Overengineering with Generative AI: When Less Is More

Generative artificial intelligence, popularised by models such as GPT, has transformed many industries through its ability to produce new content and adapt to complex contexts.

But choosing the most advanced technology is not always the smartest decision. This is known as overengineering: using overly complex technology to solve problems that could be addressed more simply and efficiently.

Common examples of overengineering with generative AI

Process automation

A common mistake is to use generative AI to automate repetitive, highly predictable tasks, such as data entry or invoice processing. Robotic Process Automation (RPA) is a much better fit for these tasks.

• RPA carries out mechanical, repetitive tasks by following clear, predefined rules. It is well suited to structured, high-volume processes, offering speed, efficiency and lower costs.

• Generative AI is designed to interpret unstructured information and adapt to changing contexts. Using it for simple mechanical tasks adds complexity and unnecessary costs.

For example, using an advanced generative model to extract data from an invoice and transfer it to an accounting system is a clear case of overengineering. A combined approach makes more sense: generative AI interprets an unstructured email, then RPA carries out the specific administrative task.

Data classification

Another common example is using advanced generative models such as GPT for basic classification tasks, such as sentiment analysis or support ticket categorisation.

• Simple supervised models, including logistic regression, support vector machines (SVMs) and basic neural networks, are specifically optimised to classify data quickly, accurately and at low cost.

• Generative models such as GPT excel at producing new, original content rather than basic classification. Using them for classification can lead to unexpected errors (hallucinations) and higher computing costs.

For simple positive/negative classification, a basic supervised model will almost always deliver better results.

Other common mistakes related to overengineering

• Assuming generative AI can do everything: Implementing it without a critical assessment can create more problems than it solves.

• Losing sight of business goals: Adopting new technologies without a clear strategy leads to underused or inefficient solutions.

• Underestimating the risks: Generative models can introduce bias or incorrect information, which is especially problematic where accuracy is essential. They can also raise data privacy concerns.

Real innovation means making intelligent use of the right technology, which is not necessarily the most advanced or popular. Before choosing a technology solution, ask yourself: do I really need this, or would a simpler, effective solution be enough?