AI Use Cases and Business Value
Business value comes from solving a real problem with measurable outcomes, not from adopting AI for its own sake. Frame use cases by the value lever: cost reduction (automation, triage), revenue growth (personalization, recommendation), risk reduction (fraud, compliance monitoring), or experience improvement (support, search).
Know common patterns: classification (spam, churn), regression (demand forecasting), ranking/recommendation, anomaly detection, computer vision (defect detection), NLP (summarization, extraction), and generative use cases (drafting, coding assistance, conversational agents). Match the pattern to the data you actually have - a use case without labeled data is not a supervised one.
A credible business case includes baseline (current performance), target metric, cost of errors, and ROI including data, compute, integration, and human review. Consider human-in-the-loop for high-stakes decisions. Distinguish augmentation (assists a human) from automation (replaces the step); augmentation usually has lower risk and faster adoption.
Traps: questions that assume the most sophisticated model yields the most value, ignore integration and change-management costs, or treat a pilot's accuracy as proof of business impact. Also watch for use cases where the cost of a false negative vastly exceeds a false positive (or vice versa) but the question ignores asymmetry.
Worked example: a support-deflection chatbot. Baseline: 1,000 tickets/week at $6 handling cost. Target: deflect 30% → 300 tickets saved → $1,800/week gross. Subtract model, integration, and escalation-review costs before claiming ROI. If escalation errors cost more than the savings, the case fails.
Rule of thumb: value = (baseline − post-AI performance) × volume × unit value − total cost of ownership.