How to Vet an AI Product Development Partner

Traditional software projects are predictable: you plan features, estimate timelines, and deliver. AI projects work differently.

The failure numbers make the point. RAND's 2024 study found more than 80% of AI projects fail to deliver their intended value, about twice the failure rate of similar IT projects (RAND, 2024).

Projects rarely fail because of the model itself. Most failures come from unclear problem definitions, poor data readiness, or issues moving to production.

This shifts what you should look for. A partner who writes good code but cannot define success clearly may still deliver a failed project. The questions below focus on decision-making and process, not just technical skills.

If your brief is unclear, you will get unclear proposals. If you cannot describe what you want in one sentence, no partner can be held accountable. Address this before reaching out to vendors.

Write down four things: the business problem, how you will measure success, whether you want a full build or just extra help, and who will own the code and models. These answers will help you narrow your options faster than any sales call.

Ownership is one of the most important, but least discussed, topics. Decide early if you are buying a finished product or just hiring help. This choice affects price, ownership rights, and how you end the partnership.

If you need help framing the outcome first, that is a job for AI and ML strategy consulting before any code is written.

Simply listing tools like LangGraph, CrewAI, or a vector database does not provide much insight. You need proof that they know when these tools are not the right fit.

Ask how they check model quality. A partner without proper testing is guessing. You will not know if results get worse until a customer complains.

Then ask them to explain a past project in which the first attempt failed and what they did next. This shows whether they think through choices or just follow a set plan.

Ask directly about their experience running live systems. Building a demo is easy, but managing a real system with real users and messy data is where most teams struggle.

A partner who has delivered generative AI in live products can list common failure modes from experience, not just from reading about them.

How fast they deliver a first working version tells you more than a polished presentation. Ask when you will see something running, not just slides, but testable code.

Getting something in weeks is a good sign. Waiting months before anything works is a warning.

Know who controls the plan. Some partners expect you to give a full plan and then stay silent. Others share control and question weak requirements.

In AI projects, where the best approach is often unclear at the start, you need a partner who is willing to question the plan.

Review the team structure. Find out who will write the code and whether they will stay for the entire project. If you are adding to your own team instead of buying a full product, make sure you can hire AI developers who work well with your current engineers.

AI systems use data, so security matters from the start. Ask how the partner handles your data during training and testing, where it is stored, and whether any is sent to external model providers. Get their answer in writing.

Compliance requirements depend on your industry. For example, fintech and healthtech projects have different rules. A partner with experience in your field should be able to name the relevant standards.

Think SOC 2 for data handling or HIPAA-aware practices for patient data.

A good partner will bring up these issues without needing to be prompted.

Make sure IP ownership is settled in the contract, not just discussed at the kickoff. The agreement should clearly state that the code, fine-tuned models, and training artefacts belong to you. If ownership terms are vague, consider it a red flag.

Good partners are upfront about what drives costs. Project size, data quality, integration complexity, and ongoing support all influence the price. If a partner offers a fixed price before reviewing your data, they have not done their homework.

Expect price ranges, not exact numbers, at the start. Custom AI projects can vary widely in cost.

The honest answer to "what will this cost" is that it depends on your needs. Avoid hourly or generic pricing that treats AI work like call centre staffing.

Ask what happens if the project changes, because it will. A clear process for changes protects both sides. If pricing hides assumptions that appear mid-project, you take that risk.

Many buyers focus on the initial build and overlook the product’s ongoing maintenance. This is often the most costly mistake. Shipping an AI product is not the same as keeping it running well.

The data proves it. MIT's Project NANDA found that 95% of enterprise generative AI pilots showed no measurable return as of 2025, and Gartner expected 30% of generative AI projects to be abandoned after proof of concept by the end of that year. The gap is almost always the move from pilot to production.

So, review the post-launch plan early. Ask how the partner monitors quality once real users are involved.

Then ask how they detect changes in the model, handle retraining, and manage the product in month six, not just in the first week.

A partner who can answer these questions has experience with AI products beyond the demo stage. If they cannot, they are likely just selling a prototype.

Use the sections above to create a weighted comparison. Score each partner from 1 to 5 on the most important criteria, then adjust the scores based on risk.

• Problem and evaluation rigour: Can they define success and measure it? (Highest weight.)
• Production experience: Have they run AI in live products, not just demos?
• Delivery velocity: How fast is the first working increment?
• Security and IP terms: Are data handling and ownership clear and in writing?
• Commercial clarity: Is pricing honest about what drives cost?
• Lifecycle support: Do they have a concrete plan for monitoring, drift mitigation, and retraining?

A partner who is fast but weak on evaluation and lifecycle support is likely to lead to a failed project. Focus your scoring on the failure modes identified by RAND and MIT, and your scorecard will help you choose a partner who can deliver lasting results.