The Category Everyone Suddenly Wants
In July 2026, a leading process mining vendor acquired Ikigai to give AI agents what it called “operational intelligence.” Weeks earlier, Bespin Global had put the argument plainly: AI competitiveness hinges on operations, not models. The market has arrived at something IGX360 was built on. Enterprise AI succeeds or stalls on the operating reality the model is placed into.
That is the right instinct, and it points at two layers that work best together.
Operational intelligence, as the category currently defines it, means process mining: the reconstruction of process reality from event logs. Mining is a genuine capability and answers a real question: what happened. AI agents need a second answer alongside it, which is why. Adding the why is what lets an agent act correctly.
What Process Mining Does Well
Process mining reconstructs how a process ran by reading the digital exhaust your systems already produce: timestamps, status changes, transaction records and the sequence of clicks across ERP and case-management platforms. It applies statistical inference to event logs and shows the process as it executed, in contrast to the procedure’s version.
That has real value. BPX reports that 60% of S/4HANA migrations stall without this visibility, because teams cannot migrate a process they cannot see. When the documented process and the executed process have diverged over a decade, mining exposes the gap. It is a strong first move for any organisation that wants to see how its work runs. You can see how we treat that visibility as an input in our related insights.
An event log contains records of actions. A log shows that an invoice over £50,000 was routed to a second approver. The threshold, the reason it exists and what happens to the invoice that arrives at £49,999 with a known-risk supplier attached are the pieces that articulation adds. Event logs capture the exceptions that were handled in the system, and articulation captures the ones resolved in a phone call, a message or a practitioner’s head.
Why AI Needs the Why
Hand an AI agent a mined process and it will replicate the observed behaviour, including the workarounds and shortcuts baked into the trace. The event log does not label the correct path against the habitual one, so the agent needs that label from somewhere.
To act correctly, an agent needs the decision logic: the rules, thresholds and criteria that govern the process. It needs to know that invoices over £50,000 go to a second approver, that the rule exists, what it protects against and which conditions override it. Mining shows the path that was taken. Articulation shows why that path was chosen, and when a different path applies.
This is the opportunity behind AI’s production success rate. Pilots that add decision logic to the observed behaviour carry the intent of the process into production, and judgement you have made explicit is judgement you can delegate. The way we make it explicit is set out in how we articulate processes.
Process Articulation: Human-Legible, Decision-Ready
Process articulation is the deliberate capture of how work is done and why, in a form both people and AI agents can act on. It is a structured, governed model of the operating reality, built to be read by a practitioner and executed by an agent without either losing the meaning.
IGX360’s canonical model makes this concrete. It uses 20 object types and 16 relationship types to encode the steps of a process and the decision logic that governs them: the criteria behind a branch, the conditions that trigger an exception, the authority required at a given point and the constraint that must always hold. Where mining infers a sequence, articulation states the rule. The decision becomes a first-class object, legible to a human reviewer and available to an orchestrating agent.
Mining tells you where to look and where reality has drifted from intent. Articulation is the point at which a process becomes AI-ready, migration-ready and governable, because the reasoning is explicit. It is the work process excellence teams have long done in their heads and documentation, given a structure AI can use.
If you want the mechanism before the argument, see how IGX360’s canonical model captures decision logic.
Explain It, Then Delegate It
The market is right that operations matter more than models. Operational intelligence gets stronger when decision logic is part of it, and a reconstruction of what happened combined with an explanation of why is what makes a process safe to hand to an agent.
The process teams who win with AI will be the ones who articulate the why alongside the what. When you can explain a decision, you are ready to delegate it.
Talk to IGX about adding decision logic to what your process mining shows.