When Does AI Make Sense? A Practical Framework for Product Teams

As generative AI becomes increasingly prevalent, many project managers feel the pressure to integrate AI into every digital product. But not every use case calls for machine learning (ML) or large language models (LLMs). Understanding when to use these technologies requires more than enthusiasm—it needs strategic evaluation.

Inputs, Outputs, and Patterns Matter The core of deciding whether to use ML lies in examining the input-output structure of a customer's needs. If users provide consistent inputs and expect predictable outputs—like auto-filling email addresses—rule-based systems often suffice. However, if the same input should yield varying outputs (e.g., generating new artwork with each click), ML becomes a more natural fit. The complexity and scalability of input-output combinations also guide the decision: more combinations often mean a stronger case for ML, but only if identifiable patterns exist.

Balancing Cost and Precision LLMs are powerful but can be overkill for many situations. They're costly at scale and sometimes lack precision, despite fine-tuning. For tasks like essay grading or sentiment analysis, simpler classifiers or topic modeling approaches can be more efficient and accurate. When precision is paramount and patterns are present, supervised models often outperform LLMs in both effectiveness and cost-efficiency.

When LLMs Truly Shine LLMs and generative models are best suited for complex, non-repetitive tasks with unpredictable outputs—think customer support, search, or summarizing nuanced reviews. These are scenarios where traditional rules or classifiers fail to scale due to the sheer diversity of input and required contextual understanding.

Conclusion Use AI like a tool, not a trend. The goal isn’t to force ML or LLMs into every product but to match the solution to the complexity of the need. Evaluate inputs, output diversity, scalability, cost, and precision. A well-chosen simple model often outperforms a complex one when applied thoughtfully.