How to Know If AI Is Right for Your Business

Question 1

Where does my data go — and who owns it after the engagement ends?

This is the first question and the most important one. Ask it before anything else. Some AI products are trained on your inputs. Some store your conversations. Some share data across their customer base. Some are built on third-party infrastructure with their own data policies that the vendor has no control over.

What you need to know: does your proprietary information — your client data, your pricing, your internal processes — stay yours? Where is it stored? Who has access to it? What happens to it if you cancel? If the vendor cannot answer this clearly and in writing, that is your answer.

Question 2

What does it do when it doesn't know something?

This question separates real AI implementations from dangerous ones. Every AI system will eventually encounter a question outside its knowledge base. What happens next is a design decision — and it tells you everything about how carefully the system was built.

The right answer: it says so, clearly, and routes to a human or a defined next step. The wrong answer: it generates something confident and incorrect. That is called hallucination, and in a customer-facing system it means your business is on record for whatever the AI said.

Ask to see it fail. If the vendor won't show you an edge case, they are not confident in their guardrails.

Question 3

What does implementation actually look like — and what do you need from me?

"We'll have you up and running in 48 hours" is a red flag, not a selling point. A real AI implementation requires your input. Your services. Your pricing. Your escalation paths. Your voice. Your edge cases. Your industry-specific knowledge. If a vendor is not asking you for any of that before they promise a deployment timeline, they are not building something for your business. They are installing something generic and hoping you don't notice.

Ask specifically: what information do you need from us, how long does onboarding actually take, and what does the training process look like? The depth of that answer tells you whether they understand your business or just their own product.

Question 4

What does success look like — and how will we measure it?

If a vendor cannot define success in measurable terms before the engagement begins, they are not accountable to any outcome. Push for specifics. Not "improved efficiency" — what metric, what baseline, what target, what timeline? Not "better customer experience" — what does that mean in a number you can track?

The businesses losing money on AI right now signed contracts with vague promises attached. The ones winning have documented outcomes with defined milestones. Make the vendor define what winning looks like before you write the check.

Question 5

Who builds it, who owns it, and what happens if we part ways?

This question has two parts and both matter. First: who is actually doing the work? Is it the person you are talking to, or is it being outsourced to a team you will never meet? That is not a judgment — it is a risk assessment. You need to know who is accountable.

Second: when the engagement ends, what do you walk away with? Do you own the knowledge base? The accounts? The trained model? The codebase? Or does the AI live on their platform, accessible only as long as you keep paying?

Ownership at the end is not a detail. It is the whole point. An AI system you cannot access, modify, or take with you is not an asset. It is a dependency.