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A manufacturing company spent eight months evaluating AI vendors for quality control. They sat through demos, called references, and compared pricing. The vendor they chose had impressive case studies and a polished sales team. The AI system they deployed could detect defects with 95% accuracy, far better than manual inspection.
Six months after go-live, less than 10% of quality issues were being routed through the system.
The technology worked but the implementation failed.
The vendor had sold them the capability without understanding their actual environment: how inspectors worked, what their workflows looked like, and why the existing processes existed. The AI added steps instead of removing them and did not explain its decisions. It arrived in a culture that valued human expertise, which the system seemed designed to undermine.
This is the vendor evaluation problem in a nutshell. The demos look great, the references check out, but none of the pre-work predicted whether the system would actually work.
Most companies evaluate AI vendors the same way they evaluate any enterprise software: demos, references, feature comparisons, and pricing. These criteria made sense when you were buying a CRM or an ERP system; mature categories with established expectations and predictable outcomes.
AI is different.
The gap between what a system can do in a controlled demo and what it will do in your environment is enormous. According to MIT's 2025 research, only 5% of custom enterprise AI tools reach production. Gartner puts the failure rate at 85%, and an S&P study found that companies abandoning AI initiatives before production surged from 17% to 42% in a single year.
These numbers suggest a systematic problem with how companies select AI partners, not necessarily with the AI technology itself. They're evaluating confidence when they should be evaluating competence.
Confidence is easy to manufacture. A good sales team with a polished deck and a cherry-picked demo. Competence is harder to fake. But detecting it requires knowing what to ask.
Start with the red flags. A vendor who never pushes back is optimizing for the sale. Every use case fits their solution, every timeline seems achievable, and every integration is "straightforward."
Real expertise includes knowing limitations. A competent vendor will tell you when their solution is wrong for your problem, when your data isn't ready, and when your timeline is unrealistic. If you've been through three meetings and haven't heard a single caveat, you're talking to a sales team, not a delivery team.
Watch the gap between demo and details. Demos are theater and designed to show the best possible outcome under ideal conditions. The question is what happens when conditions aren't perfect, which is always. Ask how the system handles edge cases. Ask what happens when your data is messy, incomplete, or formatted differently than expected. If the answers involve a lot of "we'll customize that" or "our team will work with yours to figure that out," you're looking at a gap between what they've sold and what they've built.
Be skeptical of reference lists filled with Fortune 500 logos. Large enterprises have dedicated AI teams, massive budgets, and the ability to absorb failed experiments. Their success tells you almost nothing about whether that vendor can deliver for a company of your size. Ask for references from organizations that resemble yours: similar revenue, similar team structure, similar technical maturity. If they can't produce them, that's information.
Ask about failures directly. Every vendor who has done real work has stories about projects that didn't go as planned: implementation challenges, adoption problems, and technical limitations discovered mid-project.
Ask questions like “Tell me about a project that struggled. What went wrong?" If they pivot immediately to success stories, they're either too inexperienced to have failures or too polished to admit them. Neither is a good sign.
Pay attention to how they talk about integration. AI systems don't exist in isolation. They connect to your data infrastructure, your workflows, and your existing tools. The integration work is often more complex than the AI itself. Yet many vendor conversations focus almost entirely on what the AI can do, with integration treated as a post-signature problem. By the time you discover the complexity, you've already committed.
The best vendor conversations feel less like sales pitches and more like consultations. The vendor is trying to understand your problem deeply enough to know whether they can actually solve it. A few questions help surface that dynamic.
Ask a few more questions:
"When is your solution the wrong fit?" A vendor who can articulate their limitations understands their product. A vendor who claims universal applicability is either lying or hasn't done enough implementations to know where they fail.
"What would need to be true about our data for this to work?" AI systems have data requirements: volume, quality, format, and accessibility. A vendor who can specify those requirements upfront has thought through the real-world constraints. A vendor who waves this off hasn't.
"If we don't have X, Y, or Z in place, should we wait?" This is the honesty test. A vendor who occasionally disqualifies a prospect or advises them to wait demonstrates that they care more about successful outcomes than closed deals.
The Pre-Signature Checklist
Before you sign with any AI vendor, verify the following:
Business Alignment
The vendor understands your specific business problem, not just the category.
They can articulate success in terms you care about: cost, time, accuracy, and adoption.
They've asked more questions than they've made claims.
Technical Credibility
They can explain how their system handles edge cases and failures.
They've specified data requirements clearly and early.
They've addressed integration with your existing systems in detail.
Implementation Reality
They've provided references from companies similar to yours.
They can describe a failed project and what they learned from it.
Their timeline accounts for integration, testing, training, and adoption; not just deployment.
Partnership Indicators
They've told you at least one thing you didn't want to hear.
They've recommended waiting or scoping down at least once.
Post-implementation support is defined and resourced.
MIT's research found that purchasing AI tools from specialized vendors succeeds about 67% of the time, twice the success rate of internal builds. But that statistic assumes you've selected the right vendor for the right problem. The selection process matters as much as the technology.
The best vendors will occasionally tell you no. They'll point out gaps in your readiness. They'll recommend a smaller scope or a longer timeline. That kind of pushback feels uncomfortable in the moment. It's also the clearest signal that you're talking to someone who understands what it actually takes to make AI work.
Confidence closes deals.
Competence delivers results.
Learn to tell the difference before you sign.
Choosing the wrong AI vendor is expensive. I help companies avoid that mistake. If you're evaluating options, reach out: ericbrown.com.
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