When Not to Use AI

Every week, a company approaches us wanting to implement AI. Sometimes it makes sense. Often, it does not. The pressure to adopt artificial intelligence has created a peculiar blindness: teams reach for machine learning when a spreadsheet would suffice, or build neural networks when a simple rules engine would outperform them.

This is not an argument against AI. It is an argument for clarity.

The Questions We Ask First

Before any technical discussion, we ask three questions:

Do you have the data? Machine learning requires substantial, clean, relevant data. Not data you hope to collect someday. Data you have now, in a usable format. Most companies overestimate their data readiness by an order of magnitude.

Is the problem actually ambiguous? AI excels at pattern recognition in complex, ambiguous situations. If your problem has clear rules that can be written down, you do not need a model to learn them. You need an engineer to implement them.

What is the cost of being wrong? AI systems produce probabilistic outputs. They are wrong sometimes. If being wrong 5% of the time is catastrophic, you need a different approach. If being wrong 5% of the time is fine, AI might help.

When Simpler Solutions Win

A financial services client came to us wanting to build an AI system to categorize transactions. They imagined deep learning, natural language processing, the works. We asked to see their current process. It turned out that 94% of transactions could be categorized with twelve simple rules based on merchant codes and amount ranges.

We built the rules engine in two weeks. It cost a fraction of what the AI system would have cost. It runs faster, requires no maintenance, and is 100% explainable to regulators. The remaining 6% of transactions go to human review, which they were doing anyway.

This is not a failure of ambition. It is success through clarity.

A Framework for Decision-Making

Consider AI when:

• The problem involves genuine pattern recognition in unstructured data
• You have thousands of examples to learn from
• The rules are too complex or numerous to write by hand
• Good-enough accuracy is acceptable
• You have the infrastructure to deploy and monitor models

Consider alternatives when:

• The logic can be expressed in rules
• You need 100% accuracy or full explainability
• Your data is limited, messy, or biased
• A lookup table or decision tree would work
• The problem is really a workflow problem, not a prediction problem

The Real Question

The goal is not to use AI. The goal is to solve the problem. AI is one tool among many. The best technologists we know are not the ones who reach for the most sophisticated solution. They are the ones who reach for the right solution.

Sometimes that is a transformer model. Sometimes it is a SQL query. Knowing the difference is the hard part.