How this guidance is made

Methodology

We combine task-level work analysis, current workforce research, established governance guidance, and explicit interpretation boundaries.

The core unit of analysis is the task inside a real workflow, not a prediction about whether an occupation will “be automated.”

Unit of analysis

Roles are treated as changing bundles of tasks, decisions, relationships, responsibilities, knowledge, and context. A workflow is examined for purpose, inputs, outputs, judgment, exceptions, consequence, evidence, reversibility, and human accountability.

Each task is placed in one of three provisional lanes: human-led, AI-assisted, or delegated with review. A lane is not permanent. It can change as evidence, capability, policy, or consequence changes.

Evidence labels

Observed evidence

A finding reported by a linked research source. We preserve the population, timing, and limits needed to interpret the finding.

Operating guidance

A practical recommendation drawn from an established framework, standard, or regulator guidance. It still requires local adaptation.

Design hypothesis

A task-level starting point offered for discussion and testing. It is not a factual prediction, policy determination, or employment recommendation.

Role playbooks

Playbooks use occupation and task foundations from O*NET, then add work-design hypotheses informed by current AI capability patterns, the NIST AI Risk Management Framework, workforce research, and practical review questions. They are intentionally concise so a team can challenge and adapt them.

Limits and updates

AI capability, law, standards, products, and organizational context change. Research pages display a review date. Material claims link to the source used. When a source changes or stronger evidence appears, guidance should be re-evaluated.

This site is an educational resource. It does not provide legal, employment, labor, safety, medical, privacy, cybersecurity, financial, or regulatory advice. Qualified review is necessary for consequential use.

To raise a correction or source concern, use the contact channel on Mojica Consulting and identify Human AI Workforce and the page in question.

Source shelf.

Primary, governmental, institutional, and first-party research is preferred for material claims.

O*NET Database

Occupation, task, skill, work-activity, and work-context foundations

Open source ↗
U.S. Department of Labor AI Literacy Framework

Foundational AI literacy content and delivery principles

Open source ↗
NIST AI Risk Management Framework

Governance, oversight, documentation, and review practices

Open source ↗
World Economic Forum Future of Jobs

Employer expectations about workforce and skill change

Open source ↗
Microsoft Work Trend Index

Organizational readiness and worker adoption signals

Open source ↗
PwC Global AI Jobs Barometer

Job, skill, productivity, and wage patterns in AI-exposed work

Open source ↗

Use the method on one real workflow.

Start with a role playbook or open the studio and change every assumption that does not fit your context.