role-based-aipom-competency-map

name	role-based-aipom-competency-map
description	Define observable AI product competencies by role and proficiency level, tied to real decisions, evidence, practice, and progression rather than generic tool fluency.
type	component
category	capability-adoption-and-reuse
phase	2
status	active
operating_level	["organization","product-team"]
audience	["CPO","Product Operations","Product Manager","Team Lead","Design","Engineering","Data","AI Governance","Learning and Development"]
best_for	["Setting role-specific AI product expectations","Designing applied learning paths","Distinguishing proficiency from training attendance"]
evidence_required	["Role decisions and workflows","Expected outcomes and failure consequences","Work artifacts and observed practice","Current capability and support evidence"]
produces	["Role-based competency map","Observable proficiency rubric","Practice and progression priorities"]
assessment_questions	["CAP-01","CAP-02","CAP-03","CAP-05"]
maturity_move	{"from":"emerging","to":"repeatable"}
estimated_time	60-120 min
group_size	4-10
depends_on	["aipom-capability-maturity-advisor"]
combine_with	["aipom-learning-system-designer","aipom-adoption-impact-scorecard","workflow-to-skill-converter"]
sources	[]

Role-Based AIPOM Competency Map

What Is It

Define what people in specific roles must understand and demonstrably do to make better AI product decisions. Describe proficiency through observable work, judgment, evidence, and outcomes—not tool familiarity or course completion.

Why Use It

Generic AI literacy produces uneven expectations and weak transfer into work. Role-based competencies focus development on the decisions, responsibilities, and failure modes each role actually owns.

When to Use It

Use after diagnosing a capability gap and before designing curricula, coaching, assessment, hiring, or progression. Begin with a few consequential roles and workflows rather than an enterprise encyclopedia.

What It Produces

• Priority roles, decisions, and expected competencies
• Observable proficiency levels and evidence
• Practice, coaching, support, and progression priorities
• Ownership, review cadence, and fairness safeguards

Who Should Participate

Include role practitioners and leaders, Product Operations, learning and development, relevant functional experts, and governance partners. Include people affected by how the expectations will be assessed.

Evidence to Bring

Bring role charters, real decisions, workflow playbooks, artifacts, failure examples, performance evidence, support requests, observed practice, and current learning paths. Attendance and self-confidence are weak evidence alone.

How to Do It

1. Select roles and consequential decisions in scope.
2. Define the outcomes, responsibilities, evidence duties, and failure modes for each role.
3. Group competencies around work such as framing, context, evaluation, economics, governance, facilitation, and reuse.
4. Write observable proficiency levels: foundational, applied, independent, and enabling.
5. Name evidence that demonstrates each level in real or representative work.
6. Identify required practice, feedback, coaching, tools, and organizational support.
7. Distinguish individual skill gaps from workflow, policy, incentive, or access barriers.
8. Check accessibility, fairness, role boundaries, and unintended credentialism.
9. Prioritize the smallest capability moves that unblock operating outcomes.
10. Assign a steward and review triggers as roles and practices change.

Key Concepts

• Competence is observable judgment in context.
• Proficiency should change what a person may responsibly own.
• Training cannot repair missing authority, access, incentives, or workflow design.
• Enabling proficiency includes coaching and improving organizational practice.

Organizational Applications

Use for role expectations, applied learning, coaching, hiring, internal mobility, community stewardship, and capability investment decisions.

Common Pitfalls

• Defining every role as a prompt engineer
• Measuring attendance, logins, or confidence as proficiency
• Using vague verbs such as “understands AI”
• Building levels with no observable evidence
• Blaming people for operating-system barriers
• Turning the map into an inflexible performance weapon

Combine With

Use aipom-learning-system-designer to create applied progression, aipom-adoption-impact-scorecard to measure changed practice and outcomes, and workflow-to-skill-converter to preserve reusable methods.

Assets and Templates

• Competency map template
• Synthetic worked example
• Weak example

Sources

This skill is an original AIPOM synthesis of competency-based development, applied learning, and evidence-based role design.