The mistake people make with AI is bolting it onto a task randomly — or trying to fully automate something that needs judgment, then losing trust when it goes wrong. Real leverage comes from designing the workflow: deciding which steps AI does well, which need a human, and where the checkpoints are. This maps that for your recurring task, so you get the speed of AI with the reliability of human judgment where it matters — and you stay in control.
What This Skill Produces
- The step map — the task broken into steps, each labeled: 🤖 AI does it · 🧑 human does it · ✅ human checks it (AI drafts, human approves)
- The right tool/prompt per AI step — what to use and how to prompt it for each automated step
- Hand-offs & checkpoints — how outputs pass between steps and where the human review points are (so errors are caught, not propagated)
- Failure modes & guards — where this workflow could go wrong (AI errors, hallucination, edge cases) and the checks that catch them
- A start-small rollout — how to introduce it incrementally and build trust before relying on it
- The keep-human line — the steps that should stay human (judgment, relationships, high-stakes calls) and why
Required Inputs
Ask for these if not provided:
- The task/process — the recurring thing you want AI to help with
- The current steps — how you do it now, manually
- The stakes — how much errors cost (drives how many human checkpoints)
- Your tools — the AI tools/access you have
- Your comfort — how much you want to automate vs. keep hands-on
Framework: Split The Steps, Check The Seams
- Map the current steps. Lay out how the task is done now — you can't design the AI version without seeing the manual one.
- Sort each step. For each: is it something AI does reliably (drafting, summarizing, extracting, transforming), something needing human judgment (decisions, relationships, high-stakes), or something AI drafts and a human approves?
- Pick tools and prompts. For each AI step, the right tool and a reliable prompt (often from a prompt library) — so the step works consistently.
- Design the seams. Where outputs hand off between steps is where errors hide — add review checkpoints at the seams, especially before anything irreversible or external-facing.
- Guard the failure modes. Name where AI could err (wrong facts, edge cases, confident nonsense) and the specific check that catches it before it matters.
- Roll out small. Start with the low-risk steps, verify the quality, and expand — building trust rather than automating everything and hoping.
- Keep humans where it counts. Be clear which steps should stay human — judgment, empathy, and high-stakes calls aren't candidates for automation.
Output Format
AI workflow: [the task]
Step map
| Step | Who | Tool/prompt (if AI) |
|---|---|---|
| [step] | 🤖 AI / 🧑 human / ✅ AI-drafts-human-approves |
Checkpoints: [human review points — esp. before irreversible/external steps]. Failure modes & guards: [where AI could err → the check that catches it]. Keep human: [the judgment/relationship/high-stakes steps] — and why. Roll out: [start with low-risk steps → verify → expand].
Quality Checks
- Maps the current manual steps first
- Sorts each step into AI / human / AI-drafts-human-approves
- Assigns the right tool/prompt to each AI step
- Puts review checkpoints at the hand-off seams
- Names failure modes and specific guards
- Keeps human judgment steps human; rolls out incrementally
Anti-Patterns
- Bolting AI on randomly with no step analysis.
- Fully automating a task that needs judgment.
- No checkpoints at the seams — errors propagate.
- Ignoring failure modes until something breaks.
- Big-bang automation instead of a trust-building rollout.
Example Trigger Phrases
- "How do I use AI for my weekly reporting process?"
- "Design an AI-assisted workflow for handling customer emails."
- "Where does AI fit in my content process, and where shouldn't it?"
- "Help me automate part of this task with AI without losing control."
- "Map out an AI workflow for my recurring [task]."