7 Criteria for Choosing AI and SaaS Tools: Before the Price List

A new tool always looks attractive. The demo is quick, the price list is simple, the feature list looks long. But choosing a tool is not comparing features — it is checking whether your work's risks and flow can be matched to the tool. Price comes after that.

1. Data boundaries

The first thing to check is "may this tool receive this material". Even with the same AI tool, whether input data is used for training, how long it is retained and whether admin control exists differs by plan and setting.

• Do customer names, contacts and contract details go in
• Can training use and retention be configured
• Is there a permission tier only admins can access
• Is deletion and export possible

A tool you cannot answer these questions for is not adopted first, however good its features are. The related criteria are in handling personal data in AI work.

2. Ownership and export of output

Check who holds the right to use the drafts, documents and code the AI produced. It also matters what format you can export your data in when you leave. A tool that is hard to export creates a large lock-in over time.

3. Connection to the existing workflow

See whether the tool connects to the calendar, documents, chat and storage you already use. Without a connection, people have to bridge with copy and paste, and that repetition erases the effect of automation. Too many connections is not good either. Check that only the necessary connections exist.

4. Adoption, training and maintenance cost

Calculate the hidden costs beyond the subscription.

• Initial setup and admin training time
• Loss of productivity while the team gets used to it
• Work to manage permissions and accounts
• The cost of rolling back if it fails

Free plans often limit features, seats and retention. "Start free" and "our team can keep using it free" are different statements.

5. Verification burden

Evaluate the tool on the assumption that its output cannot be used as is. If the time spent confirming what the tool produced exceeds the time saved, adoption is pointless. Check whether it can show sources, compare with the original and verify numbers.

The method itself is covered in a verification procedure so you do not use AI output as is.

6. Transparency of the price structure

Do not look at price as a number alone.

• Per user or usage-based
• How overage is charged
• Annual commitment and cancellation conditions
• Tax, exchange rate and payment method

A tool whose bill differs greatly from expectations after adoption has stopped being a work tool and become a cost risk. Set a ceiling with whoever owns the budget.

7. Exit plan

Finally, write down "what would I need to stop using this tool in six months". Data migration, a replacement tool, contract termination and user training, thought out in advance, sharpen the criteria for choosing a tool considerably.

A two-week trial before adoption

Test a candidate tool with real work, not with a feature demo.

1. Pick one task.
2. Record the time with the old way and with the new tool.
3. Record errors and the number of reversions.
4. Have one teammate try it and listen to their opinion.
5. After two weeks, decide to continue, hold or stop.

Here "what input produced what error" is more useful data than "it was good".