Making the case for context-aware AI

Enterprise AI systems can perform tasks like generating recommendations, automating processes, and aiding decision-making.

However, these systems often lack operational context—an understanding of how a business actually functions in practice.

Without this context, even advanced AI models may produce recommendations that overlook critical constraints, such as workflow dependencies, resource limitations, or regulatory requirements.

For example, an AI might suggest a financial forecast without considering a company’s cash flow restrictions, leading to costly errors.

Devavrat Shah, chief scientist of enterprise AI at Celonis, emphasizes that AI agents must grasp the role of each task within broader business processes to avoid inefficiencies or mistakes.

This gap between AI capabilities and real-world business operations is why many organizations struggle to scale AI beyond experimental phases.