Research Foundation
The Evidence Behind the Framework
The RoleDNA framework is cross-validated against two landmark research sources, in addition to primary research conducted with practitioners across industries.
2025
Stanford WORKBank Study
"Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforce"
Stanford University, 2025
Key Findings Relevant to Role Design
​✓45.2% of occupations have equal human-AI partnership (H3) as the dominant worker-preferred collaboration level — but worker preferences and expert assessments frequently diverge, creating organizational risk
✓ Core human skills are shifting from information processing to interpersonal competence — the skills gaining premium are precisely the ones hardest to see in a job description
✓ 41% of current AI investment is concentrated in low-priority or resistance zones — organizations are deploying AI without understanding where it actually creates value
What This Means for Your Organization
Without role-level intelligence, AI deployment is guesswork. The Stanford data shows which tasks should be automated, which should be augmented, and where human agency must be protected — but that analysis has to be done at the role level, not at the organizational level.
2026
Lightcast AI Skills Report
"Beyond the Buzz: Develop the AI Skills Employers Actually Need"
Lightcast, 2026
Key Findings Relevant to Workforce Planning
​✓AI skills carry a 28% salary premium — organizations that cannot identify AI-readiness at the role level will pay that premium to recruit what they could have developed
✓ Over half of AI job postings fall outside technology roles — every function needs role-level AI competency mapping, not just engineering and data science
✓ HR shows the fastest AI adoption growth among all sectors at 66% — the buyers of role intelligence are themselves in rapid transformation
What This Means for Your Organization
Organizations that cannot identify AI-readiness at the role level will pay premium salaries to recruit what they could have developed internally. Every function — not just engineering — needs role-level AI competency mapping to stay competitive.
Research Methodology
Our Primary Research Methodology
Every role blueprint is validated through structured primary research. We do not rely on job postings, industry averages, or AI-generated templates.
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Organizational Psychologists
Validate cognitive capabilities, dispositional traits, assessment methodology, and interpersonal skill proficiency levels
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Talent Experts
Validate performance indicators, seniority differentiation, qualification requirements, and hiring assessment criteria
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Practitioners
Validate task decomposition, human-AI collaboration levels, and real workflow realities
Confidence Markers
​Every field in a role blueprint is assigned a confidence marker indicating the level of validation:
✓ Confirmed — Supported by multiple sources
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→ Needs Validation — Directionally supported, requires company-specific calibration
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? Primary Research Required — Cannot be determined without direct interviews
RoleDNA™ is built using large-scale analysis of over 22,000 job descriptions, career trajectories of 184k+ successful professionals in those roles, and deep organizational psychology research on performance predictors and team fit.
The engagement