Most AI projects that fail don't fail because the technology wasn't ready. They fail because the partner over-promised, misjudged the data, or built something nobody used. The questions you ask before signing are the best protection you have.
Here are 10 to ask any AI implementation partner, and what a good answer sounds like.
1. Can you show me something you've built that's running in production?
Demos and pilots are easy. Production systems that people use every day are hard. Ask for a case study with real results, and ideally a reference you can call.
Good answer: a named or clearly described client, the problem, what was built, and measured results.
2. How will you test whether this works before we commit to a full build?
A serious partner will want to prototype on your real data first. Accuracy on demo data says little about accuracy on your messy invoices or support tickets.
Good answer: a short, fixed-cost prototype phase with an agreed success threshold.
3. What happens if our data isn't good enough?
This is where many projects stall. Your partner should assess data early and tell you plainly if a use case isn't viable yet.
Good answer: "We check that in discovery. If it isn't viable, we'll tell you and suggest what to fix or what to do instead."
4. Who will actually do the work?
Some firms sell with senior people and deliver with juniors or subcontractors. Ask to meet the engineers.
5. How will this integrate with our existing systems?
AI that doesn't connect to your ERP, CRM or document stores creates more manual work, not less. Ask which systems they've integrated with before.
6. How do you handle security and data privacy?
Ask where data is processed, whether it's used to train third-party models, how access is controlled, and which standards they comply with.
7. How do you measure success?
The answer should be a business metric: hours saved, error rate, cost per transaction. "Model accuracy" alone isn't enough.
8. What does a human do when the AI is wrong?
Every AI system makes mistakes. Good partners design review steps, confidence thresholds and escalation paths from day one.
9. What happens after launch?
Models drift, processes change, and users find edge cases. Ask about monitoring, support and how improvements are handled.
10. Do we own what you build?
Clarify ownership of code, models, prompts and data. Avoid lock-in you didn't agree to.
Red flags
- Guaranteed accuracy before seeing your data
- No production case studies, only demos
- A fixed price for a full build with no discovery
- Vague answers about where your data goes
- No plan for what happens after go-live
How Optivus answers these
We start every engagement with discovery and a prototype on real data, and we tell clients when a use case isn't ready. You can see how that played out in practice in our https://optivustechnologies.com/our-work/canary-ai-visitor-engagement-logistics case studies.
CTA: Put us to the test. Book a call and ask us all 10.