Celonis Thinks Enterprise AI Has a Context Problem

A commonly cited report by MIT's NANDA initiative, which studied over 300 deployments and surveyed 350 employees, found that 95% of enterprise AI pilots deliver zero measurable business impact. The same analysis cited S&P Global data showing 42% of companies scrapped most of their AI initiatives in 2025, up from 17% the year before.

Celonis, the process intelligence company, says it knows why this is happening. On May 12, the company launched the Celonis Context Model (CCM) and announced a definitive agreement to acquire Ikigai Labs, an AI decision intelligence company built on nearly two decades of MIT research. Together, the two moves mark Celonis' most significant repositioning since it became, by analyst measure, the market leader in process mining.

"AI is only as good as the context it has," says Carsten Thoma, Celonis President. "Every organization needs to give its Enterprise AI a holistic, living model of how a business truly operates."

Why AI Agents Fail Inside Real Companies

The argument Celonis makes is architectural. In its telling, enterprise AI agents fail because they lack an accurate, real-time understanding of how a business actually operates. ERP systems record transactions and large language models generate language, but neither captures how work flows between systems, why processes break, or what the exceptions are.