Will AI Replace My Job? Start With the Work, Not the Headline
By Shawn Geeman · 3 min read · Published · Updated
Your job title is a rough description of your work. It is not a prediction of your future. To understand how AI could affect your role, start with the tasks you actually do, the standards those tasks must meet, and who remains accountable when something goes wrong.
One job contains several kinds of work
A financial analyst may gather data, explain an unexpected result, challenge an assumption, and persuade a colleague to change a plan. Those tasks do not have the same technical requirements. Producing a plausible paragraph is different from establishing whether the paragraph is true or deciding what to do about it.
Separate your work into recurring activities. For each, record the input, the output, the time spent, and the consequence of an error. This turns a frightening question about a whole career into a set of questions you can investigate.
Read exposure as a comparison
The Replaceability Index assigns a relative exposure score from 0 to 100. Higher scores indicate greater estimated exposure to automation within the model. A score of 70 is not a 70% chance of redundancy, and it does not establish that exactly 70% of your working time can be automated.
Our occupation rankings pair those estimates with wages, employment, and projected growth from a BLS-based dataset. These are occupation-level context. Your employer, location, responsibilities, and ability to shape your role can produce a very different experience.
Capability is only part of the story
A demonstration that completes a task is not proof that the task can be delegated reliably. A real workflow also needs suitable data, privacy controls, integration, review, exception handling, and someone responsible for the result. Those requirements can change both the cost and the usefulness of automation.
Research on exposure, including Eloundou et al. (2023), examines potential effects on tasks. The OECD capability framework published in 2025 offers another reference point. Neither source directly predicts your employer will eliminate your position. Read the methodology and limitations before drawing that conclusion.
Run a small experiment before making a big decision
Choose a recurring task that uses no confidential data. Try an approved AI tool on a representative example. Compare the complete process against your normal approach, including setup, checking, correction, and handoff. Record time and errors rather than judging the first impressive output.
Use what you learn to decide where to build skill. If drafting becomes faster, the next useful responsibility may be editing, verification, coordination, or owning a decision. If a task remains difficult to automate, understand why: the answer may suggest a skill worth strengthening.
Your next move can be modest
Write down one task to test, one responsibility to deepen, and one conversation to have with your manager or a trusted peer. Review the evidence after a few weeks. A career pivot deserves more information than a single number.
The free assessment gives you a starting point. The career resilience playbook helps turn that starting point into a sequence of practical experiments.
Frequently asked questions
Can this tool tell me whether I will lose my job?
No. It estimates relative task exposure. It does not know your employer plans, performance, local market, or future AI capabilities.
Why can two people with the same title have different results?
Their actual responsibilities and task descriptions may differ. The generated assessment also involves model judgment; small score differences should not be treated as precise measurements.
How should I check whether a score is useful?
Read the task explanation, compare it with your work, investigate mismatches, and test a relevant task. A useful explanation should help you make a specific, verifiable decision.
Sources and methodology
The score is a relative index of task exposure from 0 to 100. It is not a percentage chance of losing a job or a forecast of layoffs. Individual tasks, employer choices, adoption, and regulation all matter.
The Index uses stored BLS-based occupation data. O*NET, OECD capability indicators, and task-exposure research are contextual references. The derivation of the stored occupation scores is not reproducibly documented in the published dataset.
The source snapshot does not record the wage-survey year or employment-projection window. Check the original BLS release before treating these figures as current. Editorial changes and export dates do not establish data freshness.
This essay reflects the evidence available when written; future outcomes remain uncertain.
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