Syndication Presented by Celonis
85% of enterprises want to become agentic within three years — yet 76% admit their operations can’t support it. According to the Celonis 2026 Process Optimization Report, based on a survey of more than 1,600 global business leaders, organizations are aggressively pursuing AI-driven transformation. Yet most acknowledge that the foundational work — modernizing workflows, reducing process friction, and building operational resilience — remains unfinished. The ambition is clear. The infrastructure to execute on it is not.
To act autonomously and effectively, AI agents need optimized, AI-ready processes and the process data and operational context that only comes from process intelligence. Without that, they’re guessing. And 82% of decision-makers believe AI will fail to deliver return on investment (ROI) if it doesn’t understand how the 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. “That’s not a marginal finding. What’s interesting is the shift in the framing. Leaders are confident that AI will transform operations. The question now is how to fuel their ambitions with the right AI enablers.”
Explaining the gap between ambition and reality
Right now, 85% of teams are using gen AI tools for everyday tasks, so the “will this work?” question is largely settled. The real question has shifted to: “Why isn’t it working the way we need it to?” And that’s a much harder problem, because it’s structural. It’s siloed teams. Systems that don’t talk to each other. AI that looks impressive in a demo but falters once it’s dropped into a real enterprise environment. That’s the wall companies are hitting.
So, despite the overwhelming ambition, only 19% of organizations use multi-agent systems today. It all comes down to an operational readiness problem, Thompson says.
“Nine in ten leaders are already using or exploring multi-agent systems, so the will is absolutely there, but ambition without 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 is growing, there hasn’t been a burning urge to fix them. AI changed the calculus. If 82% of leaders believe AI can only deliver ROI with proper business context, then sub-optimal processes aren’t just an operational inconvenience, they’re actively blocking an AI strategy. Suddenly, process optimization isn’t a background IT project, but a prerequisite for competing.
“This is where structural modernization becomes critical,” he says. “Organizations that have invested in modernizing their data, systems, and processes are in a far stronger position to enable AI at scale.”
The other AI stopper: Lack of business context
AI will not be able to provide the strongest ROI possible until it understands the operational context of the business. That 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 don’t naturally share a common understanding. Bringing AI into that environment is something like dropping someone into a conversation that’s been going on for years, without any of the backstory.
Process intelligence becomes the connective layer — a shared operational language that grounds AI decisions in how the business actually runs.
Why AI adoption is also a change management problem
The AI adoption challenge is less a technology problem and more of a change-management and operating-model problem than many more leaders want to admit, because technology problems feel easier to solve. The data shows that only 6% of leaders cite resistance to change as a hurdle. The real blockers are siloed teams (54%) and a lack of coordination between departments (44%). And 93% of process and operations leaders explicitly state 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 fix, part of our job is helping them see that the operating model has to evolve alongside the tooling,” Thompson says. “You can’t bolt AI onto a broken process and expect it to work. True enterprise modernization means redesigning how teams, systems, and decisions connect, and AI only works when that modernization happens first.”
Making process optimization a strategic advantage
How do you make process optimization a strategic advantage, rather than another operational project? Connect it directly to outcomes that executives care about. When processes work, they go beyond IT metrics, directly affecting board-level concerns. A full 63% of leaders use process optimization to proactively manage risks, while 58% see faster decision-making.
Plus, the economic and geopolitical environment right now makes agility a survival skill. Look at the supply chain industry, where 66% already view process optimization as a critical business-wide initiative.
“That’s the mindset shift we’re trying to catalyze across the rest of the organization,” Thompson says. “It’s not maintenance work. It’s what lets you move fast when the world changes, and right now the world is moving constantly.”
Closing the readiness gap in enterprise agentic AI
To succeed, and even triumph, organizations must be ready to close the readiness gap, and they need to be honest about where they’re starting from, Thompson says.
“The biggest risk I see is companies continuing to layer AI on top of fragmented, opaque processes and then wondering why they’re not getting results,” he says. “Moving from static, traditional tools to real process intelligence, where you have live visibility into how your operations actually run, that’s the foundational shift that makes agentic AI viable.”
Without it, agents get deployed in the wrong places, 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 tools and start with operational visibility.
“The leaders who will win in the agentic era aren’t necessarily the ones with the most sophisticated AI,” he says. “They’re the ones who’ve done the hard work of building a shared, accurate picture of their operations. Process intelligence is the starting point. It’s what enables enterprise modernization in practice, creating the operational clarity AI needs to deliver real ROI. Master your processes, give AI the context it needs, and then you can actually deploy it somewhere it will deliver.”
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