Three concrete diagnostic questions cut through the noise more effectively than any published job-title ranking. The questions focus on what your employer actually pays you for: your outputs, your relationships, and your judgment. Each question maps directly to the capabilities that current AI systems do and do not have, making the self-assessment grounded rather than speculative.
The honesty of your answers matters as much as the framework. Most people unconsciously overestimate how unique or complex their work is because familiarity makes tasks feel harder than they appear to an outside observer, or an AI system. Goldman Sachs estimates 300 million jobs globally are exposed to generative AI disruption, and many of those workers would say their work is too complex to automate. Working through these three questions carefully is the antidote to that bias.
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
The three questions are: (1) Could a capable AI system produce my primary outputs today if given my inputs and access to my tools? (2) Does my value to my employer depend on a specific person-to-person relationship that would be meaningfully lost if I were replaced by anyone, human or AI? (3) Does my role regularly require judgment in situations where the right answer is genuinely uncertain and where historical precedent is insufficient? Answering yes to question one and no to questions two and three indicates high replacement risk.
The diagnostic is only useful if you answer based on what AI can do today, not what you wish it could not do or what feels intellectually beneath you. Many people believe their work is more complex than it is because the complexity is familiar to them. The test is not whether your work feels hard to you; it is whether a capable AI system, given clear inputs and instructions, could produce outputs your employer would find equivalent. That is a more demanding standard than most people apply honestly.
That is a hybrid position, common in professional services. Your outputs may be replicable by AI, but your relationship with specific clients or stakeholders creates a retention factor that pure automation cannot replicate. The risk in this position is that your employer may decide to retain you at a reduced scope while using AI to expand the output volume, effectively changing the ratio of what you do rather than eliminating you outright. This is a moderate-risk position that rewards proactive relationship deepening.
Yes. If AI tools are already embedded in your workflow, the diagnostic question shifts from whether AI could do your job to whether the value you add above and beyond the AI output is visible and valued by your employer. If your primary role has become reviewing and lightly editing AI outputs, your employer may conclude that fewer reviewers are needed as the AI's accuracy improves. The relevant question becomes how irreplaceable your specific judgment and relationships are within that review function.
The Fossil Score assessment extends this diagnostic into a full task-level analysis, scoring your specific role across multiple exposure dimensions and returning a personalised risk rating with specific guidance. It takes under five minutes and requires no account to access. For people who want to go beyond a three-question self-check, it provides the specificity needed to make informed decisions about repositioning or career changes.
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