Client:
ZHAW Institute of Innovation and Entrepreneurship — in collaboration with Prof. Dr. Martin Feuz
Services:
Innovation Strategy, Framework Development, Evaluation Methodology, Portfolio Management, Decision Science, Iterative Concept Development, Academic Knowledge Transfer
In many innovation organisations — particularly in large, regulated companies — opportunities are identified faster than they can be evaluated. Teams lack a shared basis for deciding which initiatives are worth pursuing, and leadership lacks a transparent way to communicate those decisions across the organisation.
This pattern is well documented in both corporate and start-up innovation: most projects are pursued for too long and only fail during scaling, not because they were poorly executed, but because they should never have been pursued in the first place. The cost of late termination is high, and the absence of a shared decision logic compounds it.
The objective of this collaboration with ZHAW was to develop a framework that supports innovation managers operationally in their portfolio work while at the same time offering leadership a clear basis for Go/No-Go decisions under uncertainty — particularly for AI initiatives, where the evidence base is often immature and the temptation to commit prematurely is high.
In collaboration with Prof. Dr. Martin Feuz, ZHAW Institute of Product Management, with practitioner input from innovation leads across major Swiss organisations.
I was involved across multiple development steps, from conceptual structure through iterative testing to some support in integration into the academic curriculum. My role focused on practical applicability and usability, ensuring that the framework would be usable by innovation teams in day-to-day portfolio work and at the same time defensible at management level.
Responsibilities included:
The framework was developed iteratively over several years, combining academic research on evidence-based innovation with practitioner experience from corporate and start-up environments.
The starting point was a set of recurring questions that innovation managers and leaders face: How do we prioritise? How do we make difficult decisions? How do we deal with uncertainty? How do we steer a portfolio? Mapping these decision moments revealed a consistent gap — a missing shared logic that could carry both the operational work and the executive conversation.
The framework was built around two strategic axes. Potential captures impact potential (annual problem size, customer segments and need type), market potential and timing. Challenge captures the competitive landscape, riskiest assumptions and execution risk. Each dimension is assessed against an explicit evidence level, from anecdotal observation to validated research, making the underlying confidence of every assessment visible.
The framework was tested with practitioners across multiple iterations, including innovation, UX and product leads from Swiss public transport, healthcare and other regulated sectors. Practitioner input shaped both the simplification of the assessment logic and the visual design of the assessment sheet, so that the tool could be applied without specialist training.
The final framework consolidates the assessment into the Opportunities Worth Addressing Map, a 2×2 matrix that visualises the relationship between Potential and Challenge alongside the supporting evidence level (Red, Orange, Yellow, Green). This makes portfolio decisions communicable at C-level without flattening the underlying complexity, and gives leadership a defensible record of why specific opportunities were pursued or paused.
The framework is now a core component of the CAS "Mastering AI-driven Innovations" at ZHAW and is used by organisations to focus their innovation portfolios on opportunities with the strongest evidence base.
Its central value lies in making innovation decisions traceable and consistently communicable across the organisation. Innovation managers have a shared language for prioritisation. Leadership has a defensible basis for Go/No-Go decisions. Portfolio quality improves over time as the evidence base accumulates across initiatives, and difficult conversations about pausing or stopping initiatives become easier because the criteria are explicit and shared.
By translating uncertainty into a structured decision logic, the framework helps organisations stop pursuing the wrong opportunities earlier and commit to the right ones with more confidence.
