Introducing AI in business has become a hot topic in recent years, with companies of all sizes jumping on the bandwagon to leverage its capabilities. However, in the midst of all the hype, there is a critical question that often goes unanswered – can agentic AI truly scale in production?
To answer this question, we first need to understand what agentic AI is. Also known as strong AI, it refers to the development of machines that can think and act like humans. These AI systems are capable of making decisions and taking action without any human intervention. This type of AI is being hailed as the future of business, with promises of increased efficiency, productivity, and profitability.
But here’s the problem – the focus on developing agentic AI models is creating a trap for businesses. Many are putting all their resources into building one perfect AI model that can do everything, neglecting the fact that AI is not a one-size-fits-all solution. This approach often leads to a lack of scalability, where the AI model can only perform a limited set of tasks and becomes difficult to maintain and adapt to changing needs.
There are three main reasons why the one-model trap is not a sustainable approach for businesses:
1. Complexity
Developing an agentic AI model is a complex and time-consuming process that requires a significant investment of resources. It involves training the model on vast amounts of data, tweaking and fine-tuning it, and ensuring it can handle all possible scenarios. This complexity makes it difficult to scale the model and incorporate new data or features.
2. Lack of Flexibility
Agentic AI models are designed to perform a specific task or set of tasks, which means they lack the flexibility to adapt to new tasks or data. This limitation can lead to the need for constant retraining and rebuilding of the model, which is not only time-consuming but also expensive.
3. Human Oversight
Despite its name, agentic AI still requires some level of human oversight to function effectively. This means that