Practical guide
AI fluency for competitive intelligence: build a dated evidence matrix
Compare fictional public product claims by source, date and confidence without turning missing evidence into a competitive conclusion.
Competitive intelligence analysts can use AI to organize public evidence while checking source dates, wording and comparability. This fictional exercise produces a dated evidence matrix, not market surveillance, legal advice or a claim that one company is better.
Define the comparison question
A useful matrix begins with the decision it may inform.
The fictional team asks whether two public offerings describe an export control and how each prices access. This does not ask which product is best. Write the question, geography, audience and evidence cutoff before searching the supplied packet.
Ask AI to propose fields, then retain only fields supported by the decision. Do not add popularity, security or customer satisfaction because those dimensions are not evidenced.
Capture claims verbatim enough to verify
Paraphrase can erase qualifications such as beta, selected plans or coming soon.
The packet contains dated fictional product pages. One says export controls are available on selected plans; another says export is in beta. Record the exact qualifier, source location and observation date beside a short neutral paraphrase.
Do not infer general availability from a screenshot or a search snippet. Mark inaccessible or undated evidence as a gap rather than filling it with a model-generated description.
Normalize without claiming equivalence
Similar labels may describe different scopes.
Create separate fields for export format, administrator control and stated availability. The evidence supports different statements across those fields, but not an equivalence judgment. Keep source wording next to the normalized value.
Ask AI to flag apparent conflicts, then inspect each source. A pricing page and documentation page may have different update dates; record both instead of silently choosing the convenient one.
Test a changed-source scenario
Competitive evidence expires when the underlying page changes.
A later fictional pricing page removes the plan name but does not explain whether access changed. Update the matrix with the new date and mark pricing comparability unresolved. Do not treat removed text as proof of a price increase or feature withdrawal.
Preserve the prior snapshot as historical evidence with its observation date. The current matrix should distinguish what is observed, what is inferred and what requires confirmation.
Deliver the matrix and evidence gaps
A useful handoff allows another person to recheck every important cell.
Include question, cutoff, source URLs from the packet, observed statements, normalized fields, dates, confidence reasons and missing evidence. Add no recommendation unless a decision owner asks for one.
Review source fidelity and temporal limits. Market research examines audience needs; this page develops a reproducible public-claim comparison without asserting competitor intent, performance or superiority.
Separate absence of evidence from evidence of absence
A blank matrix cell can mean several different things.
For one fictional product, the supplied documentation does not mention administrator export controls. Mark the cell not found in reviewed sources, with the source set and cutoff. Do not translate that into the feature does not exist. A public page may be incomplete, gated or updated after the observation date.
Ask AI to suggest confirmation routes such as current documentation, an official pricing page or an authorized product contact, but do not fabricate a response. Prioritize the route by relevance to the decision and record when the matrix must be refreshed. This makes the artifact reusable without pretending that public evidence is comprehensive.
Sources and scope
NIST: AI risk management. Skills England: workplace AI foundations.
These references provide background, not validation or endorsement of this exercise. The case details, calculations and suggested review questions are original instructional material. Use them to discuss observable work, not to infer customer outcomes, professional credentials or performance in every setting. Before adapting the exercise, confirm the relevant facts, approved tools, data permissions and decision owners. If you change the case, revisit the expected answers and checks as well. These examples describe practice tasks, not a promise that a particular product includes the fictional features.
Sources: [1] [2]
Sources
- 1.AI RMF Core · NIST
- 2.AI foundation skills for work benchmark · Skills England