Austria's AI gender gap is structural, not salary-driven. Women represent 25.4 % of Core AI roles versus 43.6 % in non-AI employment, an 18.2 percentage-point participation gap that dwarfs the 7.1 % AI pay gap. The challenge deepens at the frontier: Build tier is 18.6 % female, and representation collapses from ~36 % at entry to 6.7 % at C-Suite. At the current rate of +0.3 pp per year, closing the gap to parity is a 60-year glide path.
Gender split — AI vs non-AI workforce
The more technical the definition, the wider the gap.
- Non-AI employment is 43.6% female; Core AI drops to 25.4% — an 18.2pp participation gap.
- The gap widens as you narrow the definition: Broad (31.2%) → Full (28.8%) → Core (25.4%).
- Austria's 25.4% is comparable to EU averages but well below Finland (31%) and Portugal (29%).
Gender by tier — the depth gradient
- Build tier is 18.6% female — the lowest of any tier and nearly half the non-AI benchmark.
- Enable (25.1%) and Integrate (26.3%) cluster near the Core AI average.
- Adjacent roles reach 29.8% — the highest among AI categories but still 14pp below non-AI.
Female share over time — progress at glacial pace
- Core AI female share improved from ~22% (2018) to 25.4% (2025) — roughly +0.3pp/year.
- Non-AI has been stable at 43–44%; the gap narrows because AI started far behind.
- At +0.3pp/yr, reaching Non-AI parity is a 60-year glide path — well beyond any policy horizon.
Gender by seniority — the leaky pipeline
A funnel, not a gradient. The pipeline enters balanced and collapses upward.
- Entry-level AI is ~36% female — reasonably close to broader tech benchmarks.
- Manager: ~21%; Director: ~15%. The sharpest attrition happens in the Analyst→Senior transition.
- C-Suite AI is just 6.7% female vs 17.8% non-AI — AI leadership is less diverse than general corporate leadership at every level.
Gender by subcategory — pockets of progress
- AI Governance (42.6%) is nearly at parity — far above any other AI subcategory.
- Analytics Management (34.2%) and Data Science (28.5%) also outperform.
- Core ML Research (17.8%) and Computer Vision (16.4%) are the least balanced — frontier research pipeline constraints.
Gender by geography — regional patterns
- Vienna's share (~28%) reflects service-sector and public-sector AI roles — more balanced than industrial.
- Industrial regions (Styria, Upper Austria) show lower shares — sector composition, not culture alone.
- The tier and seniority gradients are steeper than the geographic gradient; region is secondary.
Gender uses Revelio Labs' machine-predicted classifications (~95% aggregate accuracy). Individual-level predictions carry inherent uncertainty; the patterns reported here are robust to reasonable error margins. Analysis covers all AI workers in the austria_located segment (2018–2025) and benchmarks against non-AI employment observed in the same firm universe.