Innovation Competency Model

By Mikkel

Last updated April 23, 2026

An innovation competency model describes the knowledge, skills, and behaviors people need to contribute to innovation work at different levels. It helps organizations define what good looks like for roles such as innovation analyst, innovation manager, venture builder, design lead, and portfolio owner.

Common competency areas include opportunity sensing, customer discovery, experiment design, facilitation, commercial judgment, technical fluency, portfolio thinking, stakeholder management, and evidence-based decision-making.

Why It Matters

Innovation roles are often ambiguous. A competency model makes expectations visible, supports hiring and promotion decisions, and helps teams identify skill gaps before launching new initiatives.

Practical Example

A technology organization might define three levels of innovation competency: contributor, lead, and portfolio owner. Each level describes expected behaviors, such as running interviews, designing experiments, coaching teams, or making investment recommendations.

FAQ

How Is an Innovation Competency Model Used?

It is used for hiring, performance conversations, capability assessments, training design, and career planning.

Should Every Company Use the Same Model?

No. The model should reflect the organization’s strategy, maturity, industry, and operating model.

Contributor

Mikkel @mkl_vang

Covers operational innovation, AI implementation patterns, and how teams ship useful change without theater.

Mikkel writes from an operator perspective. He is interested in what happens after the strategy deck: staffing constraints, decision latency, governance friction, and the daily tradeoffs that determine whether innovation initiatives survive contact with reality. His reference base includes the OECD Oslo Manual, the NIST AI Risk Management Framework, and Google Re:Work.

His pieces often combine process design with clear implementation checklists, especially around AI adoption and cross-functional delivery. He likes explaining how high-level frameworks can be adapted to smaller teams with fewer resources by drawing on practical standards like the OECD Oslo Manual, the NIST AI Risk Management Framework, and team practices from Google Re:Work.

When reviewing content, Mikkel prioritizes precision over hype. If a recommendation cannot be tested in a sprint or measured over a quarter, it usually does not make the final draft.