How to Not Get Beat In a AI Vendor Assessment
The Vendor Assessment Framework They Don't Want You to Know
Every AI vendor demo looks flawless.
The integrations click.
The pre-sales team has an answer for everything.
But here’s the hidden truth: 92% of Fortune 500 firms have adopted AI… and only 23% see financial returns.
That’s the paradox. Billions invested. Millions committed to contracts. And yet three-quarters of enterprises are stuck in the same place: pilots that never scale, projects that collapse, and “production-ready” agents that fail at the first sign of stress.
This isn’t a skills gap.
It’s not even a technology gap.
It’s a vendor assessment gap.
Most teams are still using procurement checklists designed for ERP systems or cloud contracts.
AI is different.
The risks are different.
The failure patterns are different.
And unless you change the way you evaluate vendors, you’ll be one of the 75% who burns through budget with nothing to show for it.
The good news?
A small set of tests expose these weaknesses before the contract is signed. And once you know them, you’ll never walk into a vendor meeting blind again.
Let’s get into it.
The Shared Frustration Every Technology Leader Recognises
You're brilliant at evaluating traditional software.
You understand feature comparisons, integration complexity, and total cost of ownership.
You've successfully implemented dozens of enterprise systems.
But AI is different.
The vendor demonstrates impressive capabilities in controlled environments, yet their system crumbles under real-world conditions. They promise seamless integration but require massive process changes. They showcase impressive accuracy metrics that somehow don't translate to business value.
Meanwhile, your boss is asking why competitors seem to be moving faster with AI, and you're left wondering whether the problem is the vendors you're choosing or your evaluation process.
Here's what I've learned from analysing hundreds of AI vendor assessments: It's your evaluation process.
The Current Nightmare vs. The Desired Clarity
Current State: Walking Blind Into Vendor Demos
You rely on traditional procurement frameworks designed for predictable software. You compare feature lists and pricing models. You evaluate vendors based on their demos and references. You follow standard RFP processes.
But AI vendors know exactly how to game these traditional processes.
They've mastered the art of impressive demos that mask fundamental weaknesses. They provide references from implementations that bear little resemblance to your use case. They promise capabilities they're still developing.
Desired State: Going In Armed With the Right Framework
Instead of hoping vendor demos reveal the truth, you systematically evaluate what actually matters for AI success.
You understand the five critical domains where AI implementations fail. You know the specific questions that expose vendor weaknesses before you sign contracts.
You can distinguish between impressive technology and implementable solutions.
You evaluate long-term partnership potential rather than immediate capabilities. You assess risk across dimensions traditional procurement ignores.
What This Means for You
The barrier isn't lack of AI expertise. Plenty of companies successfully evaluate AI vendors without deep technical knowledge.
The real barrier is using traditional software evaluation methods for fundamentally different technology.
Traditional software is deterministic.
AI is probabilistic.
Traditional software has predictable failure modes.
AI systems degrade in unexpected ways.
Traditional software integration is about APIs and data formats.
AI integration requires process changes, training data curation, and ongoing model maintenance.
It's not that you don't ask questions—it's that vendors know how to answer traditional questions without revealing AI-specific weaknesses.
The Assessment Framework Cheat Code
After working with dozens of organisations that successfully navigated AI vendor selection, I've identified the systematic approach that separates successful implementations from expensive failures.
It's built around five assessment domains that traditional procurement completely misses:
Domain Analysis - Rather than evaluating generic capabilities, you assess specific use case alignment and understand exactly where AI systems break down in real-world scenarios.
Integration Reality - Instead of trusting vendor promises about "seamless integration," you systematically evaluate the true complexity of implementation, including process changes, staff training, and ongoing maintenance requirements.
Risk Exposure - Beyond standard security assessments, you evaluate AI-specific risks: model drift, data poisoning (my personal favorite), bias, and regulatory compliance challenges that traditional software doesn't face.
But here's the crucial insight: The framework only works when you know the specific questions that force vendors to reveal their true capabilities and limitations.
Ask about their support policy for vector database administration, and watch how they respond.
Question their approach to handling edge cases, and observe whether they deflect or engage.
Challenge them on regulatory compliance in your industry, and see if they truly understand the requirements.
The right questions turn vendor meetings from sales pitches into honest assessments of capability and fit.
I don't just consult on AI strategy. I've built the exact framework and question sets I use with clients into a comprehensive assessment available to paid subscribers.
It includes the six-domain evaluation framework that exposes vendor weaknesses traditional procurement misses.
The specific questions that force vendors to reveal their true capabilities rather than polished demo responses.
The scoring methodology that eliminates bias and ensures consistent vendor evaluation.
If you find yourself in the position of assessing and procuring AI technology, then this is the difference between being the leader who successfully navigated AI transformation and the one who learned expensive lessons about vendor selection.
Until the next one,
Chris