The author proposes a five-level model for integrating artificial intelligence into business processes, ranging from simple AI assistants to fully AI-native architectures. The framework aims to help companies evaluate their current automation maturity and determine subsequent steps for process optimization.
The initial levels involve using AI agents as personal assistants or integrating them directly into workflows via servers and APIs. While these methods can speed up tasks like request classification and information retrieval, they often face challenges regarding cost, reliability, and the need for constant human oversight.
Advanced levels focus on architectural optimization by replacing universal large language models with specialized tools for specific tasks, such as OCR for document processing or code for calculations. This approach aims to reduce costs and improve predictability by invoking powerful models only when necessary.
The final AI-native level involves designing entire business processes around AI capabilities, software automation, and human intervention. This requires a shift from simply replacing employees with agents to creating systems that manage end-to-end workflows, monitor performance, and handle exceptions.
The author emphasizes that not every process requires the highest level of automation. Success depends on measuring metrics like processing time, costs, and error rates, while maintaining the ability to revert to previous versions or manual control if quality declines.
