Consulting Experts — Planning AI Project

# Consulting Experts — Planning AI Project

## 🏢 About Vetto
Vetto is a global platform that connects top-tier professionals to strategic Artificial Intelligence projects around the world. Our mission is to build trust, quality, and long-term value within the AI ecosystem, for both exceptional talent and companies operating at the forefront of technology.

## 📖 About the project
We're recruiting management consulting experts to review and improve real-world business scenarios used to train AI planning assistants in an educational context. The AI model will act as a tutor — structuring complex problems, building hypothesis trees, and teaching strategic reasoning to business students. Your job is to think like a consultant at a top-tier firm: break down ambiguous problems, structure your approach, identify and discard hypotheses with data, and deliver a clear recommendation.

## 👤 Who can apply
Current or former consultants from strategy and management consulting firms (e.g. McKinsey, BCG, Bain, Roland Berger, Oliver Wyman, Accenture Strategy, or similar). Professionals with experience in structured problem solving, issue trees, and hypothesis-driven analysis. MBA students or final-year undergraduates with strong case interview or strategy project experience are also welcome to apply.

## 🧩 Selection
In this application, you will answer questions following the instructions below. If selected, you will be invited to review real consulting case scenarios as part of the project.

For the reasoning case, present a real business problem you diagnosed or solved — a strategic decision, market entry, operational inefficiency, organizational challenge, etc. You may anonymize it. We are not evaluating whether your conclusion was right. We are evaluating how you think.
The case is structured in 4 parts:

**Part 1 — The Problem:** describe the business problem and what data or information you had available at the start.

**Part 2 — Your Journey:** describe your reasoning in 3 steps. For each step, explain what you analyzed or decided and what specific evidence, data, or framework drove that decision.

**Part 3 — Discarded Alternatives:** for each of the 3 steps, list at least 2 hypotheses you considered but ruled out and explain what concrete data eliminated each one. "It wasn't the case" is not a valid answer.

**Part 4 — Conclusion:** describe the final recommendation or solution and how the evidence you gathered led to it. Also highlight 1–2 key insights — the most important findings or turning points in your reasoning: a piece of data that confirmed your direction, a detail that ruled out a strong alternative, or a non-obvious observation that most people would have missed.

**Example:**

Part 1 — The Problem
A retail client was losing market share in a key region over 3 consecutive quarters. Revenue had dropped 12% YoY. Leadership suspected a pricing issue but had no data to confirm it.
Part 2 — Your Journey
Step 1: Mapped the revenue decline by product category — 80% of the loss was concentrated in 2 out of 11 categories, which suggested a targeted issue rather than a broad pricing problem.
Step 2: Analyzed competitor pricing and shelf placement data for those 2 categories — a new regional competitor had undercut prices by 15% and secured premium shelf space in 60% of key retail points.
Step 3: Ran a customer survey in the affected region — 68% of former buyers cited price as the primary switch reason, and 71% were unaware of the client's loyalty program.

Part 3 — Discarded Alternatives
Step 1 — Alternative 1: Overall pricing misalignment / Ruled out by: decline was concentrated in 2 categories only — other 9 categories held stable or grew.
Step 1 — Alternative 2: Macroeconomic downturn / Ruled out by: competitors in the same region grew during the same period.
Step 2 — Alternative 1: Product quality issues / Ruled out by: return rates and NPS scores for the affected categories were unchanged.
Step 2 — Alternative 2: Distribution gap / Ruled out by: stock availability data showed no supply disruptions in the region.
Step 3 — Alternative 1: Brand perception problem / Ruled out by: unaided brand awareness remained flat — the issue was price sensitivity, not brand erosion.
Step 3 — Alternative 2: Seasonal demand shift / Ruled out by: the decline persisted across all 3 quarters including peak season.

Part 4 — Conclusion

The root cause was a localized competitive pricing threat combined with low loyalty program awareness. Recommended a targeted price adjustment on the 2 affected categories and an activation campaign for the loyalty program in the region. Projected recovery of 7–9% revenue within 2 quarters.

Key insights: The most critical finding was that the decline was concentrated in just 2 categories — this single observation shifted the entire framing from a broad pricing strategy problem to a localized competitive response. The second turning point was the survey data showing 71% loyalty program unawareness: it revealed a low-cost lever that leadership had completely overlooked.

⚠️ This is just an illustrative example. Your application should include more detail, specific data points, and thorough reasoning for each discarded alternative.

## 💰 Compensation
Payment will be US$ 60 per approved task, converted and paid in your local currency. Each task takes approximately 80 minutes, which corresponds to an effective rate of about US$ 45 per hour.

‼️ AI is not allowed. If we spot AI use, we'll block the application.
⚠️ This application form must be completed entirely in English or Portuguese.