When to Use AI: A Quick Reference Workflow

Artificial Intelligence isn't the end-all-be-all for every business challenge. Instead, AI is simply another tool in your toolkit; one that enables organizations to become far more efficient and streamlined when managing data.

Inspired by IBM’s video Knowing When Not to Use AI: AI Agents vs Rules vs ML, choosing the right approach comes down to matching your specific problem with the right tool. When evaluating how to solve challenges around data retrieval and analysis, decision-makers generally have four core methods at their disposal:

• Human Intelligence: Best for complex judgment calls involving ambiguous or incomplete data.
  

• Traditional Programming: Best for clearly defined, rule-based logic using structured data (like databases and spreadsheets).
  

• Machine Learning: Best for uncovering hidden patterns and making statistical predictions with structured data.
  

• Generative AI: Best for flexible reasoning, content creation, and analyzing unstructured data (like raw text, PDFs, and audio).

So, the fundamental question for leadership shouldn't be "Why aren't we using AI yet?", but rather "When is AI actually the right tool for the job?"

A Quick Note on AI Costs

While this workflow helps you choose the right approach for the task, it doesn't factor costs. Generative AI in particular can come with unexpected usage expenses depending on how you deploy it. To dive deeper into managing these costs, check out our companion post: Why AI Tokens Are So Expensive and the 7 Questions Companies Should Be Asking.

When to Use AI: A Quick Reference Workflow

This quick reference workflow will help you evaluate your options, select the right method for the problem at hand, and pinpoint exactly where AI fits into your operational strategy.

Download PDF version of the Workflow.

Note: This guide is provided for informational purposes only.