AI Case Study Generators: Write From Evidence

An AI case study generator turns supplied material into a customer story draft. Use it to organize approved evidence, explain a sequence of work, and suggest follow-up questions; never ask it to fill missing results or invent a customer voice.

The useful starting point is a small evidence packet: interview excerpts, a verified result, the conditions behind that result, and permission to use them. fenbs can keep the source links, claim checks, and editorial decisions together while people review the draft in their writing tool.

How do you get from source material to a checked draft?

1. Choose one reader question, such as whether your process can reduce repeated scheduling work. Describe the customer situation and the result you can actually support.
2. Collect approved sources. Label each interview excerpt, report, and screenshot with its owner, date, permitted use, and location.
3. Build a claim list before drafting. Separate documented facts, customer opinions, calculations, and questions that still need answers.
4. Ask the generator for an outline and then a draft using only those sources. Require a source label beside each factual claim and an explicit gap where evidence is missing.
5. Check the draft against the original sources, obtain the required customer and internal approvals, and preserve the approved version with its evidence record.

Start with a modest story that has complete evidence. A specific explanation of what changed is more useful than a sweeping success claim with no measurement behind it. These are editorial AI use cases where people still own accuracy and publication.

What belongs in the evidence packet?

Give every source a stable label, such as Interview A or Report B. Record whether the material is approved for publication, available only for background, or awaiting review. Access to a recording does not by itself establish that you may publish everything in it.

Remove unnecessary personal and confidential information before sharing material with any generator. Use your organization’s approved tool and data policy. If a source cannot leave its current system, give the drafting assistant a reviewed factual summary rather than copying the underlying record.

For an interview that still needs organizing, use the separate AI note generator workflow; this workflow begins when you are selecting evidence for a publishable customer story. Keep the chosen source references in fenbs Links and listings so reviewers can find them again.

What should you ask the generator to write?

Ask for the sequence a reader needs: the customer’s situation, the problem, what the team did, the observed outcome, and the limits of the evidence. Supply the audience and intended length. A generator cannot infer whether your goal is technical evaluation, a sales conversation, or an accessible overview.

Draft a customer case study for [audience] using only the approved material below.
Use this structure: situation, problem, work performed, observed outcome, limitations.
Attach a source label to each factual claim. Keep direct quotations exact.
Do not invent numbers, quotations, people, motives, or causal explanations.
Where support is missing, write EVIDENCE NEEDED and list the question separately.
Separate customer opinion from measured results.
Approved material: [labeled excerpts and facts].

Generate the outline first. Check that the proposed story follows the actual chronology and does not turn a side benefit into the main result. Then draft one section at a time if the packet contains conflicting accounts or several measurement periods.

For a hypothetical design studio, the supported story might be that a client approved a simpler review process and reported fewer clarification emails. Without a comparable count before and after, the draft should not translate that impression into a percentage improvement. Missing evidence should create an interview question, not more polished certainty.

How do you check numbers, quotes, and implied claims?

Check numbers outside the generator. Recalculate any change from the source values, preserve units, and confirm the comparison period. A fall from ten hours to eight hours is not evidence that all customers will save the same time, or that your product alone caused the change.

Read the headline, images, and body together. The FTC’s advertising guidance↗ (opens in a new tab) explains that advertisers need support for both express and implied claims before an ad runs. A cautious sentence in the body may not fix a headline that promises a broader result.

Compare every direct quote with the approved excerpt. Do not put a generated paraphrase inside quotation marks. The FTC’s endorsement guidance↗ (opens in a new tab) says endorsements must be honest and not misleading, and explains when material connections and exceptional results need attention. Ask the appropriate reviewer about the specific presentation.

• Does each result retain its denominator, time period, and relevant conditions?
• Does the story distinguish what happened after the work from what the work demonstrably caused?
• Are quotations accurate, correctly attributed, and approved in their final context?
• Would a reasonable reader take away a stronger promise than the evidence supports?

Who approves the story before publication?

Name a person who can verify results, a person who can approve the customer’s participation, and a person who owns publication. In a small company one person may hold several roles. Make the responsibilities explicit so a friendly response to an early draft does not become assumed approval of a different final version.

Use a short review checklist tied to the actual version. Record approved claims and unresolved questions separately. The AI approval workflow explains how to keep a proposed action distinct from permission to carry it out. For the later advertising stage, follow the ad copy generator workflow.

When a reviewer changes a number or removes a quote, update the claim record before producing derivative copy. Otherwise a later assistant may reuse the earlier draft. fenbs Decisions and rules can retain the person’s choice and the boundary that future drafts must respect.

How to do this with fenbs

1. Create a free account and add a project for the customer story.
2. Add approved evidence locations to Links and listings, and create tasks for missing measurements, quote checks, and final review.
3. Record the customer’s approved uses and internal editorial rules in Decisions and rules, identifying who made each decision.
4. Ask a connected assistant to read the project context before drafting, then save the checked claims, unresolved questions, and next reviewer in Where we left off.

fenbs does not generate customer interviews or verify results for you. If you only need to format an already approved story once, your writing tool may be sufficient. It becomes useful when evidence and approvals must remain understandable across several drafts, people, or AI sessions.

Use the AI at work hub for adjacent workflows; academic learning belongs in how to study with AI. Begin this workflow by collecting a single approved result and its source, giving your next draft a claim a person can actually check.