AI exposure risk and durable skill analysis for your role

You asked about Employees trying to figure out which AI skills to learn and where to expand their professional knowledge to stay employable, overwhelmed by scattered courses and conflicting advice about what their role will actually require — research covers the wider market around it, so some findings may sit outside your exact focus.

Act on the research

Turn the recommendation into a conditional plan

The research concluded Conditional, so one unresolved condition sits in front of this plan. Sequence the check that clears it before the steps that spend against it.

Product decision

What the first useful version needs to prove

5-8 weeks

Product promise

Mid-career technical professionals struggle to assess long-term job security for their current role given AI displacement predictions. AI Career Radar addresses this by normalizing career data against labor-market trends to provide personalized exposure scores and actionable pivot paths. This empowers users to identify skill durability and transition strategies using data-backed insights.

First-release scope

Job title normalization to SOC/O*NET codes with synonym matching
Role Exposure Score based on task routineness and AI capability overlap
Skill Durability Heatmap showing transferability across growing occupations
Pivot Path Planner ranking AI-adjacent roles by retraining time and compensation uplift
Personalized PDF reports with actionable development priorities
Geography-specific wage and outlook data (OEWS/OOH)

Build approach

Normalize user title → SOC/ONET mapping (string match + synonym table). 2) Pull OEWS wages/employment and OOH outlook where available; cache by SOC + geography. 3) Pull ONET skills/tasks/technology for the mapped occupation. 4) Compute (a) growth score from OOH projections, (b) task exposure proxy from ONET task routineness/structure + tech overlap with automation-prone categories, (c) skill transferability by cross-referencing skill sets across ONET occupations with positive growth outlooks. 5) Generate pivot recommendations by minimizing skill gap distance weighted by retraining time estimates. 6) Build programmatic role pages by templating SOC-level data with computed exposure/durability metrics.

Data or integration dependencies

O*NET (Occupational Information Network)
OEWS (Occupational Employment and Wage Statistics)
OOH (Occupational Outlook Handbook)
Bureau of Labor Statistics API

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Open the detailed generated plan for this topic when you are ready to execute it.

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Turn the recommendation into a conditional plan

You saw one niche analyzed.Now do the same for yours.

You saw one niche analyzed.
Now do the same for yours.