AI workflow automation

A workflow starts with a trigger, such as a new form submission, email or database record, then passes data through a series of steps. Traditional automation follows fixed rules; adding an AI step lets the workflow handle unstructured inputs, such as summarising a document, extracting fields from an invoice or deciding which team should handle a request. Many automation platforms now offer AI steps and agent features alongside their app connectors.

Why it matters: for many teams, workflow automation is the most practical way to put AI to work, because it fits into existing tools and keeps humans in control of each step. Compared with a fully autonomous agent, a defined workflow is easier to test, monitor and explain. When choosing a platform, compare its connectors, how AI steps are priced, error handling, data residency and whether it can be self-hosted.

Example: when a customer email arrives, a workflow uses an AI model to classify it as a refund, bug or sales enquiry, drafts a reply from help-centre articles and sends it to the right queue for approval.

Limits: AI steps can misclassify or misread inputs, so add confidence thresholds, fallbacks and human review for important decisions. Usage-based pricing can also climb quickly at high volumes.