Map how AI will change my job, task by task
Breaks your role into tasks and shows which are likely to be automated, augmented by AI, commoditized, or stay human-led.
The prompt
Help me see how AI is likely to change the work I actually do.
My job title and level: [TITLE - e.g. "senior financial analyst"]
Industry and company size: [DETAILS]
What I do in a typical week, with rough hours per task: [LIST TASKS - e.g. "build monthly variance report, 6 hrs"]
Tools I use today, including any AI tools: [TOOLS]
What my manager and stakeholders value most from me: [OUTCOMES]
Steps:
1. Turn my list into 10-20 distinct tasks. Ask me up to 3 questions first if my list is too vague to judge.
2. Classify each task:
- Likely automated - AI or software could do it end to end with light checking.
- Augmented - AI drafts or speeds it up; I still shape, check, and own it.
- Commoditized - still done by people, but no longer a differentiator because anyone with AI can do it adequately.
- Human-led - depends on judgment, relationships, accountability, or context AI does not have.
3. For each, explain the reason in one line and how soon the change is plausible (now / 1-2 years / 3+ years).
Return:
1. A table: Task | Hours per week | Category | Why | Timing | What I should do about it.
2. The share of my current hours in each category.
3. The 3 tasks where the shift will hit my role hardest, and the 3 where I am most protected.
4. Signals to watch that would tell me the timing is speeding up.
Rules: judge the specific task, not the job title. Be honest rather than reassuring, but do not predict layoffs or make claims about my employer's plans. Where you are guessing about tool capabilities, say so; these change quickly.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Career & Job Search prompts
What to fill in
- TITLE - e.g. "senior financial analyst"
- DETAILS
- LIST TASKS - e.g. "build monthly variance report, 6 hrs"
- TOOLS
- OUTCOMES
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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