Generic AI job risk lists assign a score to your job title based on average data and present it as if it applies to you personally. It does not. What applies to you is a function of your specific task mix, your employer's AI adoption posture, and the relational and judgment-intensive elements of your particular version of your role. A proper assessment measures all of those dimensions, not just the first.
The gap between average-risk and personal-risk is significant enough to matter for the decisions you make. A junior content writer at a media company actively deploying AI content tools is in a very different position than a content writer producing highly specialised technical documentation with a stable, relationship-dependent client base. Both carry the same generic title, but their actual threat levels are worlds apart. This page explains what a rigorous assessment covers and how to get one.
What the research says
92M
jobs displaced by 2030
WEF Future of Jobs 2025
170M
new roles created by 2030
WEF Future of Jobs 2025
41%
of employers plan AI-driven headcount reductions
WEF 2025
55K
job cuts explicitly attributed to AI in 2024
Challenger, Gray and Christmas
A proper assessment measures five dimensions: task routineness (what proportion of your time involves repeating the same category of task), output digitisability (whether your primary deliverables are digital and therefore directly replicable by AI), relational dependency (whether your value is tied to specific person-to-person trust), judgment intensity (whether your role regularly requires decisions in genuinely novel situations), and employer context (whether your specific employer is actively deploying AI in your function). Generic lists only measure the first dimension at the occupation level.
Generic lists assign a single risk score to a job title based on average task composition data from labour statistics surveys. They cannot account for how your specific version of the role differs from the average: your seniority, the clients you serve, the proportion of time you spend on each task, or your employer's specific AI adoption plans. Two people with the same job title at different employers in different industries can have risk profiles that are 50 percentage points apart.
You need four inputs: a task log covering approximately one to two weeks of your typical work, an estimate of the time proportion spent on each task category, your industry sector and the stage of AI adoption visible within it, and an honest reading of how your employer discusses and funds AI-related projects. With those inputs, it is possible to produce a risk assessment that reflects your actual position rather than a statistical average.
A skills gap analysis identifies which skills you lack relative to a target role or standard. An AI job threat assessment identifies how exposed your current role is to displacement before you get to the skills question. The threat assessment comes first: if your role is at low risk, a skills gap analysis tells you how to grow within it; if it is at high risk, the skills gap analysis needs to be oriented toward a different target role, not enhancement of the current one.
A high-risk result should trigger two parallel responses: an internal repositioning plan to move toward the protected elements of your current role, and an external market analysis to identify adjacent roles where your skills transfer and where the risk profile is lower. A moderate-risk result calls for deliberate strengthening of your relational and judgment-intensive responsibilities while monitoring your employer's AI adoption pace. A low-risk result is not a reason to stop monitoring; it is a baseline to revisit in 12 to 18 months.
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