Enterprise AI ambitions run up against process reality, Celonis research finds
Despite widespread enthusiasm around agentic AI, most enterprises remain constrained by the fundamentals of how their businesses actually operate. New research from Celonis suggests that while leaders are betting big on autonomous AI-driven organisations, weak and fragmented processes are emerging as a major brake on real-world impact.
Findings from the 2026 Process Optimisation Report, based on a survey of more than 1,600 global business leaders, reveal a striking mismatch between aspiration and readiness. An overwhelming 85% of organisations say they want to become an “agentic enterprise” within the next three years. Yet at the same time, 76% concede that their existing processes are actively holding them back.
At the heart of the issue is operational context. While AI agents are increasingly capable of acting autonomously, they still need a deep understanding of how a business actually runs. According to the report, 82% of decision-makers believe AI will fail to deliver return on investment if it cannot interpret real operational workflows and constraints. In other words, without AI-ready processes and reliable process data, autonomy risks becoming automation without impact.
The survey highlights that ambition is not in short supply. Around 90% of organisations are already using, or actively exploring, multi-agent systems to automate complex decision-making. Nearly nine in ten leaders see AI as their single biggest competitive opportunity. However, turning that urgency into execution remains difficult.
Two barriers stand out. Almost half of respondents cited a lack of internal expertise as a major obstacle, while 45% said getting AI to understand business context remains a persistent challenge. Organisational silos further compound the problem: 58% of process and operations leaders admit their departments still do not work seamlessly together, limiting the end-to-end visibility that enterprise AI depends on.
Celonis argues that the missing link is Process Intelligence — the ability to map, analyse and understand how work flows across systems and teams in reality, not just in theory. Without this shared operational language, AI agents are left to operate in isolation, executing tasks without insight into downstream consequences or improvement opportunities.
Carsten Thoma, President and Board Director at Celonis, said many organisations are struggling to convert bold AI ambitions into measurable value. He noted that AI requires more than access to data to succeed at enterprise scale. “For AI to truly work for the enterprise, it needs operational context,” Thoma said, adding that process intelligence provides the grounding AI needs to understand how a business runs and how it can be improved.
The report concludes that enterprises hoping to move from experimentation to sustained AI impact must rethink their foundations. Rather than layering AI on top of broken or opaque processes, organisations need to optimise and connect those processes first. Only then, Celonis suggests, can agentic AI move beyond isolated automation and start delivering continuous, business-wide outcomes.