Evaluating AI solutions before investing

By the Lynova.ai team · Published October 12, 2026

In short

Before investing in AI, define the specific job it will do, check the data it needs, review security and privacy terms, run a small pilot against a clear measure, and confirm who will own it after launch.

1. Define the job

Write one sentence describing what the AI will do, for whom, and what “good” looks like. If you cannot write that sentence, the project is not ready for a purchase decision.

2. Check the data and integrations

Ask what information the tool needs, where it comes from, and whether it can connect to your systems through supported interfaces. Many AI disappointments are really data problems.

3. Review security, privacy and risk

Ask vendors where your data is processed and stored, whether it is used to train their models, who can access it, and how access is controlled. For AI that reads documents or takes actions, review risks such as prompt injection and excessive permissions, which are covered in the OWASP Top 10 for LLM Applications. The NIST AI Risk Management Framework and its Generative AI Profile offer a broader checklist for governance.

Is customer or employee data used to train the vendor’s models?
Can access be limited by role?
Is there an audit log of what the AI did?
What happens to your data if you cancel?

4. Run a small pilot with a clear measure

Test with real work, a small group of users and a measure agreed in advance, such as time per task or accuracy against a reviewed sample. Compare the result with how the work is done today.

5. Confirm ownership and total cost

Someone has to maintain prompts, rules, integrations and user access after launch. Include licensing, usage-based fees, setup, training and ongoing support when comparing options.