The vision of AI-driven productivity, cost savings, and revenue isn’t materializing anywhere as fast as predicted. Eighty-eight percent of respondents to a 2025 McKinsey survey reported using AI regularly in at least one business function, but only 39% reported an enterprise-wide impact on earnings before interest and taxes (EBIT). As the stakes rise, CIOs face pressure from the C-suite and Board to demonstrate where, when, and how AI will deliver a material financial impact. The era of AI prototypes is over.
So, how can CIOs take their AI initiatives to the next level? The problem isn’t AI itself, it’s what AI is missing: The operational context of how a business actually operates, says Eugenio Cassiano, EVP Corporate Strategy and Innovation at Celonis. Without context AI has no foundation on which to reason correctly and act reliably. Many AI deployments are built on large language models (LLMs), but LLMs alone don’t drive real business outcomes. Without knowing how a business runs, AI can’t deliver meaningful results at enterprise scale.
Historically, context has been missing, because the key sources of operational intelligence weren’t built to work in concert, Cassiano says. Building true operational context requires three essential layers working together:
- Process data: How processes truly run
- Process definition: How they should run
- Enterprise ontologies: How an organization is structured
Connecting these three elements is required to understand how a company works now and how it could and should work in the future. It provides the context layer needed for enterprise AI to operate and scale effectively.
Context and orchestration form the essential framework
While operational context tells AI what’s happening, orchestration is how AI does something meaningful with that knowledge. Enterprises need a platform that delivers both, orchestrating agents, people, and applications across complex business processes, making cross-functional decisions possible.
It does this by building a living digital twin of an organization’s unique business context and drawing insights that reveal where AI can have the greatest impact.
“What makes process intelligence unique is this notion of mining: what agents, people, and applications are doing to capture the value that has been generated,” says Cassiano. “The impact from a business standpoint is generated by activating certain orchestrations.”
Map an operational blueprint into a digital twin
Despite the temptation to pick a single starting point and plow forward, Cassiano recommends creating a process map that spans your organization. Think of this map as an operational blueprint that correlates processes. “With Celonis, we say to map as many departments as possible — your supply chain, your finance, your HR — into a digital twin, because then you start to see the connections,” he says. “If you start too small you might see some quick wins, but possibly not the best ones. Once you have everything modeled it becomes easy to deploy complex AI scenarios.”
The more operational context the platform can create, the more likely AI can determine which processes need attention from the start. This is where CIOs can replace tacit knowledge and assumptions with data-driven strategies, he notes.
The bottom line
Organizations that build this foundation don’t just have better AI; they have proof of AI impact. In practice, this means attaching a direct financial value to every agentic action taken. Cassiano calls this an AI control tower — a single view of all AI initiatives identified by PI, the agentic opportunities they triggered, and the financial value generated as a result. It’s the runway CIOs need to not just get AI initiatives off the ground, but to deliver proven ROI on the projects that matter.
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