AI Process Automation
AI process automation is the use of AI models and agents to carry out steps of a business process that previously needed a person to read something, decide something or re-key something. It differs from rules-based automation, which follows a fixed script and stops at the first exception, in that the agent handles variation in the inputs and escalates what it cannot resolve.
In Detail
Where it applies. The processes with the clearest return are the ones where a person re-types data from one system into another, triages a queue of documents or messages, or applies a known policy to a stream of cases. Each has a history of completed work, which is what makes the agent provable.
How an agent is proven. Every agent is tested against historical executions of the same or similar workflow, so it is measured on work that has already run rather than on a description of it. The requirements come from those executions, which keeps the build anchored to what the process actually does. Agents are instrumented so the gains are measured before launch, and nothing goes live without your sign-off.
Where it sits. This is the Run stage of the AI Journey, after the foundations and the governance record are in place. The AI Diagnostic identifies which processes a department should automate first and costs the work.
Common Questions
Is this RPA?
No. Robotic process automation follows a script; an agent reads, decides and escalates.
Which processes first?
Those with a history of completed work and a clear return, which the AI Diagnostic ranks per department.
Who owns the agent?
You do. Intellectual property is assigned on payment.
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