Why Context Factor Matters For AI Agents In Business

Enterprises are running into a wall that has little to do with model quality: AI systems know everything about the internet and still struggle to support the processes that pay the bills. AI Business reported this week on what the trade calls the context factor, based on an interview with Celonis CEO and co-founder Alex Rinke on the Targeting AI podcast. His argument, and the thesis of the whole piece, is that the knowledge of how your company runs is the asset frontier models do not ship with.

What is the context factor for AI agents?

The context factor is the difference between what a model learned from public data and what it would need to know to execute inside one specific company. As AI models from providers like Google, OpenAI and Anthropic get more powerful, the assumption is that they will get better at running business processes, although Rinke told AI Business that this is not how it plays out. Many enterprises find that while these models are good at trip planning, they are not the best at automating core business tasks.

The missing piece is operational: how orders flow, where approvals stall, which systems hold which truth. The context factor is the gap between a model’s general competence and your company’s specific reality, and it is where automation budgets go to die.

Do frontier models actually automate core business tasks?

Only the parts that resemble public knowledge, which is why the trip-planning example lands. Rinke’s framing is direct: “AI knows a lot about London and a lot about tourist attractions, but it doesn’t know how this particular customer operates the supply chain, doesn’t have that 360-degree context about everything that’s going on in that organization.” A model without that context produces plausible output that no process owner can trust without checking.

The same interview carries the cost of the gap: “Without the right context, without process intelligence, AI is not going to deliver a strong ROI, and you’re not going to be able to reinvent all of the work and the processes that happen in your company.” Frontier models automate what they can see, and the 360-degree view of a business is not in their training data.

What is the difference between process intelligence and process mining?

Process mining grew up as the diagnostic layer: it discovers how processes actually run from system logs and shows where the waste sits. Process intelligence extends that into a living operational picture that AI agents can act on, which is the distinction Celonis draws in its own explainer and the reason the category has become the context layer for agentic AI. The old way handed you a report, and the new way hands the process itself to the agent.

Rinke describes the mechanism as a digital twin: “The vendor does this by building a digital twin of an organization that captures its processes, enabling AI agents to understand how work is performed and make informed decisions.” Process mining told you what was broken, and process intelligence gives the agent the map it needs to fix it.

What does process intelligence require from your business?

It requires you to treat tacit workflow knowledge as an asset with an owner, a system of record and access rules, because the digital twin is only as good as the process data you feed it. Rinke is explicit about custody: the goal for Celonis is to help enterprises own their context and data, rather than surrendering that information to vendors that use it to train competing models or resell it to customers. That custody line matters more as agents multiply, and every rollout that touches operational data should answer it in writing.

The custody question is the one that separates serious deployments from demos, and it is the kind of story we chase daily on the wire. Process intelligence requires you to map, own and defend your process data, and vendors should earn access to it rather than assume it.

The dispatch board knows 14 technicians, 60 open jobs and every part bin in the yard. The new AI scheduling tool knows none of it, so it routes like a stranger giving directions over the phone. There is no 360-degree view of the day, and the technicians feel it before the customers do.

That scene repeats in every industry because the pattern is structural, and Rinke’s digital twin is the general fix: capture how the work runs, then let agents act on the map instead of on assumptions. The map is the moat, and whoever holds it decides how good the automation gets.

What should you do about AI context now?

Pick one core workflow that burns real hours, map it end to end, and store the map somewhere a system can read, because that artifact is the price of admission for any agent you buy next. Evaluate vendors on the custody terms first: who trains on your process data, who can resell it, and what happens to the twin when you leave. The Celonis platform is the reference implementation here, although the questions apply to every vendor in the category.

Budget the context work before the tooling, because Rinke’s own math says the ROI does not arrive without it. Map one process this quarter, hold the custody line, and let the agent prove itself on the workflow it can see.

Source: AI Business