Workshops

What were innovations that have changed our lives and during which key moments did we feel proud contributing to an innovation? Exploring the intrinsic nature to innovation alongside personal experiences, we can derive implications of traits and practices that foster innovation. Establishing a fundamental innovation mindset, we are ready to start the journey.

Every organization has established innovation practices that contribute value. Revisiting these practices and their limitations together, directly leads us to the gap in methodology that Adaptive Innovation addresses and how, what has proven worthwhile in the past, is going to be become even more useful in the context of Adaptive Innovation.

While other methods use domain specific concepts to describe what matters to be a successful innovator, Adaptive Innovation consolidates the identical core of all successful methods in one common approach with one common language. Giving and outlook on how these principles come to life, the first steps towards action are already laid out.

Teams and organizations regularly misjudge or unknowingly disagree on their current innovation stage. Understanding, agreeing and explicitly stating the current innovation stage is a necessary prerequisite towards defining innovation objectives that are framed correctly and everyone can commit to.

Meaningful innovation objectives start with stating and prioritizing key stakeholders of an innovation. In a user-centric approach that will always include the primary beneficiaries of an innovation. Beyond that those providing budgets and investments as well as different bodies and partner of an organization are regularly found at the heart of success.

To define highly relevant innovation objectives, it's important to understand what the previously defined stakeholders are looking for in the innovation, but cannot find at the moment. We call these gaps stakeholder aspirations and provide a systematic approach to identify the most critical aspirations without overlooking the elephant in the room.

Now we are ready to phrase out innovation objectives that are correctly framed with respect to the current innovation stage, explicitly name the innovation stakeholders that we can validate progress with together, including a clear expression of their aspiration, connected to fundamental conditions of innovation success.

The innovation objectives defined earlier are going to be fundamentally different with regards to involved activities, tooling, roles, touch points and responsibilities. Therefore, we each identified innovation objective deserves its own learning and communication model that describes how progress is ensured alongside an iterative validation process.

An effective learning model needs a key leading indicator that indicates progress. Finding that indicator is the key to truly validation based approach that creates transparency while keeping the team engaged and motivated. There is a lot to be said about what makes a good indicator, incl. how to avoid vanity metrics, eliminating biases and leveraging cross-validation.

One of the key benefits of Adaptive Innovation is that nobody needs to start from zero and everyone can bring in the innovation tools that have proven themselves in the past. There is a growing library of toolboxes shared among innovators to draw from and no limits to extend them. Giving life to the validation process typically starts with selecting tools that will drive the learning model.

In a final step, tools are connected with each other through supporting activities. The result is going to be a circle that explains how assumptions and insights flow through a mind, team or organization. Crucially the emphasize is about learning about the big picture and understanding how strategy and priority are going to shape over time based on systematic insight generation.

Progress towards an innovation objective is an iterative process. Each iterations, starts with assumptions that need to be turned into insights with a specific validation target or learning goal in mind. Phrasing out these assumptions and defining an open-ended yet tangible target is a science in itself.

Towards the end of an iteration, insights converge, need to be summarized, weighted and conclusions need to be drawn for strategy and prioritization. Cognitive biases, communication deficiencies and conflicting goals are major sources of error during this critical phase where the actual learnings materialize and manifest.

During every iteration, we generate learnings about our innovation process itself, about the tools we used, how we communicated and how our expectations compare to the result of an iteration. Conclusions can be to continue working on the same target with a different approach, revisiting the learning model or to revisit the innovation objectives themselves.