How It Works
The RoleDNA Framework
Built with input from organizational psychologists, talent experts, and practitioners, this is not an AI-generated template and not a rewritten JD. It is a research-backed model of the role itself.
Why the old model breaks
Most role decisions run on assumptions, not a shared definition of the role.
Traditional job descriptions describe tasks, not capability. They capture what the role looked like when it was last posted, not what it actually requires today. They say nothing about what AI changes, what humans must own, or what proficiency actually looks like in practice.
This creates a fundamental problem: when hiring, onboarding, performance reviews, and promotion decisions are all based on different implicit models of what "good" looks like, inconsistency becomes inevitable. The real cost isn't just inefficiency, it's that organizations can't see their own people clearly.
See what replaces assumption-based role decisions
What the framework captures
A RoleDNA blueprint defines the role at the level where better decisions become possible.
Not just what the role does, but what the role requires, what changes with AI, and what readiness actually looks like.
Skills and Competencies
Technical skills, domain expertise, cognitive capabilities, interpersonal strengths, and self-regulation requirements, each calibrated to the role and the level of seniority.
AI Competency Mapping
A clear view of what AI can handle, where human judgment remains essential, and where new kinds of human-AI coordination are emerging.
Human
factors
The behavioral tendencies, aptitudes, and dispositional patterns that often shape performance but rarely appear in formal job documentation.
Human-AI collaboration levels
Task-level mapping of how work is shared between human and AI, so teams can see not just that work is changing, but exactly how.
Understand the layers that turn role clarity into usable operating logic.
Human agency in the role
The Human Agency Scale
Not all tasks are equal. Some tasks in every role are shifting to AI. Others require more human judgment than ever. Most require a new kind of partnership.
The RoleDNA framework maps this at the task level — so organizations can see exactly where the work is changing and what that means for hiring, development, and performance.
AI Impact Level | Human Involvement | AI Role |
|---|---|---|
H1 | AI handles entirely | Automation |
H2 | AI leads, human input at key points | Automation with oversight |
H3 | Equal partnership | Augmentation |
H4 | Human leads, AI assists | Augmentation |
H5 | Fully human | Human-owned |
Source: Stanford WORKBank Human Agency Scale
How the blueprint is built
A blueprint is built through research
and layered validation.
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Organizational Psychologists
Validate cognitive capabilities, self-regulation, attitudes and aptitudes, interpersonal skill proficiency levels, and assessment methodology for dispositional traits.
Talent Experts
Validate performance indicators, seniority differentiation markers, qualification requirements, culture fit signals, onboarding milestones, and hiring assessment criteria.
Practitioners
​Validate task decomposition accuracy, human-AI collaboration levels, skill proficiency calibration, AI tool adoption patterns, and decision authority boundaries.
No blueprint is finalized from desktop research alone. Primary validation is built into the methodology.
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The engagement