Plenty of AI projects die in the pilot phase. And it's rarely the technology's fault. Usually someone at the top picked the wrong vendor, or never vetted them properly in the first place. 

So before you sign anything, skip the slick deck and the polished demo. You want proof of how the thing is built, what it costs, and how it's secured. Here are the ten questions to ask. 

What to Ask Before Hiring an Artificial Intelligence Solution Provider 

At this stage, you are not evaluating a finished product. You are evaluating what the vendor can build for your business. A demo can show how similar systems work, but it cannot prove that the same approach will work with your data, workflows, security requirements, or scale. Use the pre-sales conversation to understand how the vendor would approach your use case, what they would test, what they would build, and how they would measure success before committing to development. 

Technical depth and data 

How would you test the proposed solution against fifty of our real historical cases, including the ones that went wrong?  

A polished demo can show what the proposed system might do. It doesn't tell you how it will perform on your data. Ask the vendor to explain how they would test fifty representative cases from your archive, including edge cases and known failures, once appropriate data-sharing and security agreements are in place. If they can't explain what they would measure or how they would handle messy data, that's a warning sign. 

Where does your data come from, and how fresh is it? 

Bad input gives bad output. Ask where the training data originated, whether it was collected with proper consent, and how often it's refreshed. A vendor who can show clear data lineage is protecting you from copyright trouble, data poisoning, and regulatory headaches. One who can't is leaving all of that risk with you. 

What stops the pipeline itself from failing? 

Here's something people miss: enterprise assistants usually break because of the plumbing around the model, not the model itself. Chopping documents into fixed-length chunks can wreck business logic. Vocabulary mismatches bury the passage that matters. Unchecked data can poison the context across a long conversation. Good teams handle this with hybrid search, reranking, smart context placement (models tend to lose things buried in the middle), and verification checkpoints. 

Cost, workflow, and transparency 

What happens when the model isn't sure? 

Every AI system will encounter inputs it doesn't handle reliably. The dangerous ones answer anyway, confidently and wrongly. Ask about confidence scores, escalation paths, and human review queues. Ask how human oversight would work at launch, and how that oversight could change as the system is tested and tuned. When the evidence is weak, the system should say so or flag the item for review. 

What will you actually build beyond the underlying model or API?  

Plenty of vendors build solutions around third-party models. The question is what's actually being engineered for your business: evaluation benchmarks, domain integrations, orchestration pipelines, security controls, monitoring, and workflow automation. Ask the vendor to separate what comes from the underlying model from what they will design, build, and maintain. 

What does a transaction cost at full scale? 

Pilot costs don't predict production costs. Retrieval systems in particular can get expensive fast once more departments pile on. Get the whole picture in writing: inference fees, the vendor's margin, hosting, and the cost of human review. Then ask what happens to the bill if daily queries double or triple. 

Will the proposed solution finish the job, or just generate a draft? 

A block of summary text still leaves your staff copying, pasting, reformatting, and forwarding. Ask the vendor to map out how the proposed solution would handle the full workflow inside the software you already use, including feedback, approvals, and database updates. 

Governance, ownership, and the long game 

Which governance and compliance frameworks do you actually follow? 

If you're rolling AI out across business units, you need real risk controls, which means you need AI governance framework. Look for alignment with ISO/IEC 42001, the NIST AI Risk Management Framework, and the MITRE ATLAS threat matrix. In practice, that should look like this: 

What are the terms on data retention, export, and ownership? 

Make sure the contract says what the salesperson said. Ask directly whether your business data gets used to train models for anyone else. You should own your inputs, outputs, fine-tuned parameters, and vector indexes outright. You should also be able to export everything, in a usable format, when the contract ends. 

How do you track drift and updates over time? 

When the underlying model provider changes something, outputs can shift. Ask how the proposed system would version prompts and models, run regression tests, and log changes after deployment. Also ask how feedback from daily use would be captured and incorporated over time. 

Quick comparison 

The bottom line 

Picking an AI partner takes real digging, on both the technical and financial sides. Ask these ten questions and you'll avoid most of the stalled pilots and surprise bills. The partners worth hiring are the ones who welcome the scrutiny, show you their security, and put pricing on the table. Want to pressure-test your own automation plans? Get in touch with our engineering team and we'll walk through them with you.