The AI development industry is now so saturated with vendors that buyers no longer have time to vet everyone. The same capabilities are offered by all firms, and the lists ranking them are almost exclusively written by those same firms. The actual differentiators, the ones that matter, are the ones your vendor’s sales pitch has been designed to avoid telling you. Getting a bad one wrong is more than just an IT procurement mistake.
A failed AI implementation can cost a budget a year, with either no product to show for it, or a product that reached deployment but then quietly degraded in performance over time, until one day the cost of running it was greater than the value it produced.
In this situation, understanding why AI projects don’t work is a good starting point because knowing the failure mode is a good way to filter out what doesn’t matter. The usual assumption is that it’s the technology that makes AI different; this is not the case.
Failure is usually organisational
An MIT study published in 2025 through its NANDA (Narrow AI Deployment Audit) reported that the large bulk of enterprise generative AI (GenAI) pilots had zero impact on profit and loss despite spending billions, collectively, on them. This is a report one can read with a certain degree of skepticism. A number of analysts have pointed out that it overplays the scale of AI failures by equating “no measurable return yet” with “failed.”
Even its harshest critics tend to agree, however, that the study’s central argument about how to build enterprise AI is correct: What made some pilots succeed and others stall was less about the underlying model than the approach. “The failures occurred,” the report states, “because brittle workflows and static tools never adjusted to human adaptation; thus, the technology remained perpetually out of sync with daily operations.”
Gartner’s predictions echo that assessment; before the current GenAI boom it predicted that 30 percent of GenAI use cases would fail at the proof-of-concept stage, because “poor data quality, ineffective risk mitigation, high cost of implementation, and difficulty demonstrating business case.” Again, nothing there is about the model itself. For a purchaser, the questions that sound most technical are often the least decisive.
“A vendor with the ability to design a sophisticated model,” MIT concluded, “but no capacity to deploy it into production, link it to an outcome important to the enterprise, has solved the easy part but ignored the hard part.”
Why the partner matters more than the build
According to the same MIT study, when evaluating the build-versus-buy question, buying or partnering reached deployment 67% of the time compared to about one-third of builds. This finding might be counterintuitive to some who think that control is best maintained internally, yet it is the direct result of the failure analysis. Partners with prior production implementations have navigated the mundane yet impactful challenges that stymie inexperienced developers, including data pipelines, evaluation frameworks, observation platforms and system integration tasks, areas that the in-house team will probably encounter for the first time.
In short, choosing a partner is primarily a decision regarding risk management, rather than a judgment about capability. The partner to engage with is the one with the highest probability of taking you across the finish line that the data shows that most efforts never approach, a detail which an outstanding demonstration does not reveal. So vetting must begin by finding evidence of having reached that line.
What to actually screen for
Reduce the noise to a short list of things that are hard to fake and that predict whether a system survives contact with production. A useful vendor interview turns on five of them.
- Production track record over pilots – Ask for a system that went live and stayed live, and what it took to keep it there. Anyone can walk you through a proof of concept. Far fewer can show a model that has behaved for twelve months under real traffic.
- Data and evaluation discipline – Since weak data readiness is the most common failure cause, a serious partner leads with how it will assess and prepare your data and how it will measure success, well before it talks about architecture. A partner who cannot describe the evaluation cannot tell you whether the result is any good.
- Monitoring and lifecycle ownership – Models degrade after deployment, so a partner who treats launch as the finish line is handing you a liability. Look for continuous monitoring and a defined plan for retraining and incident response.
- Domain and regulatory fit – A partner who knows your sector already understands its constraints. For regulated work, this stops being optional because the team has to know in advance what an auditor will eventually ask for.
- Verifiable references – Thin or unverifiable case studies are the clearest red flag. A long client relationship and a real reference you can call outweigh any award or partner badge on the website.
So, what’s left out of that list? What a company puts front-and-center on its website, the tools and technologies it’s trained on, the frameworks that dominate, the certifications it displays, tell you almost nothing.
You’re looking for experience, not experience with a specific toolset. It doesn’t tell you if the company can run an AI system as an ongoing product as opposed to just shipping some clever gadget.
The jurisdiction question buyers forget
One thing is not on the engineering test: the question that only gets more expensive. For regulated or high-risk applications, where your partner is located legally is part of what you are buying. When you have a system that will deal with biometric information or medical records, you must know you can look back into the live system and obtain the records and data logs an inspector requires, and you must know whose law applies to the personnel who run it.
There is a void if a company cannot answer clearly on data sovereignty or the ability to run the system; no number of fancy features will fill that hole.
It should be a part of the test alongside engineering expertise, not an addendum to the contract to be read post-signing. What endures is not a technology test but an engineering process: a company that treats the system not as a one-time experiment but as an iterative engineering loop, which you’ll see in its documentation, and the proof of its endurance, which is in its case studies.
This decision is as much about judgment as it is about the tech, so take your time, slow the buying process down and make sure you get it right. Choosing a partner is just one piece of the larger puzzle of turning a conceptual idea for an AI system into a reliable product. That’s what SkyBiometry does for you end-to-end: the data, the testing, and then the infrastructure and the API that can be relied on.
Read the full guide to AI product development, or see how we approach applied AI solutions and custom models.
