AI research for product relevance: require a concrete connection to the offer

Define the decision

AI can connect almost any public fact to a broad benefit. The reviewer needs a stricter test: which task is observable, which capability addresses it and what remains unknown? If that chain cannot be explained plainly, the outreach angle may be forced.

Work through the procedure

1. State the current product capability and its actual limits.
2. Identify the business task supported by the source, separating facts from hypotheses.
3. Explain the connection in one sentence without using generic claims about growth or efficiency.
4. Choose include, research further or exclude before asking the model to polish the message.

Worked example

The following is a synthetic example for this procedure, not a customer result or performance benchmark.

Verified task: several teams maintain recurring report definitions
Actual capability: record ownership and change notes
Connection: a shared change record may support that review
Unknown: whether the team already has a suitable process
Question: how is ownership currently recorded?

Read the result

The chain leaves an existing solution as a valid possibility. It does not claim that the prospect needs the product simply because a related task exists. If the product cannot address an important constraint, record the mismatch rather than letting the draft imply a capability beyond the implementation.

Check before moving on

1. Review the source and capability independently.
2. Remove angles that rely on sensitive personal inference.
3. Keep exclusions as useful research outcomes.

Limits and next action

This is a manual qualification method, not predictive intent scoring in Zintara. Research fields such as suggested angles are hypotheses. Verify the current product and the company context before converting them into recipient-facing claims.

Source: Zintara AI product context; review procedures do not imply additional native features