Celonis Releases 'Process Optimization Report 2026'
Celonis published the '2026 Process Optimization Report'. It reveals a gap between high expectations for Agentic AI and current operational readiness, highlighting the critical need for Process Intelligence to provide business context.
📋 Article Processing Timeline
- 📰 Published: March 31, 2026 at 22:00
- 🔍 Collected: April 1, 2026 at 13:39 (15h 39m after Published)
- 🤖 AI Analyzed: April 21, 2026 at 11:41 (478h 2m after Collected)
Celonis, the global leader in process intelligence, today released the 'Process Optimization Report 2026', a survey report of over 1,600 business leaders worldwide. The survey reveals a significant gap between the high expectations companies hold for agentic AI and their actual operational readiness.
According to the report, many companies are aggressively working towards an AI-driven future, with 85% of organizations aiming to become autonomous enterprises within three years. However, 76% admit that their current business processes are holding them back.
For AI agents to function autonomously and effectively, they need processes optimized for AI usage, as well as the process data and business context that can only be obtained through process intelligence. Without both, AI agents can neither understand a company's actual operational workflows nor determine how to improve them. Furthermore, 82% of decision-makers believe that AI cannot generate Return on Investment (ROI) unless it understands the reality of the business operations.
Key Additional Findings
- High Expectations: 90% of organizations are already utilizing or considering the implementation of multi-agent systems to automate complex decision-making.
- Expertise and Context are the Biggest Barriers: The top two barriers to adoption are a lack of internal expertise (47%) and the difficulty of making AI understand business context (rules, KPIs, benchmarks, models, enterprise architecture, etc.) (45%).
- Silos Hinder Effective AI Adoption: 58% of process/operations leaders say cross-departmental collaboration is still not seamless, preventing the end-to-end visibility required for effective enterprise AI.
- Competitive Urgency: 89% of leaders say AI is the biggest opportunity to enhance competitiveness.
To bridge this gap between ideal and reality, companies must move beyond isolated automation initiatives. The findings indicate that it is essential for AI to be grounded in process intelligence to function while understanding the complex realities of corporate activities, rather than simply executing simple, discrete tasks. Process intelligence provides a 'common language' for AI agents to understand operational workflows across departments and systems, identify friction points, and execute actions that lead to true business outcomes.
Carsten Thoma, President and Board Member of Celonis, stated:
'While many business leaders are boldly steering towards the future of enterprise AI, the reality is they are struggling to connect that vision to tangible ROI. For AI to truly work in the enterprise, data alone is not enough. What is needed is context. By leveraging process intelligence and giving AI a shared understanding of how the business actually operates, we can finally move beyond the visionary stage and deliver tangible results.'
To learn more about the survey findings and how companies are building AI-ready operations, read the full Process Optimization Report 2026.
According to the report, many companies are aggressively working towards an AI-driven future, with 85% of organizations aiming to become autonomous enterprises within three years. However, 76% admit that their current business processes are holding them back.
For AI agents to function autonomously and effectively, they need processes optimized for AI usage, as well as the process data and business context that can only be obtained through process intelligence. Without both, AI agents can neither understand a company's actual operational workflows nor determine how to improve them. Furthermore, 82% of decision-makers believe that AI cannot generate Return on Investment (ROI) unless it understands the reality of the business operations.
Key Additional Findings
- High Expectations: 90% of organizations are already utilizing or considering the implementation of multi-agent systems to automate complex decision-making.
- Expertise and Context are the Biggest Barriers: The top two barriers to adoption are a lack of internal expertise (47%) and the difficulty of making AI understand business context (rules, KPIs, benchmarks, models, enterprise architecture, etc.) (45%).
- Silos Hinder Effective AI Adoption: 58% of process/operations leaders say cross-departmental collaboration is still not seamless, preventing the end-to-end visibility required for effective enterprise AI.
- Competitive Urgency: 89% of leaders say AI is the biggest opportunity to enhance competitiveness.
To bridge this gap between ideal and reality, companies must move beyond isolated automation initiatives. The findings indicate that it is essential for AI to be grounded in process intelligence to function while understanding the complex realities of corporate activities, rather than simply executing simple, discrete tasks. Process intelligence provides a 'common language' for AI agents to understand operational workflows across departments and systems, identify friction points, and execute actions that lead to true business outcomes.
Carsten Thoma, President and Board Member of Celonis, stated:
'While many business leaders are boldly steering towards the future of enterprise AI, the reality is they are struggling to connect that vision to tangible ROI. For AI to truly work in the enterprise, data alone is not enough. What is needed is context. By leveraging process intelligence and giving AI a shared understanding of how the business actually operates, we can finally move beyond the visionary stage and deliver tangible results.'
To learn more about the survey findings and how companies are building AI-ready operations, read the full Process Optimization Report 2026.