About & Methodology
What this tool is, where the numbers come from, and what they can and cannot tell you.
Independent project — not a government service
This is a free tool built by Scott Hazlitt as a personal project. It is not affiliated with the Government of Manitoba, any provincial department, or any employer or industry association. No personal data is collected. Risk scores are modelled estimates derived from peer-reviewed academic datasets — not predictions, and not professional employment, legal, or business advice. See Terms of Use for full disclaimers.
What this tool does
The Manitoba AI Disruption Explorer shows how likely each Manitoba industry and occupation is to be affected by AI — based on published academic research, not opinions. It combines four datasets into a single score (0–100) for each sector and job type, then adjusts for your business size and current AI use.
The scores are pre-calculated and stored as static data. No AI is involved in generating them at the time you visit the site — the numbers come entirely from the published datasets listed below, processed offline.
Manitoba was chosen because its economy is a useful cross-section: large insurance and financial services in Winnipeg, significant aerospace manufacturing, a large public sector, and agriculture — each with a very different AI exposure profile.
Data Sources
| Source | Description | Vintage |
|---|---|---|
| Frey & Osborne (2013) | 702 US occupations rated for automation probability using a Gaussian process classifier. The foundational dataset for occupation-level automation risk. | 2013 |
| Felten, Raj & Seamans — AI Occupation Exposure Index | AI Occupational Exposure index linking AI patent applications to O*NET occupational task descriptions. Captures AI-specific exposure, distinct from general automation. | 2021 |
| Eloundou et al. (2023) — GPTs are GPTs | Occupation-level language AI exposure dataset published by OpenAI researchers. Human-reviewed scores (0–1) covering 923 occupations, mapped to Canadian job codes. Measures direct language AI substitution plus half-weight tool-augmentation exposure. Used as the Language AI Impact component. | 2023 |
| Brookfield Institute NOC Crosswalk | Maps US SOC occupation codes to Canadian NOC 2021 codes. Required because Frey & Osborne and the AI exposure index use SOC; Canadian employment data uses NOC. | 2019–2021 |
| Statistics Canada — Census 2021 | Manitoba employment counts by NOC occupation. The primary source for how many Manitobans work in each occupation. | 2021 |
| Statistics Canada — Canadian Survey on Business Conditions (CSBC) | Provincial AI adoption rates by industry sector. Provides Manitoba-specific figures showing the province at approximately 2% — significantly below the national average of 12% as of Q2 2025. Used for the Sector Adoption Gap component. | Q2 2024–Q2 2025 |
| Manitoba Bureau of Statistics — GDP by Industry | Manitoba GDP shares by NAICS sector. Provides the economic weight context alongside employment counts. | 2022–23 |
| Anthropic Economic Index (2026) | Tracks actual Claude usage patterns across occupations, sampled from real interactions (Feb 5-12, 2026). Unlike the other sources which measure theoretical AI capability, this index measures what AI is doing in practice — based on 94GB of Claude conversation data categorized by the Clio privacy-preserving analysis tool. Displayed as supplementary context in occupation detail panels; not included in the composite score formula due to SOC major-group granularity (22 groups vs. 923 individual occupations in the other datasets). | Q1 2026 (quarterly updates) |
| Remote Labor Index — remotelabor.ai (2025) | Benchmarks actual autonomous AI agent performance on 240 real paid freelance projects across 23 Upwork skill categories, tested with 358 freelancers. Measures what advanced AI systems actually complete end-to-end today (0.83%–4.17%), providing real-world calibration for the theoretical exposure scores in this tool. Used as a contextual callout in calculator results to ground the gap between capability and deployment. Not incorporated into the composite score formula. | Feb–Mar 2025 |
| OpenAI — Industrial Policy for the Intelligence Age (2026) | Policy paper projecting that AI infrastructure buildout (data centres, power grids, cooling systems) will require approximately 20% more skilled trades workers — electricians, mechanics, ironworkers, carpenters, plumbers — than currently exist. Used as a counter-exposure signal on trades occupations in the explorer: low AI displacement risk combined with rising infrastructure demand. Not a research dataset; cited as a policy reference only. | April 2026 |
