1 Odesa National Polytechnic University (ONPU)
- Type
- Research Mini-Series
- Status
- Ongoing · 78 articles · 2026–present
- Tool
- AI Adoption Gap Monitor
AI systems can perform a wide range of tasks at or above human capability levels, yet deployment lags far behind demonstrated capability — a gap that varies dramatically by sector, organizational readiness, and barrier type. This mini-series systematically maps this gap, distinguishing three distinct mechanisms: adoption lag (organizational delay despite clear economic case), quality threshold barriers (output insufficient for cost-effective deployment), and organizational friction (workflow, liability, or institutional constraints). Each mechanism implies different closure timelines and policy interventions. Drawing on the Anthropic Economic Index baseline, this series examines how adoption velocity — not gap size — predicts labor market disruption, and provides real-time monitoring tools for tracking coverage signals across occupational categories.
Idea and Motivation
The Anthropic Economic Index (Massenkoff & McCrory, 2026) asked a question distinct from typical AI capability research: what tasks are people actually using AI for, versus what could theoretically be automated? The answer revealed a structural chasm — ranging from 2.8× in computer science to 8.0× in healthcare support — between measured theoretical exposure and observed coverage.
But gap size alone is a misleading metric. A gap of 8× closing rapidly signals a different economic timeline than an identical gap that remains static for a decade. The series begins with this insight: the critical variable is not the gap’s magnitude today, but its closure velocity and the organizational mechanisms that govern that velocity.
Goal
The series aims to build a diagnostic framework for understanding adoption barriers sector-by-sector, and to establish a real-time monitoring system for adoption velocity signals. The goal is not to predict when gaps will close, but to make the closure mechanisms visible: which barriers are primarily technical (solvable by better models), which are procedural (solvable by better processes), and which are structural (requiring institutional change that outpaces technology development).
Scope
The series covers six major occupational categories drawn from the Anthropic Economic Index baseline dataset: Computer & Mathematical (94% theoretical exposure, 33% observed coverage), Office & Administrative (90% vs 25%), Business & Financial (85% vs 20%), Legal (80% vs 15%), Healthcare Support (40% vs 5%), and Construction (15% vs 2%). Each occupational category is examined for its dominant adoption barrier and historical closure rates estimated from available labor market data.
| Occupational Category | Theoretical Exposure | Observed Coverage | Gap Ratio | Dominant Barrier Type |
|---|---|---|---|---|
| Computer & Math | 94% | 33% | 2.8× | Adoption lag |
| Office & Admin | 90% | 25% | 3.6× | Adoption lag + Friction |
| Business & Financial | 85% | 20% | 4.3× | Organizational friction |
| Legal | 80% | 15% | 5.3× | Liability friction |
| Healthcare Support | 40% | 5% | 8.0× | Quality threshold |
| Construction | 15% | 2% | 7.5× | Physical embodiment gap |
Focus
The primary focus is on mechanisms rather than occupations. The series investigates three distinct adoption barriers. Adoption lag occurs when organizations recognize AI capability and economic feasibility but have not yet deployed due to procurement cycles, integration costs, and risk aversion — typically closing within 18–36 months. Quality threshold barriers arise when model output quality is insufficient for cost-effective deployment without heavy human review — typically a 2–5 year closure timeline dependent on model improvement rates. Organizational friction encompasses workflow integration challenges, legal liability concerns, regulatory uncertainty, and institutional inertia — typically persisting 5–15 years even as technological capability exists.
Limitations
Scientific Value
The series contributes three distinct insights. First, it operationalizes the concept of adoption velocity as distinct from capability performance — a metric essential for labor economics and technology policy but rarely isolated in AI research. Second, it documents the sector-specific institutional barriers to adoption, shifting focus from general “AI adoption” to context-specific mechanisms with different policy interventions. Third, it establishes the AI Adoption Gap Monitor as an operational research tool for tracking adoption signals in real-time from public labor market, developer activity, and scientific publication data streams.
Resources
- AI Adoption Gap Monitor (Live Dashboard)→
- Anthropic Economic Index (Massenkoff & McCrory, 2026)→
- Stabilarity Research Hub→
- GitHub Repository→
Status
Ongoing. 78 articles published as of March 2026. The series is actively expanding with new sector analyses, updated adoption velocity calculations, and enhancements to the real-time monitoring dashboard. New articles published weekly as adoption data streams and organizational case studies are completed.
Contribution Opportunities
Researchers and practitioners are invited to contribute in the following directions:
- Sector-specific case studies: Document adoption barriers and closure mechanisms in your occupational domain. Contribute data on organizational decision-making timelines and integration costs.
- Adoption velocity models: Develop forecasting models for gap closure rates using historical technology adoption curves and current capability trajectory estimates.
- Real-time signal development: Propose new data streams (hiring patterns, GitHub commits, API usage, patent filings) that serve as leading indicators of adoption velocity.
- Barrier characterization: Document organizational, regulatory, and technical friction mechanisms in regulated sectors (healthcare, finance, legal) where adoption lags are largest.
- Dashboard extension: The AI Adoption Gap Monitor is open to contribution. Propose new metrics, visualizations, or occupational categories for monitoring.