Practical guide
AI fluency for research analysts: compare evidence scopes
Build a fictional evidence matrix that separates observations, interpretations and unanswered research questions.
A research analyst can use AI to organize evidence while preserving differences in scope and method. This original exercise compares three fictional internal research records about a collaboration practice. It focuses on what each record can support, not on producing a comprehensive literature review or presenting invented studies as real research.
Write a question the packet can address
A broad claim about effectiveness may exceed the available evidence.
The fictional packet contains a team retrospective saying meetings felt clearer, a count showing fewer meetings over one month, and an interview with a manager who preferred written updates. The requested question is whether the organization should investigate a new collaboration practice further.
Do not silently replace that question with whether the practice improves productivity everywhere. The packet contains experiences and observations, not a general causal evaluation. Ask AI to restate the question and identify the decision the evidence will inform.
Create one evidence row per record
Keep the source's observation separate from the interpretation attached to it.
For the retrospective, record the participants' reported experience and the team context. For the meeting count, record the period and counted event. For the manager interview, record an individual preference. Avoid combining all three into a single finding that the practice worked.
Add fields for scope, missing information and relevance to the question. The matrix should show why a record matters and what it does not establish. AI can draft rows, but the analyst must inspect whether it has inserted measurements or methods not supplied.
Examine alternative explanations
A change in an observed count does not explain itself.
The fictional meeting count fell during a month when one project also ended. Ask whether that change could affect the count independently of the collaboration practice. The packet cannot resolve the issue, but it can identify the question for further investigation.
Preserve reported clarity as a separate observation rather than using it to prove the count's cause. Different records may support different parts of an argument. A persuasive narrative should not erase those differences simply because the sources point in a broadly favorable direction.
Recommend the next evidence step
Choose a follow-up that addresses the most consequential uncertainty.
For this exercise, propose a clearer definition of the work outcome, a longer observation window or a comparison that accounts for project activity. Explain which uncertainty each step addresses. Do not claim that collecting more interviews automatically establishes causality or that a longer period removes every confounder.
Keep the recommendation proportionate to the immediate decision to investigate further. An exploratory evidence note can be useful without a definitive verdict. AI-generated research ideas should be labeled as proposals, not described as studies already completed.
Review the final evidence note
A reader should be able to trace each conclusion to the right kind of support.
Deliver the question, evidence matrix, alternative explanations and proposed next step. Ask a reviewer to identify which source supports the statement about perceived clarity and which supports the meeting count. If the references are interchangeable in the draft, the synthesis is probably too vague.
Review source fidelity and scope handling within this fictional packet. The customer insights guide focuses on coding comments and respondent counts; this resource compares heterogeneous evidence types and explains what additional research would be needed before strengthening a conclusion.
Sources and scope
The references below are background reading, not evidence that this fictional exercise has been validated or endorsed.
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