decide to buy, four areas separate a safe choice from a regret. The first is data residency and training: where does your data physically live, and — crucially — will the vendor train their models on it? For anything confidential, you want a clear written “no training on your data” and to know which country or region holds it, because that affects which laws apply. The second is security: ask for a SOC 2 report (an independent audit of their security controls) or an equivalent like ISO 27001. A serious vendor has one ready; a hesitant one tells you something. The third is accuracy and benchmarks: vendors love a headline number, so ask on what task, on whose data, compared to what? The number that matters is how it performs on your real examples, which is why a small pilot beats any slide.

Cost and the trap of lock-in

The fourth area is total cost — and the sticker price is the smallest part of it. Add the cost of integrating it, training your people, reviewing its output, and the way many AI tools bill per usage, so costs balloon as adoption grows. Then look hard at lock-in: how painful would it be to leave? If your data, your prompts, and your workflows can’t be exported, you’re not really choosing a tool, you’re adopting a dependency. Favor tools that let you get your data out and that don’t quietly make themselves impossible to replace. The cheapest tool to enter can be the most expensive to escape.

Rule of thumb: never sign on the strength of a demo. Run a small pilot on your own data, get the security report in writing, and confirm you could leave before you commit to staying.

A simple evaluation checklist

Before you recommend any AI tool, walk this short list: Fit — does it solve a real problem we actually have? Data — where does our data live, and will it be used for training? Security — is there a SOC 2 or equivalent? Accuracy — how did it do on our examples in a pilot? Cost — what’s the true total, including usage and review? Exit — can we export our data and switch later? Owner — who on our side is accountable for it? If a tool clears all seven, you’re making a decision. If it stumbles on data, security, or exit, slow down — those are the ones that hurt later.

Spot it: red flag or green light?

Read each situation and decide for yourself, then tap a card to flip it and check your answer.

Sort the evaluation checklist

Drag each question into the checklist area it belongs to — or tap an item, then tap a bucket. Hit Check placement when you’re done.

How to use it

You can steer any vendor conversation with a few grounded questions: “Will you train your models on our data, and where does it live?” “Can you share your SOC 2 report?” “Can we run a two-week pilot on our own examples before committing?” “If we leave in a year, how do we get our data out?” “What’s the all-in cost at the scale we’d actually use?” Asking these doesn’t make you the difficult one in the room — it makes you the person who reads the fine print so the team doesn’t learn it the hard way.