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Evaluating AI email tools: a buyer's checklist

A practical checklist for evaluating AI email tools: what runs without approval, data use, admin controls, accuracy on your mail, exports and pricing clarity.

Koltrix Team5 min read
Clipboard, pencil and pen on a wooden surface
Photo by Kelly Sikkema on Unsplash
On this page(10 sections)
  1. 1. What the AI can do without asking
  2. 2. Data use and privacy
  3. 3. Admin controls
  4. 4. Permissions model
  5. 5. Accuracy on your own mail
  6. 6. Exportability and lock-in
  7. 7. Pricing transparency
  8. Running a two-week evaluation
  9. A scoring sheet you can copy
  10. Bottom line

Every AI email product looks great in a demo, because demo inboxes are tidy and the vendor picked the examples. The useful question is how the tool behaves with your mail, your team and your risk tolerance, and you can only answer that with a structured test.

Use this checklist when you're comparing tools. It's grouped into seven areas, followed by a two-week evaluation plan you can run with a small group.

1. What the AI can do without asking

This is the most important section, so start here. List every action the tool can take and whether a human approves it first.

  • Can it send an email without a person clicking send? Under what settings?
  • Can it forward mail or add recipients on its own?
  • Can it delete or permanently remove messages?
  • Can it unsubscribe you from lists or reply to senders automatically?
  • Are risky actions off by default, or do you have to find and disable them?
  • Is there an undo for actions it takes (moving, labeling, archiving)?
  • If it can draft, where do drafts go, and can someone send a draft by accident?

A reasonable baseline: sorting, labeling and drafting can be automatic because they're reversible. Sending, forwarding and deleting should require a human. If a tool blurs that line, find out exactly how, in writing.

2. Data use and privacy

Ask these directly and get answers in the contract or documentation, not only from a sales call.

  • Is your email content used to train models, the vendor's or anyone else's? Is that opt-in or opt-out?
  • Which sub-processors (such as AI model providers) receive your mail, and under what terms?
  • How long are prompts and outputs retained, and can you request deletion?
  • Where is data processed and stored?
  • What happens to your data when you cancel?
  • Does the vendor publish a security page, and can they share any independent assessments?

You don't have to be a lawyer to ask these. You do need someone who'll read the answers, and if your company has legal or compliance obligations, loop in whoever owns them.

3. Admin controls

Small teams often skip this section and regret it once the team grows.

  • Can an admin turn AI features off for the whole workspace, or for specific mailboxes?
  • Can you exclude sensitive mailboxes (finance, HR, legal requests) from AI processing?
  • Is there two-factor authentication for every user, and can admins require it?
  • When a person is removed from the workspace, do any AI connections they authorized stop working immediately?
  • Is there an activity or audit log showing what the AI did?

4. Permissions model

If you use shared mailboxes, the AI must respect who can see what.

  • Does the AI only see the mailboxes the current user has access to?
  • If you connect an outside assistant (for example through an MCP connector), is it limited to the same permissions as the user who connected it?
  • Can you grant read-only access to an assistant, or is it all-or-nothing?
  • Are AI-drafted replies attributed to the person who sends them?

5. Accuracy on your own mail

This is where demos and reality diverge most. Test with real traffic, not samples the vendor provides.

  • Sorting accuracy: take a week of incoming mail and note where the tool filed each message versus where you would have.
  • Costly errors: count how often important mail (customers, invoices, security alerts) ends up somewhere low-priority. One of these matters more than many minor misfiles.
  • Summary fidelity: for ten long threads, compare the summary to the original. Are numbers, dates and decisions right?
  • Draft quality: for twenty replies, note whether each draft was usable as-is, needed minor edits, or needed a rewrite.
  • Invented facts: did any draft promise features, refunds or timelines you don't offer?
  • Correction: when it gets something wrong, how easy is it to fix and prevent next time?

6. Exportability and lock-in

  • Can you export all your mail in a standard format?
  • Do your labels, rules and templates export too, or only the raw messages?
  • If you use the vendor's custom domain setup, can you move your domain elsewhere without downtime?
  • Are there APIs if you want to integrate or migrate later?

7. Pricing transparency

  • Is the price published, or only available after a call?
  • Are AI features included or metered separately (per message, per draft, per seat)?
  • What happens at usage limits: hard stop, overage charges, or degraded features?
  • Is there a trial long enough to run the evaluation below, and does it require a card?

Running a two-week evaluation

A checklist is only useful if you actually test. Here's a plan that fits around normal work.

Before you start

  • Pick two or three people who handle real email volume, not just the person who found the tool.
  • Choose one mailbox to test with, ideally one with mixed traffic like hello@ or support@.
  • Agree on what "good" looks like in advance, such as "no customer email filed as low priority" and "most drafts need minor edits or none."

Week one: observe

  • Turn on sorting and summaries; leave drafting off or read-only.
  • Each tester keeps a simple log of misfiles and summary errors.
  • Complete sections 1–4 and 6–7 of the checklist from documentation and vendor answers.

Week two: use

  • Turn on drafting for one or two categories of mail, such as how-to questions.
  • Log each draft as usable, minor edits or rewrite, and note anything invented.
  • Try at least one correction workflow: fix a misfile and add a rule.

Decision meeting

Score each section red, yellow or green. Red on section 1 (unapproved risky actions) or section 2 (unacceptable data use) should rule a tool out regardless of how good the drafts are. Yellow items become questions for the vendor or settings to change before rollout.

A scoring sheet you can copy

Tool: ____________   Testers: ____________   Dates: ________

1. Actions without approval     [R/Y/G]  notes:
2. Data use and privacy         [R/Y/G]  notes:
3. Admin controls               [R/Y/G]  notes:
4. Permissions model            [R/Y/G]  notes:
5. Accuracy on our mail         [R/Y/G]  costly errors: __  drafts usable: __/20
6. Export and lock-in           [R/Y/G]  notes:
7. Pricing transparency         [R/Y/G]  notes:

Dealbreakers found: ________
Decision: adopt / adopt with changes / reject

Bottom line

  • Start with what the AI can do without a human; sending, forwarding and deleting should need approval.
  • Get data-use answers in writing, and know which sub-processors see your mail.
  • Test accuracy on your own traffic and weigh costly errors far above minor misfiles.
  • Check admin controls, permissions and export before you depend on the tool.
  • Run a short, structured trial with real users and decide against criteria you set beforehand.

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