When to Use AI

In the world of technology, developers and engineers are constantly seeking the most suitable solutions to address a wide array of problems.

Traditional Code: Precision in Well-Defined Problems

Traditional code, involves writing explicit instructions that a computer executes in a precise and predictable manner. This approach is well-suited for addressing precisely defined problems with clear and specific solutions. When the problem has a fixed set of rules and a deterministic outcome, traditional code is highly effective and efficient.

Consider scenarios where calculations, data processing, or straightforward logic operations are involved. For instance, a basic calculator application performs specific arithmetic operations based on user input. The rules for addition, subtraction, multiplication, and division are well-defined, making it unnecessary to rely on AI for such tasks.

Another example where traditional code shines is in algorithms that sort data or search for specific elements within a dataset. These algorithms follow clear steps and have a deterministic outcome, making them ideal candidates for traditional coding approaches.

The Rise of AI: Fuzzy Solutions for Fuzzy Problems

While traditional code excels in dealing with precise and deterministic problems, the real world often presents challenges that are complex, dynamic, and ambiguous. In such cases, AI, which relies on machine learning and statistical techniques, emerges as a powerful alternative.

AI is particularly adept at handling “fuzzy” problems—those that lack well-defined rules or have multiple potential solutions. These may include natural language understanding, image and speech recognition, sentiment analysis, and pattern recognition, among others.

One of the key strengths of AI is its ability to learn from data and adapt its behavior based on patterns and examples. This makes it suitable for tasks where the rules might not be explicitly defined, or where they might evolve over time.

For example, consider an AI-powered chatbot designed to provide customer support. The variety and unpredictability of customer queries make it challenging to create a traditional code solution that covers all possible scenarios. AI, on the other hand, can be trained on historical customer interactions and learn to respond to new and diverse queries effectively.

Similarly, AI is widely used in image recognition applications. Training an AI model with thousands of labeled images allows it to recognize and categorize new images with a high degree of accuracy, even if they differ slightly from the training data.


The choice to use AI depends on the nature of the problem at hand. AI excels in addressing fuzzy problems that lack explicit rules and involve complex patterns. The world is full complex problems too broad for traditional code to tackle. Incidentally, the tasks of writing traditional code and AIs are fuzzy problems that, with enough time, AI will outperform humans in.