AI Use Cases and Business Applications
Framing use cases
Strong candidates translate a business problem into a measurable AI task and state the value hypothesis. Distinguish automation (rules-based, deterministic) from augmentation (AI assists a human) and from autonomous decisioning. Identify the task type: classification, regression, ranking, recommendation, forecasting, extraction, generation, or anomaly detection.
Common patterns
Know typical applications: demand forecasting, churn prediction, fraud detection, recommendation, document extraction, conversational assistants, and predictive maintenance. Each carries characteristic metrics and risks. Fraud favors recall and fast adaptation; forecasting favors error metrics and seasonality handling; extraction favors precision and human review on low confidence.
Feasibility and ROI
Assess data availability, label quality, latency and cost constraints, integration effort, and change management. Consider build versus buy, and whether a simpler non-AI solution suffices. Estimate value from the decision improved, not from model accuracy alone.
Human-in-the-loop
High-stakes or low-confidence cases need human review. Design escalation thresholds and feedback loops so reviewer decisions improve the system.
Worked example
A retailer wants fewer stockouts. The task is demand forecasting per SKU per store; the metric is forecast error tied to inventory cost; the value comes from reduced stockouts and holding costs. A rules-based reorder point may be the baseline the model must beat.
Rule of thumb
Define the decision, the metric, and the baseline before discussing algorithms. If you cannot state the baseline, the business case is incomplete.