How to Evaluate an AI Coworker: A Practical Buyer's Guide
Evaluate the job, evidence, control, and adoption - not the polish of a vendor demo.
TL;DR
- Pick one real recurring job and define success before the trial.
- Use your own messy data, including failure and stop cases.
- Measure review time, reliability, and adoption alongside output quality.
- Verify permissions, approvals, logs, and offboarding before granting broader access.
The short answer
Evaluate an AI coworker on one real recurring job. Score useful completion, grounding, review time, access control, failure handling, and team adoption. A product that produces impressive text but creates more checking work has failed the evaluation.
AI coworkers combine non-deterministic reasoning with real tool access, so feature checklists are insufficient. The evaluation must cover both business value and operating risk.
The six-part scorecard
| Dimension | Question | Evidence |
|---|---|---|
| Job fit | Does it own a real repeated job? | Named trigger, output, owner |
| Grounding | Can every important claim be traced? | Links, citations, source excerpts |
| Reliability | Does it handle ordinary variation? | Representative test set, failure log |
| Control | Can access and actions be bounded? | Permissions, approvals, audit trail |
| Economics | Does the full workflow cost less? | License, usage, setup, review time |
| Adoption | Does the team use the output? | Repeat usage and downstream action |
Start with the job, not the vendor
Write one sentence that names the trigger, the work, the output, and the accountable person. 'Every Monday, compile pipeline movement and risks into a draft for the revenue lead' is testable. 'Help sales with AI' is not.
Use your own messy examples
Build a test set from ordinary work: incomplete notes, conflicting fields, quiet weeks, last-minute changes, and one case where the correct answer is to stop. Run the same set through each product.
Count review as part of the cost
Measure the minutes required to verify, edit, and route each result. A draft created in thirty seconds can still be slower than the old workflow if it takes twenty minutes to inspect.
Test the controls before the happy path
Use the access and governance questions in the NIST AI Risk Management Framework as a baseline: who can authorize the system, what data can it reach, how are incidents recorded, and who can stop it?
How Mio fits this framework
A fair Mio test uses one shared Slack workflow, connected sources with deliberate scopes, and draft-only output. The operator reviews the first runs and measures whether the packet reduces real coordination work without weakening confidence.
Mio lives in Slack, uses the company sources a team connects, and turns recurring coordination into reviewable work. It is designed to surface and draft while people retain judgment over consequential actions. Try Mio in Slack.
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Mio is the Slack-native AI coworker that already knows your company, connects to 3,000+ tools, and turns shared context into work. Just @mio, it's handled.
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