10 Questions Every Team Should Ask Before Picking an AI Tool

Use this checklist during internal evaluations, vendor demos, or trials to avoid surprises later.

What specific problem does this tool solve for our team?

Anchor on a use case, not general capability.
Does it integrate with our current stack (CRM, docs, data sources)?

Check for native connectors or available APIs.
How much customization is available (prompts, models, workflows)?

Closed tools may be fast to use, but rigid.
Can we try it with real data before committing?

Look for sandbox environments or free pilots.
How fast is the onboarding process?

Time-to-value should be measured in hours, not weeks.
What happens to our data (storage, retention, fine-tuning)?

Check if data is stored, used to train models, or exportable.
Is it scalable to more teams or enterprise-wide use?

Ask about multi-seat management, SSO, audit logs.
What support is available (docs, SLAs, success managers)?

Especially critical for regulated or high-usage environments.
How transparent is the pricing and billing model?

Avoid tools with unclear usage caps or “credit creep.”
What’s the roadmap—are they shipping fast and listening to users?

Check changelogs, community forums, and responsiveness.

A checklist:

Question #	Evaluation Question	Response (Single Select)
1	What specific problem does this tool solve for us?	Clear / Vague / Unknown
2	Does it integrate with our current stack?	Yes / Partial / No
3	How customizable is it?	High / Medium / Low
4	Can we test with real data before purchase?	Yes / Limited Trial / No
5	How fast is onboarding?	<1 day / 1–2 days / >1 week
6	How is our data handled?	Secure & Private / Shared / Unclear
7	Is it scalable to more teams?	Yes / With Limits / No
8	What support is available?	24/7 / Business hours / Email only
9	Is pricing transparent and predictable?	Yes / Partly / No
10	Are they shipping fast & listening to users?	Yes / Infrequent updates / Unclear