General Peer Cases

General — Peer Case Library

Surface relevant named peer cases for the asker's situation. Output what worked, what failed, what they would do differently — and which single case is the closest analog.

Anchor base rate: 95% of GenAI pilots produce zero measurable P&L impact (MIT 2025). Only 5% break through. Every retrieved case must be situated against this floor — a "success story" pulled in isolation is misleading.

The library is bundled in references/. Pull cases by archetype match — not by name recall.

Output contract (stable): asker profile, case bundle, cross-case patterns, and a confidence state — including No-analog-found when fewer than 3 archetype matches exist.

Step 1: Asker Profile

Filter the library before retrieving. Without a profile, every case looks equally relevant — and nothing is actionable.

PROFILE QUESTIONS — answer each in one line:

1. Vertical? (Healthcare / FinServ / Public sector / Manufacturing / Retail / Tech / Professional services / Other — name the sub-vertical if it matters, e.g. "tertiary hospital" not just "healthcare".)
2. Org size and operating model? (Headcount band; centralized vs federated; regulated vs unregulated; geography.)
3. Use-case archetype? (Customer service automation / knowledge-worker copilot / document extraction / agentic workflow / vertical AI product / internal search — pick one. If unclear, force it.)
4. Maturity stage? (Experimenting / Piloting / Scaling / Operating — map against MIT-CISR's four stages. Consult mit-cisr-4-stages.md.)
5. Decision being made? (Selection / build-vs-buy / scale-vs-kill / vendor swap / governance gate — the case bundle should be tuned to this decision.)

If the asker can answer fewer than 4 of these, stop. Surface the gap. Cases retrieved without profile are noise.

Output: VERTICAL | ORG SIZE | ARCHETYPE | MATURITY STAGE | DECISION TYPE

Step 2: Case Retrieval

Pull 3-5 named cases from the bundled library. Match on archetype first, vertical second, org-size third. Never pad with weak matches — 3 strong analogs beat 5 mixed ones.

For each case, return the same six fields. No prose.

CASE SCHEMA:

• YEAR — when the deployment ran (or peak-public reporting year).
• MODE — Replace / Augment / Create (operational, not marketing).
• OUTCOME — measured result. Quantified where possible. Flag if only vanity metrics exist.
• STORY — three sentences max: what they did, what they tried, what happened.
• LESSON — the one thing the operator said in retrospect they would do differently.
• SOURCE — named report / paper / executive interview. No anonymized cases.

Stanford studied 51 enterprise GenAI deployments and found 77% of difficulty was invisible cost — change management, data, process redesign — not the model. Consult stanford-51-deployments.md for the canonical retrieved-case set; supplement with european-fintech-case.md for the canonical Replace-mode failure mode (700 agents replaced; CSAT -22%; hiring resumed in months).

If fewer than 3 cases match the archetype, state "No-analog-found" and skip to Step 4 with that verdict — do not fabricate adjacency.

Output: CASE_1 (year/mode/outcome/story/lesson/source) | CASE_2 (...) | CASE_3 (...) | CASE_4 (optional) | CASE_5 (optional)

Step 3: Cross-Case Pattern Extraction

Aggregate. Find the signal across the bundle — not the headline of any single case.

CONVERGENCE — what is the same across all retrieved cases?

• Common precondition (e.g. "all five had a named accountable executive before scaling").
• Common failure mode (e.g. "all five tracked volume; none tracked quality at week 12").
• Common time-to-value (state the median; state the spread).

DIVERGENCE — where do the cases split?

• Buy-vs-build split: name the count. Consult nanda-tech-buy-vs-build.md if relevant — buy deploys 2x faster; build wins only with deep moat.
• Replace-vs-Augment-vs-Create split: name the count.
• Centralized vs federated ownership split.

EMPIRICAL FLOOR / CEILING.

• Floor: the worst-case outcome among the retrieved cases. Quantified.
• Ceiling: the best-case outcome among the retrieved cases. Quantified.
• 95/5 anchor: state explicitly which retrieved cases sit on the 5% breakthrough side and which are in the 95% no-impact zone. Consult 95-5-genai-divide.md — most "case studies" in circulation are dressed-up 95% cases.

OBJECTIVE-MIX CHECK. Map each case to efficiency / growth / innovation per mckinsey-3-objective-mix.md. Note the dominant objective in the bundle — and whether the asker's stated objective matches the bundle's center of gravity.

