Think like an analytics leader. Make the call. Defend your reasoning.
Your CEO just announced a $2 million AI initiative at the all-hands meeting. Now the executive team needs to decide which projects to fund. You're the analytics lead—your recommendation carries weight.
Company: MidWest Manufacturing Co. — $450M annual revenue, 2,800 employees
Budget: $2 million for AI initiatives (Year 1)
Timeline: Board expects measurable results within 18 months
Data Maturity: Mixed — good ERP data, fragmented customer data, limited data science team (3 people)
Current Pain Points: Rising warranty costs, inventory inefficiencies, sales team spending too much time on low-value leads
👆 Your Task: Click on the use cases in order of priority (1 = highest). You have budget for approximately 1-2 projects. Then explain your reasoning below.
Use sensor data from manufacturing equipment to predict failures before they happen. Reduce unplanned downtime and optimize maintenance schedules.
The Pitch: "We're losing $3.2M annually to unplanned downtime. Industry benchmarks suggest 25-30% reduction is achievable."
Identify which customers are likely to leave so the retention team can intervene proactively. Integrate with CRM for automated alerts.
The Pitch: "Customer acquisition costs 5x more than retention. We lost 127 accounts last year worth $8.4M in annual revenue."
Deploy a GPT-powered assistant that helps sales reps generate proposals, answer technical questions, and prioritize leads using conversation analysis.
The Pitch: "Our competitors are doing this. Sales reps spend 35% of their time on admin tasks. This could free up 10+ hours per rep per week."
Improve inventory planning by predicting demand at the SKU level. Reduce both stockouts and excess inventory carrying costs.
The Pitch: "We're carrying $12M in excess inventory while simultaneously losing $2.1M to stockouts. Better forecasting could recover $3-4M annually."
You've selected: None yet
Total estimated cost: $0 of $2M budget
There's no single correct answer here—but there ARE better and worse reasoning processes. Here's what experienced analytics leaders would consider:
The best analytics leaders don't just evaluate projects—they sequence them. Notice how Demand Forecasting builds capabilities (better data pipelines, forecasting expertise, stakeholder trust) that make future projects easier.
Budget Strategy: Consider funding Demand Forecasting ($550K) as your primary initiative, then use remaining budget ($1.45M) for data infrastructure improvements that enable Year 2 projects like Churn Prediction.
You're the analytics manager at a regional bank. Six months ago, your team deployed a customer churn prediction model that was performing beautifully. This morning, you got an alert that stopped you cold.
The retention team relies on your model's predictions to prioritize outreach. They have capacity to call ~200 customers per week. If the model is flagging the wrong customers:
👆 Your Task: Select your immediate response, then explain your reasoning. What do you do RIGHT NOW?
Experienced analytics leaders know that drift is a symptom, not a diagnosis. Jumping to solutions before understanding the cause often makes things worse.
Look at the timeline. There are THREE potential causes of drift:
Each cause requires a different solution. Retraining won't help if the problem is label drift. Threshold adjustment won't help if customer behavior fundamentally changed.
The takeaway: AI fails when history (the data it was trained on) doesn't rhyme with reality. The Fed raising rates, a new competitor, and a marketing campaign ALL changed the "ground truth" your model was trained on.
The data scientists who advance fastest aren't the ones who build the best models—they're the ones who manage models in production and communicate effectively when things go wrong. This scenario is your opportunity to demonstrate leadership, not just technical skill.