Six AI Vendors. Zero Trust.

At some point in 2024, every enterprise client I worked with had the same item on their board agenda: AI Strategy.

What followed, in most cases, was a procurement process disguised as a strategy process. Six vendors invited. Six demos with beautiful slides. Six promises of 40% efficiency gains. Zero clarity on what to actually buy.

I've been through this enough times — as an advisor, as an interim technical lead — to have a working framework. Here it is.

Why Most AI Vendor Evaluations Fail

Enterprise AI demos are optimized for one thing: making the technology look effortless in controlled conditions. The vendor knows the demo data. They've tuned the prompts. The failure modes are invisible.

Your data is messier. Your processes have exceptions. Your people have habits that don't show up in the demo script.

The evaluation fails because it compares ideal vendor performance against theoretical client requirements — not real-world implementation against actual operational friction.

Five Questions I Ask Before Anything Else

1. Where does my data go?

Not "is it secure?" — every vendor says yes. Specifically: Is it used to train models? Where is it processed? What happens to it if I cancel? Vagueness here is itself an answer.

2. What does the workflow change actually look like?

Show me not what the AI does — show me what the user does differently on a Tuesday afternoon. Where does it fit in the actual process? What breaks when it's wrong? Clean answers here signal maturity. Vague answers don't.

3. Who owns the edge cases?

Every AI system fails sometimes. The question is: when it fails, what happens? Is there a human override? Is the failure visible or silent? Who is accountable? In regulated industries, this matters more than the accuracy rate.

4. What's the integration surface?

"It connects to everything" usually means "we have a Zapier connector." Ask specifically: REST API? Does it write back to source systems or only read? Is there a sandbox for testing? One hour with a developer beats any number of architecture slides.

5. What does the contract look like in year 2?

The vendor who wants a 3-year enterprise contract for a technology that didn't exist 18 months ago is managing their revenue, not your risk.

Three Categories I Actually See in the Market

After reviewing tools across clients in financial services, manufacturing, and professional services in 2025, the landscape separates roughly into:

Workflow wrappers. Productivity tools that put an LLM interface on top of existing processes — email drafting, meeting summaries, document search. Low switching cost. Low risk. Low transformation potential. Good for quick wins; don't mistake them for strategy.

Data platform plays. These require connecting actual operational data (ERP, CRM, systems of record) and deliver value through analysis and decision support. Higher integration cost. Higher value if data quality is there. Most fail because data quality isn't there.

Process replacement tools. These don't assist a process — they claim to replace it. Document review, compliance checking, parts of underwriting. Highest potential. Highest risk. Require legal, compliance, and change management involvement before procurement, not after.

Most clients want Category 3 value but buy Category 1 tools and wonder why nothing changed.

What I Actually Recommend

Start with the constraint, not the vendor.

Identify one process where the volume is high enough to matter, where AI error is recoverable, where you can measure the outcome clearly, and where a human can override in 30 seconds.

Run a real pilot. Not a demo — a pilot with your data, your people, your edge cases. Three months. Then decide.

The vendor who can't scope a pilot that fits this description doesn't know how their product works in your environment. That's information.

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If you're building an AI vendor shortlist or evaluating a specific tool for enterprise adoption — I offer structured assessment sessions for senior teams.