Workforce Intelligence Research & Data Sources
The authoritative workforce intelligence resource hub for data-driven operators, CFOs, and HR leaders designing their teams for the AI era. Every calculator, benchmark, and hybrid model on The People Stack is built on the primary sources catalogued here.
The most reliable workforce intelligence comes from five primary source categories: BLS occupational wage and compensation data (the gold standard for salary and benefits benchmarks in the US), McKinsey Global Institute AI research (the most cited body of work on automation potential and hybrid team productivity), World Economic Forum Future of Jobs reports (the leading longitudinal study of skill shifts), Gartner and Deloitte workforce planning benchmarks (practical adoption and cost data for enterprise decision-makers), and direct platform pricing data for AI tools (the only way to model real AI deployment costs). This page curates all of these with direct links, publication dates, and a plain-language summary of what each source contributes.
Bureau of Labor Statistics Data
BLS is the primary source for US workforce cost data. The People Stack calculators use OEWS for salary benchmarks and ECEC for benefits loading rates — updated quarterly.
all occupations
compensation
rate (FICA)
Occupational Employment and Wage Statistics (OEWS)
The definitive source for US salary benchmarks by occupation, metro area, and industry. Updated semi-annually. The People Stack uses Q4 2024 OEWS data for all role salary inputs.
View Dataset ↗Employer Costs for Employee Compensation (ECEC)
Quarterly survey of employer compensation costs — wages plus benefits (health insurance, retirement, PTO, supplemental pay). Used to calculate the 30.5–33% benefits loading rate in our calculators.
View Dataset ↗Occupational Outlook Handbook (OOH)
10-year employment projections by occupation. Identifies fastest-growing and fastest-declining roles — critical input for workforce design decisions about which human roles to invest in vs. phase out.
View Handbook ↗Job Openings and Labor Turnover Survey (JOLTS)
Monthly data on job openings, hires, quits, and layoffs by sector. Key input for talent availability modeling — if a role is hard to hire, that increases the relative value of automation or hybrid models.
View Survey ↗Industry Research Reports
McKinsey, WEF, Deloitte, and SHRM produce the most-cited workforce research. These reports anchor The People Stack's benchmarks on AI adoption rates, hybrid team productivity, and HR cost ratios.
The State of AI 2025: McKinsey Global Survey
Annual survey of 1,000+ executives on AI adoption, productivity gains, and workforce impact. Source for The People Stack's 3× hybrid output benchmark and 60–70% task automation estimates across structured roles.
Read Report ↗Future of Jobs Report 2025 — World Economic Forum
The most comprehensive longitudinal study of workforce transformation. Covers the 5-year skills outlook, fastest-growing and declining job categories, and reskilling investment benchmarks across 55 economies.
Read Report ↗2025 Global Human Capital Trends — Deloitte
Annual C-suite survey on workforce priorities. Covers hybrid operating models, AI augmentation, worker wellbeing economics, and organizational resilience. Heavily cited in workforce planning at $50M+ organizations.
Read Report ↗SHRM Talent Acquisition Benchmarking Report 2025
The industry standard for HR cost metrics: cost-per-hire (21% of first-year salary benchmark), time-to-fill, benefits cost ratios, and turnover costs. These figures are built into all People Stack cost models.
View Research ↗Superagency in the Workplace: AI and Talent Strategy
McKinsey's most detailed report on AI-augmented workforce design. Covers specific productivity outcomes by function, implementation timelines, and the organizational conditions that predict hybrid team success.
Read Insights ↗Global AI Jobs Barometer 2025 — PwC
Cross-country study of AI's effect on productivity, wages, and employment. Quantifies the AI wage premium (25–40% higher pay for AI-augmented roles) and the skills gap cost businesses absorb during AI transitions.
Read Report ↗AI Capability Benchmarks
Real AI deployment costs come from platform pricing, not theoretical models. These sources underpin our AI stack cost estimates across function categories.
GitHub Copilot Enterprise Pricing & Productivity Data
Microsoft's published data on Copilot productivity outcomes — 55% faster task completion for common coding tasks. Pricing: ~$39/user/month enterprise. Input for our engineering role AI stack models.
View Pricing ↗Intercom AI Resolution Rate Studies
Intercom's Fin AI resolves 50–70% of support tickets autonomously at ~$0.99/resolution. The benchmark source for customer support hybrid stack modeling. Compares directly against tier-1 human support cost of $8–22/ticket.
View Data ↗Gartner AI in Sales: Automation Potential by Task
Gartner's assessment of AI task automation in sales — 60–70% of SDR volume tasks (prospecting, outreach, follow-up cadences) can be fully automated at L3–L4. Basis for sales role autonomy level assignments in our calculator.
