Most organisations have run an AI pilot. Far fewer have an AI process — something that runs every day, under control, with a named owner and a measurable result. The gap is not the model. It is everything around it. This playbook sets out the steps, drawn from twenty years of running production analytics in banks, and shows where ATX Institute's products fit.
An experiment answers 'can a model do this?'. An AI process answers 'who is accountable when it runs at 6 a.m. tomorrow, what happens when it is wrong, and how much does it cost?'. AI process automation is the discipline of answering the second question.
Good first candidates are frequent, text-heavy and currently manual: triaging incoming requests, answering internal questions from policy documents, classifying customer feedback, preparing a recurring report. Avoid starting with a process where a single error is catastrophic and there is no human check.
Break the process into steps: trigger, data retrieval, AI reasoning, validation, human decision, action, record. The model is one node in that chain. In ATX Agent Hub each step is a node on a visual canvas; in ATX AnalyticsTable the equivalent for data is a pipeline with a scheduler.
Add human-in-the-loop approval at every consequential action, and guardrails — intent checks, fact checks, domain constraints — on every output that leaves the organisation. Automation that removes the check-and-approve step entirely is rarely what a regulator, or a customer, will accept.
Treat an AI workflow like code. Keep immutable versions, run a test suite against known cases before release, and require an approver who did not build the change. ATX AnalyticsTable keeps its configuration in Git for the same reason.
Run on a schedule or on events, keep a log of every run, and watch token cost per process. A process that silently doubles its cost after a model change is a process out of control.
Measure the thing the business cares about — hours saved, time to answer, error rate — not model accuracy in isolation. Once one AI process is running under control, the next ones reuse the same knowledge bases, credentials, roles and review path, and get much cheaper.
Automating business processes in which AI performs part of the work — reading, classifying, drafting, deciding what to do next — inside a controlled workflow with triggers, approvals, logging and ownership.
Robotic process automation follows fixed rules on structured screens and data. AI automation handles unstructured input — text, documents, conversations — and variation, which is why it needs guardrails and human review that RPA did not.
ATX Agent Hub for AI agent workflows with human approval and governance, and ATX AnalyticsTable for AI-driven analytics pipelines and unattended scheduled reporting.
This site needs JavaScript for the full experience. Every page above is reachable without it.