Where AI fits inside a workflow

The most reliable AI automation is not a robot that does everything. It is an ordinary workflow, with triggers, actions, and conditions, that hands the language-heavy steps to AI and keeps people in charge of the decisions that matter. This lesson shows how to design that combination.

Rules first, AI where it earns its place

Before adding AI to a step, ask: could a simple rule do this? Sending a reminder at a set time, copying an order into accounting, or assigning a lead by zip code are rule-based. They are cheaper, faster, and more predictable without AI.

AI belongs where the input is free-form language or varies too much for rules: emails written a hundred different ways, invoices in dozens of layouts, call recordings, open-ended questions.

The five jobs AI does in a workflow

Three worked examples

1. Inbox triage

1. Trigger: a new email arrives in the shared inbox.
2. AI step: classify it and return category, urgency, and a one-line summary as JSON.
3. Rules: route by category to the right person or tool; post urgent items to the team chat.
4. Fallback: anything the AI marks as unclear goes to the office manager.

2. Invoice capture

1. Trigger: a PDF arrives in the invoices mailbox.
2. AI step: extract vendor, invoice number, date, due date, and line items.
3. Rules: check the vendor exists and the total matches the line items; create a draft bill in accounting.
4. Human step: the bookkeeper approves drafts over a set amount or any that failed a check.

3. Support reply drafts

1. Trigger: a new support ticket.
2. Retrieve: find the most relevant help articles for the question.
3. AI step: draft a reply using only those articles.
4. Human step: an agent reviews, edits if needed, and sends. Over time, routine categories can be sent automatically once quality is proven.

Keeping AI steps safe

• Constrain the output: fixed categories and formats, so a strange answer is easy to detect
• Validate: check AI output with rules where possible (does the total add up, is the date valid, is the category allowed)
• Use thresholds: route low-confidence or invalid results to a person
• Approve what matters: human review for anything that commits money, reaches an upset customer, or has legal weight
• Log everything: keep the input, output, and any human edits, so you can spot patterns and improve the prompt

Start with drafts, earn automation

A practical path for any AI step: start with AI drafting and a person approving everything. Track how often the person changes the draft. When edits become rare for a category, let that category run automatically, and keep reviewing a sample. Trust is earned with evidence, not assumed.

The AI workflow automation guide covers more examples. In the next lesson, you will look at AI agents, which take this a step further.