Your company’s workforce strategy is either ahead of this analysis or already behind it.
HR leaders are flying blind on AI displacement risk
Workforce planners and policy teams are making million-dollar decisions about hiring, reskilling, and restructuring without a structured method to separate roles that AI will eliminate from roles it will simply reshape. The gap between gut instinct and evidence is where expensive mistakes live.
921 occupations just got a risk score
Modeling an AI jobs transition examines 921 occupations across 148 million U.S. jobs, categorizing each role into one of four outcomes: automation risk, reorganization, growth, or minimal disruption. Users input an occupation or sector and receive a structured classification with supporting rationale drawn from task-level AI exposure data. The output is a defensible, data-grounded assessment, not a vendor forecast.
The people who need this analysis yesterday
- Chief People Officers benchmarking which job families face near-term restructuring before a board presentation demands answers
- Policy researchers who need occupation-level displacement data to model retraining program scope and funding requirements
- Career counselors advising clients on whether their current role is a three-year runway or a ten-year anchor
Each of these users is currently working from fragmented sources with no single framework tying task exposure to occupational outcome at this scale.
The window to get ahead of this is closing fast
The Bureau of Labor Statistics updates occupational projections on a two-year cycle, a pace that cannot track AI capability shifts happening quarter to quarter. Organizations that map their workforce now have a structural advantage over those waiting for official projections to catch up.
What this framework lets you do
- Score any of 921 U.S. occupations against current AI task exposure
- Separate roles facing full automation from those facing partial reorganization
- Build reskilling priority lists ranked by displacement timeline
- Pressure-test executive assumptions about which teams are actually safe
Pricing not listed — check our directory.
The honest gap in any occupation-level model
Occupation-level classifications smooth over real variation inside job titles, so two people with identical roles at different companies may face very different actual exposure.
Other tools in this space
McKinsey’s occupational automation research covers similar terrain but sits behind consulting engagements rather than open analysis. The World Economic Forum’s Future of Jobs report maps displacement at sector level, which is too coarse for role-specific planning.
The race between reskilling budgets and AI deployment timelines is already underway
Tools like this are surfacing faster than most HR functions can absorb them — we cover tools like this every Friday, so subscribe here and we’ll send the best ones straight to you.