Anyone can write a clever prompt. Few can build AI workflows that keep working a year later. The difference is design discipline — not model choice.

The 4-part workflow anatomy

Every durable workflow has four parts: input (where the data comes from), transformation (the AI step), review (human or rule-based), and output (the deliverable, ideally written somewhere persistent).

5 starter workflows worth cloning

  1. Inbox triage: incoming email → AI labels and drafts → you review and ship.
  2. Meeting → action items: Granola transcript → Claude extracts decisions/owners → posted to Linear / Asana.
  3. Weekly metric brief: sheet/data source → Claude summary → emailed Friday morning.
  4. Customer research synthesis: 10 interviews → themes + quotes → Notion doc.
  5. Personal CRM nudge: contact list → "who to reach out to this week" Sunday email.

The orchestration tools — pick one

Zapier (broad, easiest), Make (more powerful, mid difficulty), n8n (open-source, technical), Apple Shortcuts (personal-only, free), MCP (newest, growing fast). Don't try to learn them all at once — master one before adding another.

The 5 design rules that prevent decay

  • Document the workflow in a one-pager.
  • Version your prompts.
  • Add a logging step — always know what ran.
  • Force a human review on anything customer-facing.
  • Sunset workflows that aren't being used; they're maintenance debt.
Key takeaway: When you build AI workflows, optimize for survival — not cleverness. Boring, documented, reviewed beats brilliant and fragile every time.

The eval habit

For workflows you depend on, build a small eval set: 5–10 representative inputs with known-good outputs. Run it weekly. The day a model update silently breaks your workflow, you'll catch it before customers do.

Where to start

The Be Fluent AI portal includes ready-to-clone workflow templates. Pair with our workflow coaching and automation coaching guides.