AI Job Risk Calculator

How to use this AI job exposure calculator

What this calculator does

This calculator estimates how strongly artificial intelligence may reshape the workflow of a selected occupation. It starts from a six-part task profile, then adjusts the result for experience, salary, location, local work environment, and work mode. The score is an exposure estimate: it is meant to help you think about task change, augmentation, and the likely speed of adoption. It is not a forecast that your job will disappear, and it should not be used as a hiring, firing, compensation, or career-selection decision by itself. The underlying concept follows task-level labor research rather than treating an occupation as one indivisible activity. The O*NET occupational database documentation explains the type of skills, work activities, tasks, and context information commonly used for this kind of occupational analysis.

When to use it

Use the calculator when you want to compare how AI exposure may differ across occupations, test how a more remote or more on-site work arrangement changes a scenario, think about whether a role is primarily digital or physical, or frame a reskilling conversation around the tasks most likely to change. It is also useful for comparing a junior and senior version of the same occupation, because the model treats experience as a proxy for the shift from procedural execution toward judgment, coordination, and responsibility.

How to calculate

1. The calculator opens with a ready-to-use software developer demonstration profile and a validated Excel workbook that can be downloaded immediately.
2. Replace Your role, Experience level, Annual salary, Location, Work environment, and Work mode with your scenario. Results update live; there is no separate Calculate button.
3. Read the exposure score and level first, then compare the three timeline scenarios and the main driver/protective factor. Use the task composition chart and table to see which type of work dominates the selected role.
4. Select Download Excel to export the current validated inputs, results, and breakdown as a real XLSX workbook. Reset clears the demonstration and all current profile data; after Reset, Excel export stays disabled until a complete valid profile is entered again.

Input guide

Your role is required and must be one of the listed occupations. For example, choose “Software developer.” It determines the six task shares and the 2022 U.S. wage benchmark used by the salary signal. Choosing a more digital or structured occupation generally raises exposure; choosing a more physical or people-intensive occupation generally lowers it. A common mistake is treating the role score as a personal performance rating – it is not.

Experience level is required and accepts Junior / entry, Mid-level, or Senior / lead. “Mid-level” is the demonstration value. Junior work adds a modest exposure adjustment because it is modeled as more procedural, while senior work subtracts a modest adjustment because it is modeled as more judgment- and coordination-heavy. The choice is a scenario assumption; it cannot capture every workplace's actual division of responsibility.

Annual salary is required and must be a positive USD-equivalent amount from $1,000 through $2,000,000. The parser accepts plain digits, U.S. comma grouping, an optional leading dollar sign, and optional cents, such as “127000,” “127,000,” “$127,000,” or “$127,000.50.” Decimal-comma formats and scientific notation are rejected rather than silently reinterpreted. Salary only creates a small economic incentive signal by comparing the entered amount with the role's fixed 2022 U.S. occupational wage benchmark; it does not mean a higher-paid person is personally more replaceable.

Location is required. “United States” is the demonstration setting. Country choices apply only a small regional adoption scenario modifier, reflecting the general point that AI diffusion is geographically uneven. Work environment is also required and accepts Rural / low-tech area, Standard market, or Tech hub / major city. The demonstration uses Tech hub / major city. Higher-density technology environments receive a modest faster-adoption adjustment; rural/low-tech scenarios receive a modest slower-adoption adjustment. These are coarse scenario modifiers, not country or city rankings.

Work mode is required and accepts Remote, Hybrid, or On-site. “Hybrid” is the demonstration value. Remote work receives a modest upward exposure adjustment because more of the workflow is already digitally mediated; on-site work receives a modest downward adjustment where physical presence and unpredictable real-world interaction remain important. Do not confuse this with a productivity or job-quality judgment.

Output guide

Estimated AI exposure score is a percentage bounded away from 0% and 100% to avoid implying certainty. Higher values mean the modeled role has more digital, analytical, or repetitive work after scenario adjustments; lower values mean human, managerial, or physical work is more protective. Exposure level converts the score into Low, Moderate, High, or Very high. These are scenario bands, not probabilities of job loss.

Fast AI adoption, Expected AI adoption, and Slower AI adoption are year windows for meaningful workflow transformation, anchored to 2026 in this version. A nearer window means the model expects tools to affect a material share of workflow sooner; it does not mean the occupation disappears in that window. Main AI exposure driver is the strongest of digital/software, analytical/knowledge, and repetitive/structured work. Main protective factor is the strongest of human interaction, management/strategy, and physical/manual work.

The Task composition breakdown table contains Task dimension, Share, and Interpretation. Share is the role's modeled percentage of work in each of the six dimensions, and the bars use exactly the same values. The shares always sum to 100%. The chart is a composition comparison, not a risk probability chart.

Worked example

The startup example uses Software developer, Mid-level, $127,000 annual salary, United States, Tech hub / major city, and Hybrid work. The role profile is 40% digital/software, 32% analytical/knowledge, 12% repetitive/structured, 6% human interaction, 8% management/strategy, and 2% physical/manual. Applying the task weights and the small salary, location, environment, and work-mode modifiers produces an exposure score of 57.11%, which falls in the High band. The timeline windows are 2027 – 2029 for fast adoption, 2029 – 2031 for expected adoption, and 2031 – 2034 for slower adoption. Digital and software work is the main exposure driver, while human interaction is the largest protective factor in this profile.

Learn more

The ILO's 2025 refined global index of occupational GenAI exposure emphasizes that exposure is a gradient and that transformation is generally more likely than wholesale replacement. For regional differences, the OECD's 2024 report on the geography of generative AI examines how exposure and adoption vary across places. Salary benchmarks in this calculator are based on the structure of the BLS May 2022 national occupational wage estimates.