We have spent the last few years captivated by the potential of enterprise AI. We’ve seen the demos, felt the excitement, and talked the talk. But for the C-suite, the period of experimentation is rapidly reaching its expiration date. Boards are no longer asking what AI can do; they are asking what AI has done for the bottom line. It’s time for AI to walk the walk.
Yet for Enterprise AI to transition from delivering simple productivity gains to autonomous decision-making and action that drives transformational business outcomes, it needs more than data. It needs to understand how your business actually runs, and how to improve it. Without this understanding, AI agents cannot make a real impact, so companies struggle to see meaningful returns on their enterprise AI investments.
That’s why we’ve launched the Celonis Context Model to eliminate enterprise AI’s operational blind spots. And we’ve entered into a definitive agreement to acquire Ikigai Labs, which brings state-of-the-art enterprise Decision Intelligence and cutting-edge AI innovation to the Context Model.
Agents are only as smart as the context they have
AI models are brilliant reasoning engines, but they’re probabilistic. They predict based on patterns — and in the case of LLMs, those patterns are the ones they’ve learned from the public internet. They know generally what an invoice is and how a supply chain works. But when it comes to your business, they have major operational blind spots.
AI models don’t know about how your specific invoices are related to your shipping records because that data is proprietary, private, and fragmented across internal systems, applications, and devices. And without that deterministic foundation — the ground truth of your operational reality — no AI agent can be trusted to make reliable real-time decisions and take actions that effectively drive your business outcomes.
Context Model vs. Context Graph vs. World Model
Jaya Gupta and Ashu Garg from Foundation Capital called out context’s critical importance to AI in December, describing a “context graph.” This is a structured record of how decisions actually get made inside a specific enterprise, including the exceptions, overrides, and precedents that currently live in Slack threads and people’s heads.
At the same time, “world models” — simulators that understand the real world (either physical or social) — have gained significant attention within business and AI circles. These models build on the context graph concept by modeling the dynamics of a system. A world model learns how a business behaves under different conditions through prediction and simulation. It allows an AI system to estimate what is likely to happen next and evaluate alternative scenarios.
The context graph is grounded in recorded history. World models generalize beyond observed data to reason about possible futures. Each serves a distinct and complimentary role, but to move from describing how a business operates to actively supporting how it should operate, you need to combine them both into a “context model.”