Read Research ↗Anthropic + OpenAI Platform Pricing (Claude, GPT-4o)
Current token pricing for frontier AI models — the foundation for AI content generation cost models. Claude API: $3–15/MTok input; GPT-4o: $2.50–10/MTok. Used to build content production cost estimates in all role models.
View Pricing ↗vs. all-human teams
current AI (SDR function)
with AI copilots
Workforce Planning Frameworks
These frameworks provide the conceptual scaffolding for how The People Stack approaches workforce design — not just cost optimization, but intentional stack architecture.
The People Stack Autonomy Model (L0–L4)
Our proprietary framework for classifying every role by AI autonomy level: L0 (fully human) through L4 (AI-native, 95% automatable). The basis for all hybrid stack recommendations in our calculator. Methodology built on BLS task taxonomy and Gartner automation research.
Explore in Calculator →Hire vs. Automate vs. Hybrid Decision Framework
A four-dimension decision model — role complexity, error tolerance, regulatory environment, and cost — that systematically routes each role to the optimal workforce configuration. Validated against McKinsey and BLS task taxonomy data.
Read the Full Framework →WEF Skills Taxonomy for the AI Era
The WEF's structured classification of future-critical skills: analytical thinking, AI/big data literacy, resilience, and leadership. Used by workforce planners to identify which human skills to develop when AI handles more routine work.
View Framework ↗SHRM HR Metrics Benchmark Framework
The industry standard for HR cost benchmarking — headcount ratios, cost-per-hire, time-to-productivity, turnover rates, and compensation ratios. Referenced in all People Stack overhead and recruiting cost models.
View Benchmarks ↗People Stack Tools & Calculators
All research above is synthesized into these free, interactive tools. Built for operators — not analysts.
Workforce Design Calculator
Design your entire team across every role — get autonomy levels, hybrid stack recommendations, cost data, and a 3-phase implementation plan. Uses BLS, McKinsey, and Gartner data under the hood.
Open Calculator →AI vs. Human Cost Calculator
Model the fully-loaded cost of a human, AI, or hybrid configuration for any specific role. BLS salary data, real AI platform pricing, SHRM benefits rates. Transparent formulas you can verify.
Open Calculator →Hire, Automate, or Stack Hybrid? The Framework
The definitive framework for routing each workforce decision. Covers 7 decision dimensions with an interactive decision tree, cost tables, and risk profiles by path. Sourced against BLS, McKinsey, and Gartner.
Read the Framework →AI vs. Employee Cost: 2026 Breakdown
Comprehensive cost breakdown across human, AI, and hybrid stacks by role, industry, and company size. The single most-cited People Stack resource — a living document updated quarterly with fresh BLS data.
Read the Breakdown →Data Methodology
Salary benchmarks
All US salary benchmarks use BLS OEWS Q4 2024 median annual wages by Standard Occupational Classification (SOC) code, adjusted for industry and metro area where applicable. For non-US locations, we apply BLS-indexed multipliers derived from published cost-of-labor comparisons and PwC's Global Total Remuneration survey.
Benefits loading
Benefits are applied as a percentage of base salary using BLS ECEC Q4 2024 data: 30.5% for companies under 500 employees, 33% for 500+ employees. This covers legally required benefits, health insurance, retirement, paid leave, and supplemental pay. Management tax (15% of base for individual contributors) and recruiting cost (21% of first-year salary, per SHRM) are added separately.
AI platform costs
AI stack costs use published Q1 2026 pricing for leading platforms in each function category (e.g., Intercom Fin for support, GitHub Copilot for engineering, Salesforce Einstein for sales). All AI stack costs include: platform/API fees, one-time setup amortized over 36 months, annual maintenance (15% of platform cost), and partial oversight FTE (0.3–0.8 FTE depending on autonomy level). We update platform pricing quarterly.
Hybrid configuration modeling
Hybrid stack models are calibrated against McKinsey 2025 enterprise AI deployment case studies and Gartner's 2025 AI maturity assessments. The default hybrid split (35% human / 65% AI capacity) represents observed configurations at companies 18+ months into hybrid deployment. Earlier-stage deployments typically start at 60% human / 40% AI and migrate over time.
Autonomy levels (L0–L4)
The L0–L4 autonomy scale classifies what percentage of a role's tasks can be autonomously performed by current AI: L0 = 0%, L1 = 25%, L2 = 50%, L3 = 75%, L4 = 95%. These assignments are derived from McKinsey task automation research, Gartner AI capability assessments by function, and direct review of AI platform capability documentation. Autonomy levels are updated semi-annually as AI capabilities advance.
Build your workforce stack with the data that's in this page
Our calculators apply BLS, McKinsey, Gartner, and SHRM data to your specific roles and company profile. Free, transparent, and built for operators.