The AI vendor market has exploded. Every consulting firm, boutique agency, and freelance developer now offers "AI services." Choosing the wrong partner doesn't just waste budget — it can set your AI roadmap back by 12 to 18 months. The right questions, asked early, save enormous pain later.
Here are the 10 questions we recommend every CTO ask before signing an AI engagement — and what to listen for in the answers.
1. Can you show me a production system you've built — not a demo?
Demos are easy. Production systems are hard. Ask for a reference customer, a case study with real metrics, or access to a system that runs live. If a vendor can only show polished demos, that's a red flag. Every credible AI firm has at least one production deployment they can talk about in detail.
2. How do you handle model drift and performance degradation over time?
AI models don't stay accurate forever. Data distributions shift, edge cases emerge, and a model that performs at 94% accuracy on day one can degrade to 78% six months later without anyone noticing. Ask how the vendor monitors model performance post-deployment and what their retraining cadence looks like. Vendors who can't answer this haven't shipped real production systems.
3. Who actually builds the solution — and will they be on my project?
Many firms sell senior talent in the pitch and deliver junior talent on the project. Ask specifically: who will be the lead engineer on this engagement? What is their background? Have they worked on similar problems before? Request bios, not just company credentials.
4. Where does my data go — and who has access to it?
This question surfaces data governance practices immediately. Does the vendor use your data to train shared models? Does it leave your infrastructure? Is it processed through third-party APIs without your knowledge? The answer should be specific and contractually enforceable, not a vague assurance about "taking security seriously."
5. What happens if the project doesn't hit the success metrics we agreed on?
Push for accountability structures. Does the contract define success criteria? Is there a remediation plan if targets aren't met? Is payment tied to any milestones or outcomes? Vendors who are confident in their work are willing to put this in writing. Vendors who resist this question are telling you something important.
6. How do you evaluate whether AI is the right solution for this problem?
A trustworthy AI vendor should sometimes tell you that AI isn't the right tool. If every problem gets an AI solution, you're working with someone who's optimizing for their own revenue, not your outcomes. Look for vendors who talk about when not to use AI — that intellectual honesty is a strong signal of genuine expertise.
7. What model or models will you use, and why?
This question separates vendors who make deliberate technical decisions from those who default to whatever is trending. There is no universally best model — the right choice depends on your latency requirements, cost constraints, data privacy needs, and task complexity. A good answer will reference tradeoffs, not just a single model name.
8. What does handoff look like — will our team be able to maintain this?
AI systems that only the vendor can maintain create permanent dependency. Ask about documentation, knowledge transfer, and whether your internal team will be trained on the system. The goal of a good engagement is for your team to be able to operate and iterate on the solution independently. If the vendor's business model depends on you not being able to do that, you have misaligned incentives.
9. How do you test and validate the system before go-live?
Rigorous evaluation is what separates AI engineering from AI experimentation. Ask about their testing methodology: do they maintain held-out test sets? Do they run adversarial evaluations? How do they test for edge cases and failure modes? A vendor with strong evaluation practices will have specific, detailed answers. A vendor without them will speak in generalities.
10. What could go wrong — and how would you handle it?
This is the most revealing question on the list. Vendors who are honest about risks — hallucination, latency spikes, integration failures, data quality issues — are vendors who have actually shipped systems and learned from what broke. Vendors who promise smooth sailing haven't done this enough times to know what can go wrong. Trust the ones who can describe failure modes in detail.
The Underlying Filter
Across all ten questions, you're looking for one thing: specificity. Credible AI vendors give specific answers — specific examples, specific metrics, specific tradeoffs. Vendors who respond with vague assurances and generic platitudes about "leveraging AI to drive business value" have not built the systems they're selling you.
The AI market will continue to mature and consolidate. The firms that survive will be the ones with real engineering depth and real production track records. Use these questions to find them.