Let’s be honest: your vendor selection process wasn’t built for AI. It was designed for buying databases, ERP systems, and cloud infrastructure. Now you’re trying to use it to evaluate AI vendors, and it’s failing you.

I’ve watched enterprise procurement teams spend months creating elaborate scorecards, only to select vendors that look great on paper but can’t deliver. The problem isn’t the people. It’s the framework.

The Traditional Checklist Doesn’t Work

Traditional IT vendor selection focuses on the wrong things when it comes to AI. You’re asking about uptime guarantees, data center locations, and ISO certifications. These matter, but they’re table stakes. They won’t tell you if the vendor can actually solve your problem.

Here’s what typically happens: procurement builds a scorecard with 50+ criteria. Most of these are binary checkboxes. Vendor A ticks 47 boxes. Vendor B ticks 45. Vendor A wins. Six months later, Vendor A’s solution doesn’t work in production, and you’re scrambling.

Why? Because AI projects fail for completely different reasons than traditional IT projects. They fail when the training data doesn’t represent real-world scenarios. They fail when the model can’t explain its decisions to your compliance team. They fail when the vendor’s data scientists don’t understand your domain.

None of these risks show up on your traditional scorecard.

What You Should Evaluate Instead

Domain expertise over technical credentials. I don’t care if the vendor has a dozen PhDs if none of them understand your industry. Ask them about similar problems they’ve solved. Push them on the specifics. If they’re talking in generic terms about “AI transformation,” that’s a red flag.

Data understanding over model sophistication. The fanciest algorithm in the world won’t save you if it’s trained on garbage data. Ask how they’ll assess your data quality. What happens if your data has gaps? How will they handle bias? If they say your data is “probably fine,” run.

Explainability over accuracy. An 85% accurate model that your team can interpret and trust will outperform a 95% accurate black box every time. This is especially true in regulated industries. Ask vendors how they’ll help your stakeholders understand what the model is doing.

Iteration capability over fixed-price proposals. AI projects are inherently experimental. You won’t know what works until you try it. Vendors who promise everything upfront in a fixed-price contract either don’t understand AI or they’re planning to disappoint you. Look for partners who structure work in phases with clear decision points.

The Questions Your RFP Should Ask

Ditch the checkbox scorecard. Replace it with these questions:

1. Show us three projects similar to ours. What went wrong, and how did you fix it?
2. How will you evaluate if our data is sufficient before we commit to building anything?
3. Walk us through your process for handling model failures in production.
4. How do you ensure your models remain fair and unbiased over time?
5. What does ongoing support look like after deployment?

These questions force vendors to demonstrate actual experience rather than just claim capabilities. The answers will tell you more than any compliance certification.

Building Internal Capability

Here’s the uncomfortable truth: if your team can’t evaluate the answers to those questions, you’re not ready to buy AI. You need to build internal capability first.

This doesn’t mean you need to hire a team of data scientists. It means you need someone who understands AI well enough to ask intelligent questions and spot nonsense. Many enterprises work with the Team400 team or similar advisory firms to build this capability while they’re evaluating vendors.

According to McKinsey research, organizations with dedicated AI leadership are twice as likely to successfully deploy AI at scale. That leadership doesn’t have to be permanent staff, but it needs to exist.

The Procurement Redesign

Redesign your process around these phases:

Phase 1: Problem validation. Before you talk to any vendors, confirm that AI is actually the right solution. Sometimes it isn’t. Sometimes you just need better data infrastructure or process redesign.

Phase 2: Proof of concept. Bring 2-3 vendors in for small, paid POCs. Set clear success criteria upfront. This should take 4-8 weeks, not 6 months.

Phase 3: Production pilot. Take the winner from Phase 2 and deploy to a limited real-world scenario. Watch it fail. (It will.) See how the vendor responds.

Phase 4: Scale decision. Only now do you commit to enterprise-wide deployment. At this point, you actually know what you’re buying.

This process takes longer than traditional vendor selection. But it dramatically reduces your risk of expensive failures.

Don’t Rush This

I’ve seen too many enterprises rush AI vendor selection because executives are impatient or competitors are moving fast. As Gartner reports, 55% of organizations are now in pilot or production with generative AI, but many are struggling with governance and ROI measurement.

The pressure to move quickly is real. But selecting the wrong vendor quickly just means you fail faster. Take the time to redesign your process. Ask the right questions. Build internal capability.

Your traditional procurement framework served you well for decades. It’s just not built for this. And that’s okay. Neither was anyone else’s. The winners will be the organizations that recognize this and adapt.