Not everything needs machine learning. There, I said it. As someone who has both implemented ML solutions and watched companies waste millions on unnecessary AI projects, I feel obligated to share this heresy.
The tech world has become so enamored with machine learning that we’re trying to use it everywhere—like a child with a new hammer who suddenly discovers that everything looks like a nail. Sometimes a simple rule-based system, traditional statistics, or even (gasp!) a human decision-maker is actually the better tool for the job.
So how do you know when machine learning is truly the right approach versus expensive overkill? Let’s break it down with some real-world examples.
Machine learning shines when:
-
You have a pattern recognition problem too complex for simple rules. One retailer I worked with tried using basic if-then rules to detect fraudulent transactions. After creating hundreds of increasingly complex rules, they still had high false-positive rates. Switching to ML reduced false flags by 73% because the patterns were too subtle and multidimensional for manual rules.
-
Your environment constantly changes, requiring frequent model updates. A manufacturing client used ML to optimize their supply chain because seasonal patterns, market trends, and thousands of variables were constantly shifting. The system could continuously learn and adapt in ways a static model couldn’t.
-
You have vast amounts of historical data. An insurance company was sitting on 20 years of claims data—a perfect ML opportunity. Their predictive models now identify potentially fraudulent claims with remarkable accuracy because they’ve “seen” millions of examples.
Conversely, ML is probably overkill when:
-
Simple rules work perfectly fine. One startup spent six figures developing an ML system to route customer service tickets before realizing their tickets fell into just five clearly defined categories that could be handled with basic keyword matching.
-
You lack sufficient quality data. A healthcare provider wanted ML to predict patient no-shows but had only captured relevant data for six months. They would have been better served by implementing simple reminder systems and collecting better data before attempting ML.
-
The cost of mistakes is astronomical, and you need explainability. In highly regulated industries or life-or-death scenarios, being able to clearly explain exactly how decisions are made can be more important than marginal performance improvements.
The most successful companies I’ve worked with don’t ask “How can we use machine learning?” Instead, they ask, “What’s the simplest approach that solves our business problem?” Sometimes that’s ML, sometimes it’s basic automation, and sometimes it’s just a well-designed spreadsheet.
Remember: The goal isn’t to use the most sophisticated technology available—it’s to solve business problems in the most effective way. Sometimes a screwdriver really is better than a Swiss Army knife.