6 templates · ~2 hours · BeautyCo case throughout
Each template follows the same rhythm: Learn, review the BeautyCo evidence, use a copyable prompt, open the right AI tool, challenge the output, save your work. Discover → Diagnose → Decide → Deliver.
"Why is increased traffic not translating into stronger business outcomes, and what should BeautyCo do next?"
Clarify the decision before analysing or recommending. Use AI as a thinking partner to ask clarification questions, formulate the decision question, identify objectives, expose assumptions, and define constraints.
| KPI | 2024 | 2025 |
|---|---|---|
| Footfall | 220,000 | 282,000 |
| Revenue | $4.8M | $5.0M |
| Conversion | 24% | 18% |
| CSAT | 82 | 71 |
| Repeat purchase | 38% | 29% |
Role: You are an experienced strategy and decision-intelligence advisor. Goal: Clarify the decision that needs to be made. Context: BeautyCo is a premium beauty retailer. Traffic has increased, but revenue is almost flat. Conversion, CSAT and repeat purchase have declined. Before answering: Ask up to three clarification questions if the decision is unclear. Tasks: 1. Frame the decision question. 2. Identify the business objective. 3. Identify key symptoms. 4. Surface initial assumptions. 5. Identify information gaps. 6. Identify decision constraints. Output: A Decision Framing Brief — decision question, objective, symptoms, assumptions, information gaps, constraints.
"What assumption did AI make too quickly?"
🗳️ NearpodBuild a complete picture before diagnosing. This is the first template where tools differentiate — NotebookLM grounds you in evidence, Colab analyses the numbers, ChatGPT/Gemini synthesizes it all.
| Evidence need | Tool / Agent |
|---|---|
| Internal documents | NotebookLM / Projects |
| External market evidence | Deep Research / Research Briefing Agent |
| Data patterns | Data Insight Agent |
Based only on the supplied sources: 1. What direct evidence shows a conversion problem? 2. Separate observations from interpretations. 3. What customer complaints appear repeatedly? 4. What external trends matter to the decision? 5. What important evidence is still missing?
Use the prepared BeautyCo Colab notebook — KPI trends, store comparisons, traffic vs. revenue, stock-outs vs. revenue, staffing vs. conversion.
Assess the situation. Organise into: 1. Internal signals 2. External signals 3. Customer/stakeholder signals 4. Emerging themes 5. Risks and opportunities 6. Evidence gaps For every signal, separate: Observation / Interpretation / Decision implication. Do not identify final root causes yet.
Generate competing explanations before choosing one. Expand the hypothesis space across customer, product, people, process, technology and market causes — without ranking them yet.
Generate five distinct explanations for why BeautyCo's traffic is increasing while conversion, satisfaction and repeat purchase are declining. For each: state the cause, explain the logic, identify supporting evidence, identify contradicting evidence, identify additional data required. Group into: Customer / Product / Process / People / Technology / Market. Do not rank the explanations yet.
Use NotebookLM for qualitative evidence, Colab where numbers are relevant.
Collaborate Board: "Post one plausible cause other groups may have missed."
🗳️ Nearpod BoardThe strongest point of integration in the course. Ground claims in documents, inspect the numbers, then run an AI critique.
| Validation need | Tool / Agent |
|---|---|
| Internal evidence check | NotebookLM / Projects |
| External benchmark | Deep Research |
| Data pattern check | Data Insight Agent |
| Structured critique | Imperial AI Council / Reflection pattern |
Staff capacity vs. conversion, stock-outs vs. sales growth, store-level outliers.
Evaluate the hypotheses from Template 3 objectively. For each: 1. Supporting evidence 2. Contradictory evidence 3. Missing evidence 4. Possible bias/assumption 5. Confidence: High/Medium/Low 6. What evidence would change the conclusion. Prioritise by evidence strength. Do not treat correlation as causation.
"How confident are you that staffing is a primary cause?" High / Medium / Low / Insufficient evidence.
🗳️ Vote| Hypothesis | Confidence |
|---|---|
| Service Capacity Constraints | High |
| Inventory Availability Issues | High |
| Loyalty Programme Weakness | Medium |
| Traffic Quality Issues | Low |
Diagnosis statement: Traffic generation is not the primary issue. Conversion capability is.
Generate broadly, then evaluate rigorously — in three rounds: generate options, score against criteria, then examine from multiple stakeholder perspectives.
Generate six possible actions to address the prioritised BeautyCo diagnosis: low-cost quick wins, operational fixes, customer-experience improvements, process/technology improvements, longer-term strategic options. Do not recommend yet.
Score: Impact · Feasibility · Cost · Risk · Evidence · Time-to-value.
Evaluate each option from: Customer, Employees, Operations, Finance, Brand, Risk & compliance. State what they value, would support, concerns, trade-offs. Recommend a prioritised sequence.
"Which option should BeautyCo prioritise?" — vote before and after the evaluation.
🗳️ Run the Vote| Option | Impact | Feasibility |
|---|---|---|
| Increase Marketing Spend | Low | High |
| Improve Staffing | Medium | High |
| Improve Inventory Planning | Medium | Medium |
| Integrated Service + Inventory Improvement | High | Medium |
Recommended option: Integrated Service + Inventory Improvement — fix conversion capability before driving more traffic.
Translate the recommendation into coordinated execution using every previous output as context.
Use all five previous outputs as context. Create a 30/60/90-day executive action plan: recommended initiative, workstreams and actions, owners, timeline, KPIs and targets, risks and mitigation, dependencies, human approval points, stakeholder communication, monitoring cadence. Identify what can be AI-assisted vs. requires human judgement or approval.
Final reflection: strongest part of the plan, biggest unresolved risk, assumption to monitor.
🗳️ Final ReflectionKey initiatives: Service Capacity — workforce scheduling, queue management, advisor training. Inventory Availability — inventory forecasting, automated replenishment, stock visibility. Loyalty Enhancement — personalized promotions, AI-assisted recommendations, tiered rewards.
| KPI | Current | Target |
|---|---|---|
| Conversion | 18% | 22% |
| CSAT | 71 | 80 |
| Repeat purchase | 29% | 35% |
| Stock-out rate | 14% | <8% |
| Template | Thinking Pattern |
|---|---|
| 1. Decision Framing | Expert Perspective (RGCTO) |
| 2. Situation Assessment | Structured Analysis (Chain-of-Thought) |
| 3. Root Cause Analysis | Multiple Explanations (Tree-of-Thought) |
| 4. Evidence Validation | Critical Review & Evidence Validation (ReAct + Reflection) |
| 5. Options Assessment | Trade-off Evaluation (Multi-Perspective Reasoning) |
| 6. Executive Action Plan | Action Planning (Plan-and-Solve / Prompt Chaining) |
AI thinking patterns are embedded naturally — the focus stays on Decision Intelligence, not prompt engineering.