How to Humanize a Business Case Study Assignment After ChatGPT
ChatGPT case analyses invent market share and ignore the exhibit. Lock numbers from the case packet, name one recommendation, then humanize synthesis prose once—not invented citations.
Business case study assignments train a muscle ChatGPT does not have by default: recommend something using only what the packet gives you.
Models love to add a competitor you never read about, round revenue to a plausible percentage, and end with “the company should leverage synergies.” Professors have graded the same case for years. They know the exhibits.
Humanizing without fixing the numbers produces polished fiction. Lock packet facts first, then one voice pass on synthesis paragraphs.
Related: MBA admissions essays (biography, not packet analysis), policy memo for class (similar one-ask discipline), ethical AI checklist.
What case study rubrics punish
| Element | Weak AI draft | Strong packet-based draft |
|---|---|---|
| Evidence | “Industry growth is strong” | Exhibit 3: 12% CAGR, 2019–2023 |
| Recommendation | “Pursue a balanced strategy” | Enter Segment B; defer Segment A |
| Tradeoff | Ignored capacity constraint | Plant 2 at 94% utilization through Q4 |
| Framework | SWOT wallpaper | One lens from the syllabus |
| Tone | Consultant brochure | Direct; names the CEO from the case |
Packet-only rule
Before any model help, build a fact sheet from the PDF:
- Company name, year, and decision date in the case
- Three numbers you would defend in class (revenue, margin, share, headcount—whatever the exhibits show)
- Two constraints (budget, regulation, capacity, brand)
- One competitor action described in the text—not from your memory of the industry
- Your recommendation in one sentence
If a stat is not on an exhibit or in the narrative, delete it. ChatGPT will fill gaps with plausible lies.
Do not paste confidential employer data into a consumer chatbot. Use only the version your instructor distributed.
Typical case structure
Adjust to your rubric, but most assignments look like:
- Problem / decision (what the protagonist must decide)
- Analysis (options against criteria from class)
- Recommendation (one path; implementation steps)
- Risks (what could go wrong; packet-backed)
| Section | Humanize? | Why |
|---|---|---|
| Exhibit numbers, quotes | No | Accuracy |
| Framework labels from class | No | Use the course vocabulary |
| Analysis narrative | After matrix locked | AI flattens tradeoffs |
| Recommendation prose | Yes, one pass | Often the stiffest block |
Paste analysis and recommendation paragraphs into Human Writes once. Put back every number exactly as printed.
Before and after
AI recommendation:
The firm should adopt a holistic, customer-centric approach that leverages digital transformation to drive sustainable growth across key markets.
After packet facts:
Close the Dallas warehouse and consolidate into Memphis (Exhibit 5). Saves $4.2M annually but adds two-day shipping to West Coast retail partners—the group the case says drove 38% of FY revenue.
Second version has an ask, a number, and a cost from the file you were assigned.
MBA vs undergrad case assignments
| Undergrad strategy | MBA / exec format | |
|---|---|---|
| Length | Often 3–5 pages | Sometimes 1-page exec summary + backup |
| Framework | Porter, SWOT, one clear lens | Often “what would you do Monday?” |
| Evidence | Named exhibits | Same—no outside Bloomberg quotes |
| AI failure | Generic industry essay | Fake McKinsey voice |
Same workflow: packet sheet, recommendation, one humanize pass, re-check exhibits.
Workflow
- Read the case once without a model. Highlight every number.
- Fill the five-line fact sheet by hand.
- Build a 2×3 options table (option × criterion from the syllabus).
- Draft from the table—not from “analyze this case study.”
- One Human Writes pass on stiff analysis prose.
- Walk every figure back to an exhibit footnote.
- Disclose AI editing if the course requires it.
For group cases, align on the recommendation before anyone humanizes. See group project writing.
What not to do
- Cite HBR articles the assignment did not assign.
- Invent survey data or “industry benchmarks.”
- Recommend three strategies and pick none.
- Humanize five times until exhibit numbers drift.
- Treat a humanizer as a way to “beat” Turnitin on a case upload.
Scores are a review signal. Wrong numbers fail faster than a percentage. See best practices for humanizing.
Bottom line
Case study grades ride on packet fidelity and a clear recommendation. Human Writes is the voice pass after your exhibit sheet is true.
Paste stiff analysis prose on Human Writes when every number points to an exhibit you can name in class.