AI Workflows: How They Work and How to Build One
An AI workflow is an automated process with a defined sequence of steps, where one or more of those steps uses a language model. A support ticket arrives, a model classifies it and drafts a reply, a rule routes it by category, and a person approves the reply before it is sent. The path is fixed; the model does the parts that need language or judgment.
That makes AI workflows the most practical way most teams put AI into production today. They are predictable, testable and easy to explain to the people who have to trust them.
How an AI workflow works
An AI workflow is built from the same parts as any automation, plus AI steps:
- Trigger: a new record, email, form submission, webhook or schedule.
- Data steps: look up the customer, fetch the order, read the contract.
- AI steps: classify, extract fields, summarize, draft, translate or score.
- Rules: route by the AI's output, such as category, confidence or amount.
- Human steps: review and approve where a mistake would be costly.
- Actions: update the record, send the message, create the task.
AI workflows vs AI agents
In a workflow, the designer decides the steps and the model fills in specific ones. In an agent, the model decides the steps: it chooses which tools to call, in what order, until it thinks the goal is met.
| AI workflow | AI agent | |
|---|---|---|
| Who decides the path | The designer | The model |
| Predictability | High | Lower |
| Best for | Known, repeatable processes | Open-ended tasks with many possible paths |
| Testing | Step by step | By evaluating outcomes |
Start with a workflow. Move to an agent only when the number of possible paths is too large to design by hand.
AI workflows vs classic automation
Classic automation handles structured data and exact rules: if the amount is over $1,000, route to finance. AI workflows add the steps rules cannot handle: reading an unstructured email, extracting an invoice total from a PDF, deciding whether a message is a complaint.
Examples of AI workflows
- Ticket triage: classify incoming tickets, tag urgency, draft a first reply for an agent to approve.
- Invoice processing: extract vendor, amount and due date from PDFs, match to purchase orders, flag mismatches.
- Lead qualification: summarize a lead's website and form answers, score fit, assign to a rep.
- Contract review: pull renewal dates and unusual clauses into a record for legal.
- Weekly reports: turn a week of orders or tickets into a written summary for managers.
How to build an AI workflow in six steps
1. Pick one process
Choose something frequent where the slow part is reading or writing text. Ticket triage and document extraction are common first choices.
2. Map the steps
Write down the trigger, each step, and which ones need judgment. Only those become AI steps.
3. Connect the data
The model needs context: the customer record, the order history, the policy. Connect the systems where that data lives rather than pasting it into prompts.
4. Write focused prompts
Each AI step should do one job with a structured output, such as a category from a fixed list or a JSON object of extracted fields.
5. Add a human check
Put an approval step before anything customer-facing or financial. Remove it later for categories where the model is consistently right.
6. Log and measure
Record inputs, outputs and human corrections. The correction rate tells you where the workflow needs work.
Building AI workflows with Jet Admin
Jet Admin builds AI workflows on data you already have, from 200+ integrations including Postgres, Salesforce, Zendesk and REST APIs. You describe the workflow, and Jet generates the automation steps, the AI steps and the screens where people review and approve. Workflows and users are unlimited on every plan; granular permissions, SSO and audit logs are on the Business plan and above.
For tools that specialize in AI automation, see our comparison of AI workflow automation tools.
Frequently asked questions
What is an AI workflow?
A fixed sequence of automated steps where some steps use an AI model to classify, extract, summarize or generate text.
Is an AI workflow the same as an AI agent?
No. A workflow follows steps you define. An agent decides its own steps to reach a goal.
Do AI workflows need a developer?
Not usually. No-code tools can build them. A developer helps when a step needs custom code or an unusual API.
How do you keep AI workflows accurate?
Give each AI step one clear job, use structured outputs, add human review for risky steps and measure how often people correct the model.
Put AI into the process, not next to it
Start with Jet Admin for free and build your first AI workflow on the data you already have.