AI Vendor Evaluation Framework
A vendor-agnostic set of questions that helps buyers avoid being sold—and pause when answers are vague.
Interactive worksheet
Nothing is sent anywhere. Your inputs stay in your browser unless you choose to copy or download a summary.
This is a worksheet, not a scorecard. Mark answers as clear, unclear, or red flag—then write down what you’d need to feel safe.
What happens when accuracy drops—how will we know, and what do we do?
Can we monitor outcomes without reading every transcript or every message?
What changes when the underlying model changes (behavior, costs, latency, retention)?
How do we audit outputs and show why something happened (in plain English)?
Where do humans override—and how is that captured?
Can we extract our data cleanly (inputs, outputs, configurations) without a rebuild?
What data do you store, and where is it processed?
What assumptions does this tool make about our process and roles?
What work will still be manual after implementation (and who owns it)?
If answers are vague, treat that as a signal—not a gap to “assume away.” Pause and ask for specifics you can operationalize.
- None flagged yet.
- None flagged yet.
If a vendor can’t answer these clearly, pause.
Preview summary (for copy/paste)
AI Vendor Evaluation — Summary Guidance: if a vendor can’t answer these clearly, pause. Avoid buying based on a demo that doesn’t match your real data.
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No CTA here on purpose. This tool is meant to help you think, even if you never hire anyone.
What this helps you decide
- Evaluate vendors on operational reality, not demos.
- Clarify what happens when accuracy drops or behavior changes.
- Prevent lock-in surprises (data, workflows, governance).
When to use it
- You’re comparing chatbot/automation platforms.
- You want to understand ownership, auditing, and exit costs.
- You need a framework for procurement conversations.
The framework
Reliability and drift
- What happens when accuracy drops—how will we know, and what do we do?
- Can we monitor outcomes without reading every transcript?
- What changes when the underlying model changes?
Auditability and accountability
- How do you audit outputs and show why something happened?
- What logs are available, and for how long?
- Where do humans override, and how is that captured?
Data and portability
- Can we extract our data cleanly (inputs, outputs, configurations)?
- What data do you store, and where is it processed?
- Can we move to another system without redoing everything?
Workflow assumptions
- What assumptions does this tool make about our process and roles?
- Does it require us to change how we work, or can it adapt to our workflow?
- What work will still be manual after implementation?
Common mistakes
- Buying based on a demo that doesn’t match your real data.
- Ignoring exit strategy until after year one.
- Assuming “AI features” automatically equal operational value.
What this does NOT answer
- Which vendor is best (that depends on your workflow and constraints).
- Whether you should automate a workflow that isn’t stable yet.