TL;DR for operators Most organizations do not have a compliance problem because nobody wrote the rules down. They have a compliance problem because the rules exist in prose, the operational evidence exists in messy records, and the bridge between the two is usually a small group of overworked experts quietly aging in a meeting room. ...
Opening — Why this matters now The current fashion in enterprise AI is to give agents more tools, more context, and more freedom. The assumption is charmingly simple: if the model can reason, retrieve, plan, and call APIs, then the organization becomes more adaptive. Add a dashboard, call it orchestration, and wait for productivity to bloom like a suspiciously well-funded greenhouse. ...
A procurement workflow looks boring until an AI agent touches it. Before that moment, the process is usually wrapped in the comforting machinery of enterprise software: approval rules, validation checks, role permissions, exception paths, and enough audit trails to make everyone feel governed. Then someone inserts an agent and asks it to “handle the workflow.” The agent may know the words. It may call the right tools. It may even produce the next step that looks plausible. ...
Back office teams rarely suffer from a shortage of opinions. They suffer from a shortage of completed cases. A bank wants to know whether a loan application will require costly rework. A hospital wants to know whether an emergency-department case will need laboratory processing. An operations manager wants to know how long a running case will take before it becomes tomorrow’s apology email. Predictive Process Monitoring, or PPM, is supposed to help with exactly this kind of question. It looks at event logs and predicts what will happen next: total completion time, future activities, process outcomes, delays, exceptions. ...
TL;DR for operators A recent case study from If P&C Insurance is useful because it does something most AI automation stories conveniently skip: it follows the work after the model is deployed.1 The company used an LLM to identify specialised claim parts in insurance claims, a task that had depended on human claim handlers and specialist knowledge. In offline evaluation, the fifth model iteration built around GPT-4o-0806 reached 81% recall in English, above the company’s 70% human baseline. That sounds like the usual “AI beats humans” headline. Mercifully, the paper is more interesting than that. ...