Picking an AI vendor is one of those decisions that looks simple until you're six months in and wondering where your budget went.
The market is loud right now. Every platform claims to be the fastest, the most accurate, the easiest to integrate. Most of them are selling you a demo, not a product.
Here is what actually matters when you sit down to evaluate an AI vendor.
Start With the Problem, Not the Demo
Before you watch a single pitch, write down the specific thing you need AI to do. Not "improve our workflow" or "automate customer service." Something specific: "Reduce the time our team spends on first-reply emails from 4 hours a day to under 1 hour."
If you go into vendor conversations without a defined problem, you will be impressed by demos that have nothing to do with your actual situation. Vendors are very good at showing their strongest use cases. That is their job.
Your job is to force them to show their system working on your problem, with your data, in your context.
Ask About Failure, Not Features
The question most buyers forget to ask is: what happens when this fails?
Every AI system fails sometimes. The difference between a good vendor and a bad one is not zero failures. It is how failures are handled. Ask them:
- What does the system do when it does not know the answer?
- How does it flag low-confidence outputs before they reach a customer?
- What does escalation look like?
- Can you show me a real example of a failure and how your system handled it?
A vendor who cannot answer these questions clearly is selling you something they have not stress-tested.
Look at Integration Depth, Not Integration Count
The marketing slide says "integrates with 200+ tools." That number is usually meaningless.
What you need to know is the depth of integration with the two or three tools you actually use every day. A shallow integration that syncs basic data is not the same as a deep integration that reads full context, writes back results, and handles errors gracefully.
Ask them to walk you through exactly what data flows where, in both directions. Ask what happens when your CRM is slow or returns an error. Shallow integrations fall apart the moment something unexpected happens.
Check the Data Handling Story
Your customers' data is going into this system. You need to know where it lives, who can see it, how long it is retained, and what happens if you end the relationship with the vendor.
Ask directly: Is customer data used to train shared models? Ask for this in writing. Some vendors have clean answers. Others hedge. The hedging is the answer.
If you are in a regulated industry, this is not optional. Even if you are not, your customers expect their information to be handled responsibly.
Run a Real Pilot With Real Costs
The best way to evaluate any AI vendor is a small paid pilot on a real use case. Not a proof of concept on dummy data. A real workflow, with real volume, tracked against real outcomes.
Set a time limit (30-60 days), define your success metric before you start, and calculate the cost per output. Not the monthly platform fee. The cost per email handled, per ticket resolved, per lead qualified.
If the vendor resists a paid pilot and wants you to jump straight to an annual contract, that tells you something.
One Final Check
Before you sign anything, ask yourself: if this vendor disappeared tomorrow, how would we handle this process manually?
If you do not have an answer, you may not be ready to automate that process yet. AI should remove friction from something that works, not create a dependency on something you do not fully understand.
At Othex Corp, we help businesses work through exactly these questions before committing to any AI integration. If you are about to sign an AI vendor contract and want a second opinion, visit othexcorp.com.
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