Enterprise Agentic AI Hampered by Missing Process Layer, Report Finds

A new report reveals that while enterprises are aggressively pursuing agentic AI, most are ill-equipped to support it due to a fundamental lack of optimized process infrastructure. The Celonis 2026 Process Optimization Report, based on a survey of over 1,600 global business leaders, highlights a stark reality: 85% of enterprises aim to become agentic within three years, yet 76% concede their current operations cannot handle such a transformation.

This ambition-readiness gap means that foundational work like modernizing workflows, reducing operational friction, and building resilience remains largely unfinished. Without this crucial process layer and the underlying process intelligence, AI agents operate blindly. An overwhelming 82% of decision-makers believe AI investments will fail to deliver return on investment (ROI) if they lack a clear understanding of business operations.

"The scale of the opportunity is truly remarkable: 89% of leaders see AI as their biggest competitive opportunity," stated Patrick Thompson, Celonis's global SVP of customer transformation. He emphasizes that the conversation has shifted from "will this work?" to "why isn't it working the way we need it to?" – a question that points to deep structural issues rather than technological feasibility.

The Structural Barrier to AI Efficacy

While 85% of teams already leverage generative AI for daily tasks, widespread multi-agent system adoption remains low, with only 19% of organizations currently using them. Thompson identifies this as an operational readiness problem. "Ambition without infrastructure doesn’t get you very far," he cautions, noting that nine out of ten leaders are exploring multi-agent systems.

Historically, messy and disconnected processes were often tolerated as long as businesses grew. However, AI's arrival fundamentally changes this calculus. If contextual understanding is paramount for AI ROI, then sub-optimal processes are no longer mere inconveniences; they actively impede AI strategy. This elevates process optimization from a background IT task to a critical prerequisite for competitive advantage in the AI era.

The Critical Role of Business Context

For AI to deliver its strongest ROI, it must possess a deep understanding of the business's operational context. This includes how key performance indicators (KPIs) are defined, internal policies, organizational structure, and where decision-making authority truly resides. Unfortunately, this vital knowledge is frequently fragmented across siloed departments, each with its own language and systems.

Process intelligence emerges as the essential connective tissue in this environment. It establishes a shared operational language, grounding AI decisions in the actual mechanics of how the business runs. Without this unified view, AI agents are akin to outsiders dropped into complex, ongoing conversations without any prior context.

Beyond Technology: A Change Management Imperative

Many leaders perceive AI adoption as primarily a technology problem, a view that Thompson and the report challenge. The data indicates that resistance to change (6%) is far less of a hurdle than siloed teams (54%) and a lack of departmental coordination (44%). Moreover, 93% of process and operations leaders acknowledge that process optimization involves people and culture as much as tools.

"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 explains. He asserts that AI cannot simply be bolted onto broken processes; true enterprise modernization demands redesigning how teams, systems, and decisions interact, with AI only becoming effective once this modernization is complete.

Process Optimization as a Strategic Lever

To transform process optimization from a routine operational task into a strategic advantage, organizations must link it directly to executive-level outcomes. Efficient processes extend beyond IT metrics, influencing board-level concerns such as risk management (cited by 63% of leaders) and faster decision-making (58%).

The current economic and geopolitical climate further underscores the importance of agility. In sectors like supply chain, 66% already recognize process optimization as a critical business-wide initiative. "It’s not maintenance work. It’s what lets you move fast when the world changes, and right now the world is moving constantly," Thompson emphasizes, urging a broader adoption of this mindset.

Closing the Readiness Gap for Agentic AI

To truly succeed with agentic AI, organizations must honestly assess their starting point and proactively close the operational readiness gap. Thompson warns against layering AI atop fragmented processes and then questioning the lack of results. He stresses that the foundational shift involves moving from static tools to real process intelligence, offering live visibility into operations.

Without this visibility, AI agents risk being deployed inappropriately, failing to integrate with existing systems, and resulting in expensive, non-scalable pilot projects. The clear call to action is to prioritize operational visibility and process intelligence over a tool-first approach.

"The leaders who will win in the agentic era aren’t necessarily the ones with the most sophisticated AI," Thompson concludes. "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."

FAQ

Q: What is "agentic AI" and why is it important for enterprises?

A: Agentic AI refers to autonomous artificial intelligence systems designed to perform complex tasks by understanding context, making decisions, and interacting with various systems. It's important for enterprises because it promises to transform operations, drive efficiency, and offer a significant competitive advantage by automating and optimizing intricate business processes.

Q: What is the primary challenge enterprises face in implementing agentic AI?

A: The primary challenge is a lack of operational readiness, specifically the absence of a robust, optimized process layer. Many enterprises have fragmented, opaque, and siloed processes, as well as disconnected systems, which prevent AI agents from gaining the necessary business context to operate effectively and deliver substantial ROI.

Q: How does process intelligence help bridge the gap between AI ambition and operational reality?

A: Process intelligence acts as a connective layer, providing AI with a shared operational language and real-time visibility into how the business actually runs. By mapping and understanding existing workflows, KPIs, policies, and departmental interactions, process intelligence grounds AI decisions in concrete business context, preventing agents from making uninformed guesses and enabling them to integrate seamlessly into enterprise environments.