The days of trusting a vendor’s demo over due diligence are officially over. Half of enterprise buyers now start their AI vendor research before a single sales rep walks into the room. I’ve watched this shift firsthand in my work-where a mid-sized financial services firm, buried under proposals for AI analytics tools, decided to flip the script. Their CTO’s blunt declaration-“We’re not signing blindly anymore”-marked the turning point. AI vendor research has become the first weapon in their arsenal, not an afterthought. The question isn’t if you’ll research; it’s whether you’ll do it right.
AI vendor research: Where due diligence meets reality
The old playbook-demo, trust, sign-is dead. Teams now treat AI contracts like nuclear weapons deals: extreme vetting required. A healthcare data client spent six months evaluating four vendors before committing. They didn’t wait for pitches; they examined lawsuits, patent portfolios, and even the university affiliations of founders. One vendor’s bias-mitigation flaw surfaced in their research-a detail the sales team had conveniently omitted. That’s how AI vendor research works now: digging until the vendor’s true risks (or red flags) surface.
Key research priorities
Teams need to focus on three non-negotiables before signing anything:
- Production track records over hype. How many real-world deployments? Length matters more than flashy case studies.
- Code transparency. Black-box algorithms are red flags. Can you review the actual model, or are you just paying for marketing?
- Escape clauses. What happens if regulations change? Or if you want to switch vendors? Lock-in fees can kill profits.
Remember: you’re not buying software. You’re buying a risk profile. Ignore that at your peril.
When the smallest player wins
A logistics client faced a dilemma: big vendors promised 92% accuracy with a three-year contract and custom data centers. A two-person startup offered 88% accuracy-but no contracts, no data lock-in, and a team that had written the textbook on warehouse optimization. They chose the startup. Today, they’re saving 12% on labor costs. AI vendor research isn’t about picking the most sophisticated solution; it’s about avoiding the ones that will betray you. Big players have scale, but often lack agility. Small players have agility, but might lack security resources. My rule? If the vendor’s team acts like they’re hiding something, they probably are.
The dark side of AI vendor research: more digging doesn’t always mean better decisions. Clients often drown in white papers and vendor spin. The result? More confusion than clarity. Yet the savviest buyers cut through the noise by asking just three hard questions:
- What’s the worst-case scenario if this fails in production?
- Who owns the data generated by your system-and can you take it back?
- What’s the exit cost if you change vendors in three years?
A vendor that can’t answer these in plain language? Walk away.
Even the best research can’t predict everything. One client’s due diligence missed a critical detail: their chosen vendor’s AI relied on a third-party library with a known vulnerability. Six months into the project, a security audit exposed it. AI vendor research isn’t a one-time event-it’s an ongoing process. The contract signed on Day 1 is just the starting line.
The enterprises that win aren’t the ones who pick the sexiest AI tool. They’re the ones who treat vendor selection like a marriage: cautious, curious, and always planning for the exit. Research is no longer optional. It’s the difference between saving millions-or burying yourself in regret.
