AI, explained
What is an AI workflow?
An AI workflow is ordinary process automation with a model doing the steps that used to require a human to read something and decide.
Most of the value businesses get from AI today comes from workflows rather than from chat. Nobody talks to them. They run when an email arrives, a form is submitted, a file lands, or a schedule fires, and they quietly remove a task that used to sit in someone's inbox.
The anatomy of one
- Trigger. An inbound email, a webhook, a new row, a nightly schedule.
- Model steps. Classify this message, extract these fields from this invoice, summarise this call, draft this reply in the house voice.
- Deterministic steps. Validate the extracted data, apply business rules, write to the CRM, escalate when confidence is low.
- Human checkpoint. Approval on anything that leaves the building or moves money.
Workflow or agent
The distinction is who chooses the path. In a workflow, you define the sequence in advance and the model fills in specific slots. In an agent, the model decides which tools to call and in what order to reach a goal. Workflows are more predictable, cheaper to run, easier to test, and easier to debug when something goes wrong. Agents handle genuine variety that you cannot enumerate ahead of time. The mistake made most often is reaching for an agent when the process is actually a fixed sequence with three branches, which is a workflow and should be built as one.
Concrete examples
Inbound enquiry handling: an email arrives, the model classifies intent and urgency, extracts the contact details, creates or updates a CRM record, drafts a reply in the right language, and routes it for approval. Document processing: an invoice or contract lands, the model extracts the fields, code validates them against the purchase order, matches go straight through and mismatches go to a person. Meeting follow-up: a transcript is produced, the model extracts decisions and actions, tasks are created against owners with dates. Reporting: overnight, the model reads the week's data and drafts a commentary that a human edits rather than writes.
What makes them succeed
Start with a process that is high volume, rule-shaped, and currently annoying. Instrument it so you can see the model's decision on every run. Keep a confidence threshold below which the item goes to a human rather than through. Measure the pass-through rate, and treat a workflow that needs human correction most of the time as unfinished rather than as a partial win. Most failed AI projects are not failed models, they are workflows that were never measured. If you want to find the ones worth building first, that is precisely what an AI audit maps.
Frequently asked questions
What is an AI workflow in business terms?
It is an automated business process, triggered by an event such as an inbound email or a new file, in which a language model performs the judgement steps like reading, classifying, extracting, and drafting, while conventional code handles routing, validation, and writing to systems. The sequence is defined in advance rather than chosen by the model.
What is the difference between an AI workflow and an AI agent?
A workflow follows a path you defined, with the model filling specific steps. An agent decides for itself which tools to use and in what order to reach a goal. Workflows are cheaper, more predictable, and easier to test. Agents suit genuine variety you cannot enumerate. Many processes labelled agentic are really workflows.
Which processes are best suited to AI workflows?
High-volume, repetitive processes where the input is unstructured text or documents and the decision follows recognisable rules. Inbound enquiry triage, invoice and contract data extraction, meeting follow-up, support ticket classification, and first-draft reporting are the usual early wins because volume is high and each item is individually low risk.
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