Prioritizing AI Use Cases That Drive Revenue

In the world of AI, focus is a competitive advantage. With countless potential applications, the ability to identify and prioritize the right ones is what separates successful AI programs from a collection of interesting but low-value experiments. Effective prioritization ensures you deliver measurable outcomes quickly and avoid wasting precious engineering cycles.

A Simple Scoring Model

A powerful way to prioritize is to use a scoring model. For each potential use case, rate it across key dimensions like Impact and Effort. Impact can be a measure of potential revenue, cost savings, or user retention. Effort can represent data quality, integration complexity, and engineering hours required. A simple formula like Impact / Effort or a more nuanced one like (Impact × Confidence) / Effort can help you rank your options and surface the quick wins.

High-Value Use Case Examples

While every business is unique, some use cases consistently deliver high value across industries:

• Customer Churn Prediction: Identify at-risk customers and proactively engage them with targeted offers or support.
• Automated Claims Triage: In insurance, automatically route claims to the right department, significantly reducing processing time.
• Dynamic Recommendation Engines: Increase average order value and customer lifetime value by suggesting relevant products or services.

How to Operationalize Your Choices

Once you've prioritized, the key is to start small and prove value. Build a minimal viable product (MVP) for your top-ranked use case. Instrument key performance indicators (KPIs) from day one and measure the lift against a control group. This data-driven approach not only validates your choice but also builds the business case for scaling the solution. To accelerate future projects, maintain a single, centralized MLOps pipeline to reuse components and best practices across all your use cases.