Plan what to hand to AI without losing my own skills
Sorts your tasks into ones to delegate to AI, ones to keep doing yourself for mastery, and ones to do with AI as a coach.
The prompt
Help me make a deliberate plan for what I delegate to AI so I save time without weakening skills I need.
My role and career goal: [e.g. "financial analyst aiming for FP&A manager"]
Tasks I do regularly: [LIST]
Tasks I already use AI for: [LIST]
Skills that matter most for my next step: [e.g. "modeling", "writing for executives", "negotiation"]
Skills I feel are getting rusty: [LIST]
For each task, decide:
- Delegate to AI: low learning value, easy to check, low risk if wrong.
- Do it myself: builds core judgment or expertise I'm accountable for, or I'm still learning it.
- Do it with AI as a coach: I do the work, then use AI to critique, explain, or test me.
- Alternate: delegate usually, but do it myself every few cycles to stay sharp.
Return:
1. A table: | Task | Decision | Skill it builds or risks | How I'll check AI output |
2. Mastery list: the 3-5 tasks I should keep doing by hand, and why each matters for my goal.
3. Skill-erosion warnings: places where heavy AI use could leave me unable to spot errors or do the work in a crunch.
4. Practice routine: a light monthly habit to keep key skills sharp (e.g. "draft one analysis without AI, then compare").
5. Rules for delegating: 5 personal rules, such as "never send AI output I couldn't have written myself" or "always check numbers against source".
Be honest about trade-offs. If a task is both important and something I'm weak at, recommend practicing it rather than outsourcing it entirely.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Working with AI prompts
What to fill in
- e.g. "financial analyst aiming for FP&A manager"
- LIST
- e.g. "modeling", "writing for executives", "negotiation"
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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