FUTURE-BUILT SIGNAL. Consult bcg-future-built.md: leaders pursue 10/20/70 split — 10% algorithm, 20% tech/data, 70% people/process. State for each case whether the operator credited the win (or blamed the failure) on the 70% layer.

Output: CONVERGENCE | DIVERGENCE | FLOOR/CEILING | 95/5 PLACEMENT | OBJECTIVE MIX | 70% SIGNAL

Step 4: Closest-Match Recommendation

From the bundle of 3-5, pick exactly one. The closest analog. State the parallel explicitly. State the divergence explicitly. State what the asker should do differently because of the divergence.

CLOSEST CASE: Name the case in one line.

PARALLEL — three concrete points where the asker's situation matches:

1. (e.g. same vertical, same archetype, same regulatory regime)
2. (e.g. comparable org-size and operating model)
3. (e.g. same maturity stage at decision time)

DIVERGENCE — three concrete points where the asker's situation differs:

1. (e.g. asker is federated; case was centralized — implication?)
2. (e.g. asker has weaker data foundation — implication?)
3. (e.g. asker is in regulated EU context; case was US — Art. 26 EU AI Act applies, case did not face it.)

ACTION INHERITANCE — for each parallel, what the asker should copy:

• (e.g. "Adopt their named-executive-owner pattern before scaling — saved 6 months in case.")

ACTION DIVERGENCE — for each divergence, what the asker must do differently:

• (e.g. "Case did big-bang rollout. Asker is federated — must phase by BU; expect 9-month not 4-month time-to-value.")

CONFIDENCE STATE: state one — High-confidence-analog / Partial-analog / No-analog-found.

• High-confidence-analog: 3+ parallels, 0-1 material divergences.
• Partial-analog: 2 parallels OR 2+ material divergences — case is directional, not prescriptive.
• No-analog-found: stop here. Tell the asker the library does not contain a sufficient match — and what they would need to source.

Output: CLOSEST CASE | PARALLELS | DIVERGENCES | ACTION INHERITANCE | ACTION DIVERGENCE | CONFIDENCE STATE

Synthesis: Case Bundle + Cross-Case Lessons + Closest-Match Action

Consolidate Steps 1-4 into a peer-learning brief. Write it the way the asker would want to hear it from a peer over coffee — terse, specific, named.

PROFILE: one line. (Vertical | Size | Archetype | Stage | Decision.)

CASE BUNDLE: the 3-5 cases as a numbered list, six fields each, no narrative connective tissue.

CROSS-CASE LESSONS: three bullets max.

1. The convergence finding the asker should treat as a precondition.
2. The divergence finding the asker should treat as a fork-in-the-road.
3. The 95/5 placement — how many of these cases actually broke through, and what separated them.

CLOSEST-MATCH ACTION:

• Named case: [case name].
• Inherit: [the one specific pattern to copy].
• Adjust: [the one specific divergence-driven adjustment].
• Avoid: [the one mistake the case made that the asker is still upstream of].

CONFIDENCE: High-confidence-analog / Partial-analog / No-analog-found.

NETWORK PROMPT: name 1-2 specific people / forums / events the asker should approach to deepen the case (e.g. "Stanford GSB Digital Business Academy alumni in same vertical"; "the named operator from the closest-match case if reachable; LinkedIn intro path"). Peer learning is incomplete without a route to a live conversation.

Output: PROFILE | CASE BUNDLE | CROSS-CASE LESSONS | CLOSEST-MATCH ACTION | CONFIDENCE | NETWORK PROMPT

References

All files below live in references/ at the plugin root (${CLAUDE_PLUGIN_ROOT}/references/ when installed as a plugin).

• stanford-51-deployments.md — 51-deployment study; 77% invisible-cost finding; canonical retrieved-case set.
• 95-5-genai-divide.md — base-rate anchor: 95% of pilots produce zero measurable P&L impact; 5% breakthrough criteria.
• european-fintech-case.md — canonical Replace-mode case: 700 agents replaced, CSAT -22%, hiring resumed; the measurement-gap failure mode.
• mckinsey-3-objective-mix.md — objective-mix classification (efficiency / growth / innovation) for cross-case mapping.
• bcg-future-built.md — 10/20/70 split; the 70% people/process layer signal in retrieved cases.
• mit-cisr-4-stages.md — Experimenting / Piloting / Scaling / Operating maturity-stage filter for case match.

Reference files are bundled with this skill — Claude resolves them by filename regardless of install layout (single-skill or plugin).