Answer these before moving forward

• What specific job does this tool do? Write it in one sentence. If you cannot, the use case is not clear enough.
• Is this a repeated workflow or a one-time experiment? Tools earn their subscription cost through repetition, not novelty.
• Does this replace something you currently do manually, or does it add a new step? The strongest tools compress existing workflows, not expand them.
• How often will you actually use it in a typical week? Less than twice a week rarely justifies a paid subscription.
• Does it fit into an existing workflow, or does it require building a new one? New workflows need adoption effort. Factor that in honestly.

• What does the tool need as input? Text, files, API access, connected apps, or something else?
• Do you already have that input ready in the right format? Reformatting inputs before every use adds friction and reduces adoption.
• What does the tool produce? Make sure the output is something you can use directly, not something that needs significant cleanup before it is useful.
• Can the output be exported or shared in the format your workflow needs? A tool that produces great outputs you cannot easily use is still a friction point.

Data safety is the most commonly skipped section of tool evaluation — and the one with the highest risk. Before connecting any AI tool to real data or workflows, check the following:

• Does the tool process sensitive data? Customer information, employee data, financial records, or proprietary documents all have privacy implications.
• Where does the data go? Is it sent to an external server? Used to train models? Stored in a third-party system?
• Does the tool comply with relevant regulations? GDPR, HIPAA, SOC 2, and other frameworks may apply depending on your industry and location.
• Who in your organization has reviewed the privacy policy? “I assume it is fine” is not a review.
• Does connecting this tool require giving it access to other apps? Understand exactly what permissions you are granting and why.

• Who reviews the output before it is used? AI tools produce outputs that need a human check before they go into the world. Define who does that review.
• Is there a clear owner for this tool? Tools without an owner drift. Someone needs to be responsible for whether it is working and worth keeping.
• How will you know if the tool is not performing well? Define what “not working” looks like before you start, not after months of mediocre results.

• Does this tool connect to the apps you actually use? Check the native integrations, not just the ones on the marketing page. Test them before committing.
• Does it overlap with a tool you already pay for? If you already use an AI assistant that covers 80% of this use case, the new tool needs a very specific reason to exist alongside it.
• What is the total cost including add-ons, seats, and API usage? Starting prices are rarely what teams actually pay after adding users and features.
• Is there a free trial long enough to evaluate it properly? Two or three days is usually not enough. You need to use a tool across a real work cycle before you can assess it fairly.
• What is the cost per hour of time saved? If a $20/month tool saves you 2 hours a week, that is a very different value proposition than one that saves 10 minutes.

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