AI-Powered Market Intelligence: Track Evidence and Changes

AI-powered market intelligence uses AI to organize and examine evidence about customers, competitors, and market conditions over repeated reviews. Begin with a business question, collect dated sources, check the claimed changes, and give a person a clear decision or follow-up question.

Its value comes from a maintained evidence trail. fenbs can keep research links, reviewed context notes, open tasks, and lessons together, so your next AI session can tell what changed and what remains an assumption.

How do you set up the first review?

1. Choose one decision to support, such as which customer segment deserves more interviews. Name the geography, offering, and planning period.
2. Create a source list covering competitors, customer evidence, and relevant market data. Record source dates and the exact question each source might answer.
3. Ask an assistant to extract observations with supporting locations. Require separate labels for reported facts, first-party claims, and interpretations.
4. Verify the findings most likely to change an action. Compare them with your previous reviewed baseline, not with the assistant’s recollection.
5. Write a short update: what changed, why it matters, what remains uncertain, and who will take the next step.

For example, a small US scheduling-software company might track whether independent repair shops describe a recurring booking problem. That is a hypothetical research question, not evidence that the problem exists. The review should test that idea rather than gather only material that makes it sound promising.

What belongs on a useful source list?

Use original material wherever possible. A competitor’s own documentation can show how it describes a feature, while a customer interview can show how that person experiences a problem. Neither alone establishes how an entire market behaves.

The SBA market research guide↗ (opens in a new tab) distinguishes existing-source research from direct customer research and discusses competitive analysis by segment. The SEC’s filing search↗ (opens in a new tab) provides access to public company disclosures when those companies are relevant to your market.

Keep the original source separate from articles that repeat it. Five websites repeating one announcement are one underlying signal, not five independent confirmations. Save the references and access dates in fenbs through Links and listings, along with a note explaining why each matters.

What should you ask the assistant to extract?

Ask for observations before conclusions. Each observation needs a subject, a precise statement, a source location, and a date or explicit note that the date is unavailable. Request the wording needed to check the statement without copying unnecessary source material into the final update.

Business question: [specific decision or uncertainty].
Scope: [customer segment, geography, offering, period].
Use only the supplied authorized sources and reviewed baseline.
For each observation, provide the source URL, date if available, and supporting location.
Separate first-party claims, independently supported facts, and your interpretation.
Identify contradictions and coverage gaps.
Compare like-for-like observations with the baseline; do not infer a change from missing information.
Return a short update with what changed, possible implications, and checks a person should perform.
Do not make the business decision.

Keep your criteria stable between runs. If you change the definition of the target segment or the sources you inspect, note that change before comparing results. Otherwise, a difference in research coverage can look like a difference in the market.

Avoid including personal customer information when an anonymized observation will answer the question. Respect access restrictions and your organization’s rules for the material you supply. A research note should retain the usable evidence without spreading unnecessary sensitive details into more tools.

How do you tell a real change from a new mention?

Separate publication date, event date, reporting period, and access date. A newly published article may discuss an old event, and a page you visited today may have been unchanged for months. When the original date is unknown, say so rather than assigning the date of discovery to the event.

Compare the same subject under the same definition. A new product announcement is evidence of an announcement; it does not establish that customers are using the product. A customer reporting a problem is evidence of that customer’s experience, not proof that demand has grown across your whole segment.

For the scheduling-company example, several interviewees mentioning missed calls might justify a targeted follow-up question. Before calling it a growing pattern, check whether the interview pool, question wording, and observation period changed. Keep a finding provisional when you cannot separate those explanations.

Write the reviewed baseline as a short project note with its evidence links. The AI brain guide explains why keeping work outside a private chat matters. Your baseline should remain readable even when you choose a different assistant for the next review.

How should you turn findings into follow-up work?

Use a short structure: observation, interpretation, uncertainty, next action. For example, “The vendor added a public integration guide” is an observation. “Buyers may ask for a similar connection” is an interpretation that might justify customer interviews, not a product commitment.

Assign an owner to each useful check. Leave weak signals in a note instead of making every mention a task. When evidence leads a person to change direction, record that person’s choice and rationale separately from the research so an assistant does not turn its own recommendation into an approved plan.

Record a lesson when the process misled you, such as counting repeated press coverage as independent evidence. fenbs keeps Lessons learned and Decisions and rules beside the project, helping the next review use the better method and respect the decision already made.

How to do this with fenbs

1. Create a free account and add a project named for the market question you are tracking.
2. Add source URLs to Links and listings. Write the segment, geography, evidence rules, and reviewed baseline in project context notes.
3. Store the reusable research instructions in Skills and prompts and the review steps in Routine processes. Create tasks for follow-up interviews or evidence checks.
4. Ask your connected assistant to read context before a review and save progress afterward. Record a person’s decisions in Decisions and rules and reusable research mistakes in Lessons learned.

fenbs is free for one person, no credit card. It does not automatically monitor competitors, scrape websites, or purchase market datasets. If you need continuous data collection, choose a suitable research service and use fenbs for the reviewed work that follows.

Which nearby workflow answers a different question?

Use AI for market research for a focused study, AI in business intelligence for internal data analysis, conversational intelligence for conversation analysis, collaborative intelligence for people and AI working together, and the AI tools guide for general tool selection. This recurring review should stay centered on external evidence and its changes.

For broader working habits, see how to use AI at work and the AI at work topic hub. A useful update lets the next reviewer trace the evidence and understand the pending action without reopening the entire conversation.