| MIT Project Iceberg (2024) | Maps 13,000+ deployed AI tools against 32,000+ skills across 923 O*NET occupations (151 million workers) to produce an Iceberg Index: the percentage of an occupation's wage value where AI has demonstrated capability. Key finding: visible tech-sector disruption is just 2.2% of the U.S. labour market's wage value, while hidden white-collar and administrative exposure is 11.7% — five times larger and geographically distributed across all states. Referenced as a methodological context source; not incorporated into composite scores because occupation-level Index values are not published as a downloadable dataset. | 2024 |
| Future Skills Centre — Canada's Workforce in Transition | Classifies 57.4% of Canadian jobs as highly AI-exposed, split between AI-competing roles (where AI automates core tasks) and AI-augmenting roles (where AI enhances human capabilities). Analyzes 19 million job postings to track shifting demand. AI-augmenting roles grew 2.9% in 2024, outpacing AI-competing roles at 1.6%. | Sept 2025 |
| Conference Board of Canada — Understanding the Influence of AI on Employment | Canadian task-level AI exposure index covering 501 NOC occupations and 304 NAICS industries. Uses a 3-phase framework: exposure, productivity gains, and automation likelihood. Projects a short-term employment dip of 535,000 jobs by 2030, followed by a long-term gain of 555,000 jobs by 2045 as productivity benefits materialize. | Jan 2026 |
| The Dais / FSC — Right Brain, Left Brain, AI Brain | Exposure-complementarity framework classifying 506 Canadian NOC occupations into four quadrants based on AI exposure and whether AI assists or replaces workers. 56% of Canadian workers are in occupations with higher AI exposure. Used as the basis for the AI-augmenting and AI-competing labels shown in occupation detail panels. | Jan 2025 |
| Policy Exchange — Government in the Age of Superintelligence | UK policy think-tank report examining workforce transformation, skills revaluation, and government preparedness for AI disruption. Projects large-scale labour market dislocation across white-collar sectors, recommends national retraining capacity of 250,000 workers annually, and argues that roles dismissed as 'low-skilled' are actually 'low-paid' and will command increasing premiums as cognitive work is automated. | 2025 |
Score Formula
Overall Risk Score =
(Automation Probability × 0.30)
+ (AI Occupation Exposure × 0.30)
+ (Language AI Impact × 0.25)
+ (Sector Adoption Gap × 0.15)
Adjusted Score =
Overall Risk Score × Business Size Factor × AI Adoption Factor
The Frey & Osborne (2013) automation probability for the closest matching occupation, converted to a 0–100 scale. Reflects the probability that an occupation’s tasks could be automated by computerisation over roughly a 10–20 year horizon.
The Felten-Raj-Seamans AI Occupational Exposure index, normalized to 0–100. Measures how much of an occupation’s task content corresponds to capabilities demonstrated in recent AI patent applications. More AI-specific than the automation probability measure.
Human-reviewed scores from the Eloundou et al. (2023) published dataset, normalized to 0–100. Measures the fraction of an occupation’s tasks that could be handled directly by language AI, plus half-weight credit for tasks where AI tools provide assistance. Covers 923 occupations; mapped to Canadian job codes via the Brookfield Institute crosswalk.
The gap between maximum possible AI adoption (100%) and the sector’s current adoption rate. A high gap means the sector has not yet adopted AI broadly, indicating that AI-driven disruption is ahead of, not behind, the current workforce.
Business size modifiers
AI adoption modifiers
Risk tier thresholds
Limitations
- Frey & Osborne predates LLMs. The 2013 paper was written before GPT, diffusion models, and modern generative AI existed. It likely underestimates disruption risk for knowledge-work occupations. We partially compensate with the Language AI Impact component, but the underlying dataset remains a product of its era.
- National AI adoption rates used as Manitoba proxy. No province-specific AI adoption survey exists. Manitoba may lag national averages in some sectors (particularly due to firm size distribution and distance from technology hubs) or lead in others (aerospace).
- Composite scores are relative, not predictive. A score of 72 does not mean 72% of jobs in that sector will be lost. It means that sector scores in the 72nd percentile of AI disruption exposure relative to others. Actual employment outcomes depend on labour market conditions, regulation, adoption rates, and adaptation.
- O*NET-to-NOC crosswalk covers 45 of 49 occupations directly. Four occupations (College Instructors, Biological Scientists, Equipment Assemblers, Shelf Stockers) use averaged scores from the nearest job category groupings rather than a single exact match. Their confidence rating is still marked “published” as the underlying scores are real data, but the crosswalk introduces slightly more noise for these occupations.
