AI tool evaluation is harder than normal software evaluation because the demo can be impressive even when the workflow is weak. A tool may write a beautiful answer in a demo and still fail on permissions, data handling, pricing, or adoption.
This checklist is designed for practical buyers: founders, operators, managers, and small teams who need enough rigor to avoid obvious mistakes without turning every tool review into a six-month procurement project.
Use it before a demo, during a pilot, and again before you expand access.
Define the job before reviewing the tool
Start by writing the job the tool is supposed to do. Not the category. Not the feature list. The job.
A useful job statement sounds like this: help support reps draft accurate replies to refund questions, help sales reps turn call notes into follow-up emails, help operations classify inbound requests, or help marketers turn product notes into publishable drafts.
If you cannot write the job in one sentence, the evaluation is probably too broad.
Workflow fit questions
- What business problem does this tool solve?
- Who will use it every week?
- What input data does it need?
- What output should it produce?
- Where does the output go?
- Who reviews or approves it?
- What metric will prove the pilot worked?
Security and privacy questions
Security evaluation should start with data type. Public marketing copy, internal meeting notes, customer messages, contracts, source code, payroll data, and medical information all carry different levels of risk.
Ask whether your data is stored, used for training, shared with subprocessors, encrypted in transit and at rest, retained after deletion, and available for export. Ask whether admins can manage users, roles, and access. If the tool integrates with a system of record, check what permissions it requests.
For early pilots, use the least sensitive data that still proves the workflow.
Data controls to verify
- Does the vendor explain whether customer data is used for training?
- Can admins control retention or deletion?
- Can access be limited by role or workspace?
- Does the integration ask for more permissions than the workflow needs?
- Can the team export data if it leaves the tool?
- Is there an audit trail for important actions?
- What happens if a user leaves the company?
Pricing and ROI questions
AI pricing is often difficult because usage units vary. One vendor charges seats, another charges credits, another charges tasks, another charges minutes, and another charges contacts or documents.
Convert pricing into your workflow volume. If the team handles 1,000 tickets a month, estimate cost per ticket. If the tool summarizes meetings, estimate cost per recorded hour or participant. If it writes drafts, estimate cost per finished piece of work.
Also include rollout costs: setup, training, data cleanup, integration work, and manager review time.
Integration questions
Integrations are where many AI tools become either valuable or painful. A tool that works beautifully in isolation may still add manual copying if it cannot connect to the systems your team uses.
Ask what data flows in, what data flows out, whether sync is automatic or manual, how errors are handled, and whether users can review changes before they update a system of record.
For the first pilot, a manual export may be acceptable. For daily use, the workflow needs to fit the places where people already work.
Rollout questions
A rollout plan should be small enough to finish. Choose a pilot group, define sample work, set review rules, and decide what success looks like.
During the pilot, collect examples where the tool helped, where it failed, and where it created extra work. The failures are not only bugs. They are clues about training, prompt templates, permissions, data quality, and whether the workflow is a good fit.
At the end, make a clear decision: expand, adjust, replace, or stop.
Red flags
- The vendor cannot explain data use in plain language.
- The demo depends on perfect inputs your team rarely has.
- Pricing changes sharply once normal usage is estimated.
- The tool requires broad permissions for a narrow task.
- There is no clear human review step for high-risk output.
- The workflow saves time for one person but creates work for another team.
- The team cannot name a metric that would prove value.