The AI vendor landscape has grown fast in recent years. There are software companies that have added “AI” to their catalog, digital marketing agencies offering “AI solutions,” large consulting firms with AI practices of varying size, and specialist teams with real experience in artificial intelligence projects.
For a company looking for a partner on an AI project, telling these categories apart isn’t easy — and choosing badly has consequences. This article gives you concrete criteria and direct questions to get that evaluation right.
The problem with today’s market
The bar for calling yourself an “AI company” is very low. Any business can add AI to its website without having put a single project into production. That doesn’t mean you should distrust every vendor — it means the label isn’t enough, and your evaluation criteria have to go deeper.
The most common vendor profile that lacks the expertise it promises comes from software development or digital marketing and has added generative AI tools to its offering without ever working on supervised ML, data, production integration, or organizational change management.
The questions that reveal the difference
“How many AI projects have you taken to production, and how many are still in production today?” This is the most important question. Not how many they’ve started, but how many are running in a client’s real systems, delivering measurable value. A vendor who can’t name production projects with real systems at real clients has little experience with the hardest part: making the project work beyond the demo.
“When has an AI project gone differently than expected, and how did you handle it?” This question is very revealing, because AI projects always bring surprises — the data isn’t as good as expected, the model doesn’t converge as planned, users don’t adopt the system as designed. An experienced vendor has stories about how they handled those situations. One without real experience gives generic answers.
“How do you measure success once the system is in production?” If the answer covers only technical metrics (model accuracy, response time) and not business metrics (hours saved, fewer errors, improvement in specific KPIs), the vendor’s perspective is too technical for a project that has to deliver business value.
“Do you have commercial agreements with specific software or tool vendors?” A vendor with reseller agreements for specific tools has an incentive to recommend them whether or not they’re the best fit for the use case. Independence from technology vendors is an important trait in an AI implementation team.
Red flags in the sales pitch
Some phrases in AI vendors’ sales pitches deserve follow-up questions:
“We implement AI in X weeks.” Well-designed AI projects don’t come with guaranteed timelines, because they depend on factors you discover along the way (data quality, integration complexity). A vendor who guarantees very specific timelines before auditing your situation is either oversimplifying the scope or has unrealistic expectations about what they’ll find.
“Our documented ROI is X times.” An AI project’s return depends on the use case, the state of the data, and the size of the company. A generic ROI figure without context tells you nothing about what your company can expect.
“You don’t need to change anything in your systems.” Integrating AI with existing systems is one of the most complex and expensive parts of the work. A vendor who says nothing needs to change may be underestimating the integration effort or proposing a solution cut off from the rest of your infrastructure.
What sets teams with real experience apart
Teams with real AI project experience have recognizable traits. They ask about the data before proposing solutions. They explain what might turn out differently than expected. They don’t promise results that depend on factors they haven’t yet audited. And they have references from completed projects — not just projects they’ve started.
They’re also upfront about AI’s limitations for specific use cases. A team that says “for this particular case, a standard analytics solution may be more efficient than ML” is more credible than one that proposes AI for everything.
Why technology independence matters
When you commission an AI project, the vendor’s independence from the tools it recommends is an important criterion. A team that doesn’t sell tools or hold reseller agreements has incentives aligned with yours: recommending the best tool for the problem, not the one that earns the vendor the most margin.
Frequently asked questions
Is it better to hire a local vendor or one with a wider national presence? Location matters less than you might think: most projects can be done remotely. What matters is the vendor’s experience with cases like yours and in your industry, not where they’re based.
How do I know the vendor will still be around after launch? It’s a fair question. Ask about their post-launch support and maintenance model, how many projects are currently in maintenance, and which team would take yours on. A vendor with many projects in maintenance is a sign that clients keep trusting them once the system is in production.
What documentation should I ask for at the end of the project? Technical documentation of the system (architecture, data flows, model parameters), user documentation (how to operate and maintain the system), documentation of the model retraining process, and baseline performance metrics so you can detect degradation in production.