- SOC-to-NOC crosswalk introduces noise. Frey & Osborne and the AI exposure index use US Standard Occupational Classification codes. The Brookfield Institute crosswalk maps these to Canadian NOC codes, but the mapping is not always one-to-one. Some NOC occupations combine multiple SOC categories; scores for these are averaged.
- Small-sector estimates carry higher uncertainty. Sectors with fewer than 10,000 Manitoba employees (e.g., Mining & Oil, Corporate Management) have fewer reference occupations, making sector-level score aggregation noisier.
- Anthropic Economic Index data is at the SOC major-group level — all occupations within a group (e.g., all “Computer and Mathematical” occupations) receive the same usage intensity value. Shown as supplementary context only; not incorporated into composite scores.
- Remote Labor Index reflects a point-in-time snapshot. The 0.83%–4.17% autonomous completion range is from a Feb–Mar 2025 benchmark across 23 Upwork categories. AI agent capability is improving rapidly; this figure should be treated as a lower-bound calibration anchor, not a permanent ceiling.
- MIT Iceberg occupation scores are not yet incorporated. The Iceberg Index covers 923 O*NET occupations using 32,000+ skills mapped against 13,000+ deployed AI tools. Its key insight — that visible tech disruption (2.2% of wage value) is dwarfed by hidden white-collar exposure (11.7%) — is referenced in the methodology but individual occupation Index values are not published as a downloadable dataset, so they cannot be added to the composite score formula.
- AI-augmenting/AI-competing classification is a binary simplification. Derived from the FSC complementarity framework (building on IMF methodology by Pizzinelli et al.) applied to Canadian NOC codes. Some occupations near the threshold could reasonably be classified either way. The classification reflects the current generation of AI tools and may shift as capabilities evolve.
Methodology Decisions
Each component captures a different dimension of AI-related risk. The automation probability measures task routineness. The AI occupation exposure index measures demonstrated AI capability overlap. The language AI impact score captures the generative AI wave specifically. The adoption gap captures timing — a sector with high exposure but low current adoption is at acute near-term risk, not just theoretical risk.
Automation probability and AI occupation exposure are given equal weight as the two most established academic measures. Language AI impact is weighted slightly lower because some scores are estimated rather than directly published. The adoption gap is weighted lowest because it is a sector-level proxy rather than an occupation-specific measure.
Micro-businesses (<5 employees) tend to have less access to AI tools and less organizational capacity to manage AI transitions, increasing their effective risk. Large businesses have more resources to adapt and to absorb workforce changes, reducing their effective risk. These modifiers adjust the composite score to reflect business context without changing the underlying occupational data.
Businesses already using AI tools have de facto begun their transition. Their effective exposure to disruption from AI adoption is lower because they are driving the change rather than being surprised by it. The 30% reduction reflects this first-mover advantage, not immunity.
All four composite score components measure theoretical AI capability exposure — what AI could automate, based on task and skill overlap. Real-world deployment lags significantly behind theoretical capability. The Remote Labor Index (2025) found that advanced AI systems complete just 0.83%–4.17% of complex professional projects end-to-end without human intervention. The composite scores in this tool reflect the destination of the disruption curve, not its current position. The cost convergence charts in the calculator results are explicitly modelled as a 24-month trajectory, not an instantaneous shift.
MIT Project Iceberg (2024) found that visible tech-sector AI disruption represents just 2.2% of total U.S. labour market wage value — while hidden white-collar and administrative exposure is 11.7%, five times larger, spread across manufacturing, financial services, logistics, and healthcare administration in every state. This tool's occupation and industry scores capture both the visible layer (software, engineering, creative roles) and the larger hidden layer (administrative, coordination, office support). The Iceberg research was independently validated against the Anthropic Economic Index with 69% geographic agreement and 85% accuracy in predicting occupational transitions.
The Anthropic Economic Index (March 2026) measures actual Claude usage patterns across 22 SOC major occupational groups — a 2026-vintage real-world signal. Adding it to the composite formula at 15–25% weight would flatten within-group differentiation: Software Developers and Network Technicians would both become “Computer and Mathematical = same score,” which is methodologically dishonest for a precision tool. It is shown as supplementary context in occupation detail panels instead, with a note about the group-level granularity.
Flag a Data Issue
If you notice an occupation score that appears incorrect, a Manitoba employer missing from a sector, or a data source that has been updated since our vintage year, please open an issue on the project repository or contact the maintainer directly. We prioritize corrections that affect high-employment occupations or flagship Manitoba sectors.