AI Workflows
AI Workflow Examples for Founders, PMs, Developers, and Teams
Concrete AI workflows you can adapt for research, coding, meetings, content, sales, support, and operations.
AI becomes useful when it is attached to a repeatable workflow. A workflow has inputs, steps, review points, and an output someone can use.
The examples below are intentionally practical. They work with ChatGPT, Claude, Perplexity, Cursor, Granola, Zapier, n8n, and similar tools.
Quick Verdict
Research Workflow
Use Perplexity to collect current sources, open the best sources manually, then use Claude or ChatGPT to synthesize a decision memo with evidence, open questions, and recommendation.
Coding Workflow
Start with a short spec, ask Cursor or Claude Code for implementation options, choose the narrowest plan, generate edits, review the diff, and run tests before shipping.
Meeting Workflow
Use Granola or Fathom to capture notes, summarize decisions, extract owners, draft follow-ups, and move tasks into Linear, Notion, HubSpot, or Slack.
Automation Workflow
Use Zapier, Make, or n8n to route form submissions, classify them with AI, draft responses, update records, and notify the right person for approval.
Direct Answers
What are good AI workflow examples?
Good examples include research to memo, meeting notes to action items, support ticket triage, PRD drafting, code review, sales call prep, and content repurposing.
How do I build an AI workflow?
Define the input, desired output, steps, tool responsibilities, human review point, and success measure before automating anything.
Which AI workflows save the most time?
Meeting follow-up, research synthesis, support triage, sales prep, code review, documentation, and content repurposing usually save time quickly.
