Presented by Celonis
85% of enterprises want to become agents within three years – yet 76% admit their operations can’t support it. Organizations are aggressively pursuing AI-driven transformation, according to the Celonis 2026 Process Optimization Report, based on a survey of more than 1,600 global business leaders. Yet most acknowledge that the fundamental work – modernizing workflows, reducing process friction, and building operational resiliency – remains incomplete. The ambition is clear. There is no infrastructure to implement this.
To function autonomously and effectively, AI agents need customized, AI-ready processes, and the process data and operational context that only comes from process intelligence. Without it, they’re guessing. And 82% of decision-makers believe that AI will fail to deliver return on investment (ROI) if it does not understand how a business runs.
"The scale of the opportunity is truly remarkable: 89% of leaders see AI as their biggest competitive opportunity," Says Patrick Thompson, global SVP of customer transformation. "This is not a marginal discovery. What’s interesting is the change in framing. Leaders are confident that AI will transform operations. The question now is how to realize their ambitions with the right AI enablers."
Explaining the difference between ambition and reality
Right now, 85% of teams are using general AI tools for everyday tasks, so the question “will this work?” The question has been settled to a great extent. The real question boils down to: “Why isn’t it working the way we need it to?” And this is a very difficult problem, because it is structural. These are silent teams. Systems that don’t talk to each other. AI that looks impressive in demos but falters when it comes to real enterprise environments. Companies are hitting this wall.
Therefore, despite huge ambitions, only 19% of organizations use multi-agent systems today. Thompson says it all comes down to an operational readiness problem.
"Nine out of ten leaders are already using or exploring multi-agent systems, so the will is absolutely there, but ambition without the infrastructure doesn’t get you very far," He explains.
Until now, process has largely been a “good enough” problem, because processes that are messy and disconnected can still produce results, just inefficient and opaque. As long as the business has been growing, there hasn’t been a strong desire to fix them. AI changed calculus. If 82% of leaders believe AI can only deliver ROI with the proper business context, then sub-optimal processes are not just an operational inconvenience, they are actively blocking AI strategy. Suddenly, process optimization is no longer a background IT project, but a prerequisite for competitiveness.
"This is where structural modernization becomes important," He says. "Organizations that have invested in modernizing their data, systems and processes are in a far stronger position to enable AI at scale."
Another AI Stopper: Lack of Business Context
AI will not be able to deliver the strongest ROI unless it understands the operational context of the business. This includes how KPIs are defined and calculated, any unique internal policies and procedures, how the organization is structured, and where the real decision authority sits.
This knowledge is usually trapped in different departments that have developed their own languages and systems over time. They naturally do not share a common understanding. Bringing AI into that environment is a bit like engaging someone in a conversation that’s been going on for years without any backstory.
Process intelligence becomes the connective layer – a shared operational language that bases AI decisions on how the business actually runs.
Why is AI adoption also a change management problem?
The challenge of AI adoption is less a technology problem and more of a change-management and operating-model problem, which many leaders want to accept because technology problems seem easier to solve. Data shows that only 6% of leaders cite resistance to change as a barrier. The real blockers are siled teams (54%) and lack of coordination between departments (44%). And 93% of process and operations leaders clearly say that process optimization is as much about people and culture as it is about tools and technology.
"When companies come to us looking for a technology improvement, part of our job is to help them see that the operating model must evolve along with the tooling," Thompson says. "You can’t apply AI to a broken process and expect it to work. True enterprise modernization means redesigning the way teams, systems, and decisions connect, and AI only works if that modernization happens first."
Making process optimization a strategic advantage
How do you make process optimization a strategic advantage rather than just another operational project? Connect it directly to the results that executives care about. When processes work, they transcend IT metrics, directly impacting board-level concerns. A total of 63% of leaders use process optimization to proactively manage risks, while 58% see faster decision-making processes.
Additionally, the current economic and geopolitical climate makes agility a survival skill. Look at the supply chain industry, where 66% already view process optimization as an important business-wide initiative.
"That’s the mindset change we’re trying to catalyze in the rest of the organization," Thompson says. "This is not maintenance work. This is what allows you to move forward when the world changes, and right now the world is constantly moving forward."
Closing the readiness gap in enterprise agentic AI
To succeed and even win, organizations must be willing to bridge the readiness gap, Thompson says, and they have to be honest about where they are starting from.
"The biggest risk I see is that companies continue to pile AI on top of fragmented, opaque processes and then wonder why they aren’t getting results," He says. "Moving from static, legacy tools to true process intelligence, where you get live visibility into how your operations actually run, is the fundamental shift that makes agentic AI viable."
Without it, agents get deployed in the wrong places, they can’t be integrated with existing systems, and organizations end up with expensive pilots that don’t scale. The call to action is clear: stop starting with devices and start with operational visibility.
"The leaders who will win in the agent era will not necessarily be those with the most sophisticated AI," He says. "They are the ones who have worked hard to create a shared, accurate picture of their operations. Process intelligence is the starting point. While this enables enterprise modernization in practice, AI requires operational clarity to deliver real ROI. Master your processes, give AI the context it needs, and then you can actually deploy it somewhere where it will deliver."
Sponsored articles are content produced by a company that is either paying for the post or that has a business relationship with VentureBeat, and they are always clearly marked. Contact for more information sales@venturebeat.com.
<a href