The AI Vendor Evaluation Framework I Use After 20 Years in Banking IT

Every AI vendor demo looks incredible. The slides are polished, the pilot results are cherry-picked, and the sales engineer always knows exactly which button to click. Then you sign the contract, hand it to your engineering team, and spend 14 months discovering what the demo didn't show you. I've evaluated over 40 AI vendors across three major banks. Roughly 60% of those evaluations ended with a 'no.' Not because the technology was bad, but because the vendor couldn't survive contact with enterprise reality.

Why Most Vendor Evaluations Fail Before They Start

The typical vendor evaluation at a large bank looks like this: someone in the C-suite sees a demo at a conference, gets excited, and tells a director to 'look into it.' The director schedules three vendor calls in a week. Each vendor sends a different deck with different metrics. The team tries to compare apples to submarines. Six weeks later, someone picks the vendor whose sales rep was most persistent.

This is how you end up with a $2.3M platform contract that your security team rejects in month four. I watched this exact scenario play out in 2019 at a bank I won't name. The vendor had great NLP capabilities, but their data residency model violated three separate regulatory requirements. Nobody asked about data residency during evaluation because nobody had a framework that forced the question.

The fix isn't more meetings. The fix is a scoring model that makes every vendor answer the same hard questions before you ever see a demo. I've refined mine over a decade. It has four categories, each weighted differently depending on your industry. For regulated financial services, the weights skew heavily toward integration and compliance. For a tech startup, they'd skew toward speed and flexibility.

The 4-Category Vendor Scoring Framework

Every AI vendor gets scored across four categories: Technical Fit (25%), Integration Reality (30%), Compliance and Security (25%), and Commercial Sustainability (20%). Those percentages are what I use in banking. Adjust them for your industry, but don't skip any category. The moment you drop one, that's exactly where the vendor will burn you.

Technical Fit is the category everyone focuses on, which is why I only give it 25%. Yes, the model needs to be accurate. Yes, the platform needs to handle your data volume. But most enterprise AI vendors clear the technical bar. What separates them is everything else. For Technical Fit, I score on five sub-criteria: model accuracy on YOUR data (not their benchmark data), latency under production load, support for your existing data formats, API maturity, and documentation quality. I require vendors to run a proof of concept on a sanitized version of our actual data. If they refuse or stall, that tells you everything.

Integration Reality is where vendors fall apart, which is why it gets the highest weight at 30%. This category answers one question: how much work does MY team have to do after we sign? I score on: number of pre-built connectors for our existing stack, estimated integration timeline (then I double it), required changes to our current architecture, vendor professional services availability and cost, and the size of their implementation engineering team. A vendor once told me integration would take 6 weeks. I asked how many enterprise banking clients had completed integration in under 12 weeks. The answer was zero. We passed.

Compliance and Security is non-negotiable in banking and increasingly important everywhere else. I score on: SOC 2 Type II certification (not 'in progress,' completed), data residency controls, model explainability for regulatory audits, role-based access controls, audit logging completeness, and their response to your security questionnaire turnaround time. If a vendor takes more than 10 business days to return a completed security questionnaire, their compliance operation is understaffed. That's a leading indicator of problems you'll hit post-contract.

Commercial Sustainability evaluates whether this vendor will exist in three years and whether the deal makes financial sense. I score on: company funding runway or profitability, customer concentration (if one client is 40% of revenue, you're exposed), pricing model transparency, contract flexibility for scaling up or down, and executive sponsor accessibility. In 2023, I watched two banks scramble to replace an AI vendor that ran out of funding 18 months into a 3-year contract. Due diligence on commercial viability would have flagged both.

The Reference Call Script That Vendors Hate

Every vendor gives you three reference clients. Those references are hand-picked, coached, and often have financial incentives to say nice things. So I use references differently. I ask the vendor for five references, not three, and I specify that at least two must be in regulated industries with over 10,000 employees. Then I ask a very specific set of questions that the vendor can't pre-coach answers for.

Here's the script I use. Question one: 'What was the gap between the timeline you were promised and the timeline you actually experienced?' This question has never once gotten a response of 'it was on time.' The average answer across 30+ reference calls I've made: 2.4x the original estimate. Question two: 'If you could go back to contract negotiation, what clause would you add or change?' This surfaces hidden costs and gotchas that only appear post-signature. Question three: 'How many support tickets have you filed in the last 90 days, and what's the average resolution time?' Vendors love quoting SLAs. Clients tell you what actually happens.

Question four: 'Has the vendor ever changed pricing, API limits, or feature availability after you signed?' This is increasingly common with AI vendors burning cash. They sign you at one rate, then restructure pricing 12 months in. Question five, and this is the one that makes vendors uncomfortable: 'Would you choose this vendor again today, knowing what you know now?' I've gotten two honest 'no' answers from vendor-provided references. Both times, we walked away from the deal. Both times, other banks that signed with that vendor came to regret it within a year.

Running a Proof of Concept That Actually Proves Something

A POC is not a demo with your logo on it. A POC should be designed to stress the three areas where vendors most commonly underdeliver: data integration, performance at scale, and edge cases. I run every POC with what I call the 3-3-3 rule: 3 real data sources, 3 weeks maximum, and 3 predefined success metrics agreed on before the POC starts.

The 3 real data sources requirement eliminates the vendors who only work well with clean, structured, pre-formatted data. Enterprise data is messy. If the vendor can't handle your actual data feeds, including the ones with missing fields and inconsistent formatting, you'll spend six figures on data engineering before the AI even turns on. I had a vendor ace every benchmark with test data, then choke on our production feeds because 23% of records had a field they didn't expect. That's not an edge case. That's Tuesday.

The 3-week cap forces urgency and reveals the vendor's actual implementation speed. If they can't show meaningful results in three weeks with dedicated support, imagine what happens when you're one of 50 enterprise clients competing for their engineering team's attention. And the 3 predefined success metrics prevent the post-POC goalpost moving that every vendor attempts. 'Well, it didn't hit the accuracy target, but look at these other metrics.' No. We agreed on three numbers. Hit them or don't.

One more thing I always include in the POC agreement: a kill clause. If the POC misses all three metrics, the vendor covers 100% of the POC cost. If they hit two of three, we split it. If they hit all three, we cover it. This single clause has caused four vendors to withdraw from consideration. Every one of those withdrawals saved us from a bad deal. A vendor that won't put skin in the game during the POC will absolutely not go above and beyond during implementation.

Actionable Takeaway