Innovation is a paradox: it requires impetus and planning, and produces unexpected results – which, in retrospect, seem inevitable.
Experience Innovation (TM) from Tolksdorf.digital is a customizable, structured framework for successful innovation projects that combines openness, creativity, and a culture of error with AI and systems thinking. The framework is based on 12 areas of impact that have a decisive influence on the success of projects. It is worth thinking together about what this means for your own projects. Taking these areas of impact into account helps to make innovation more likely to succeed in different contexts – from small improvements to disruptive reinvention. They combine technological tools – especially AI – with human creativity, a culture of error, and systems thinking. The model is based on findings from science, management theory, and many years of innovation practice.
In addition, the role of artificial intelligence (AI) was highlighted in italics.
Overview
Definition of Innovation
Innovation Culture: Innovation requires an inspiring guiding principle and open communication
Innovation can be learned and realized in all situations
Innovation is an ongoing joint task
Not all challenges are solvable, but they can be addressed
Innovation requires applied competence and artificial intelligence
Innovation and experience only grow together, guided by intelligence
Innovation requires creativity, verification, and reflective intuition
Innovation is holistically linked to a system (Innovation Context)
Mistakes are an inevitable invitation to improvement and innovation
Innovation can be encouraged but not stopped
Innovation in an unpredictable future
A cautionary note to conclude:
“What is liked will be done” versus Change Management
Definition of Innovation
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Innovation is a change that is viewed and evaluated holistically. It can be applied to existing or new things. It results in:
- novel, sustainable business benefits for all parties involved
- technological improvement or general progress
- user-friendliness
- AI provides tools for all project and implementation phases of innovations.
Innovation Culture: Innovation requires an inspiring guiding principle and open communication
- What is liked will be done. An innovation that generates positive feedback has the best chance of being implemented and having a lasting impact. Open communication on an equal footing allows all insights to be taken into account and increases shared motivation.
- AI helps everyone involved to prepare for specialist dialogues, link knowledge, ask specific questions, and engage in the joint learning process.
Innovation can be learned and realized in all situations
- Successful innovation requires curiosity, practice, structures – and the willingness to deal constructively with uncertainty and ignorance at all times and in all circumstances.
- Free AI tools, open-source software, and structured approaches (lean innovation) enable innovative solutions even where resources are scarce.
Innovation is an ongoing joint task
- Innovation is not a one-time goal, but rather an open attitude—implemented in a continuous innovation process.
- AI supports innovation in reflective work with context, goals, and implementation.
Not all challenges are solvable, but they can be addressed
- „You can't solve a problem if you're not willing to have it.“ ~Bill Burnett
- AI helps those involved to view such situations not as obstacles that need to be resolved in advance, but as peripheral conditions that do not hinder progress toward a higher-level solution.
Innovation requires applied competence and artificial intelligence
- „The ability to predict the consequences of actions is what constitutes intelligence.“
~Russell L. Ackoff - ...regardless of whether it stems from experience, intuition, or machine learning. AI is an indispensable part of innovation processes thanks to its immediately available knowledge and high application speed.
Innovation and experience only grow together, guided by intelligence
- Experience is the basis for innovation, and innovation in practice becomes experience.
- AI has a dual effect on innovation: as a control mechanism for experiences and as a creative inspiration for innovations. Together, these two factors lead to viable solutions.
Innovation requires creativity, verification, and reflective intuition
- Intuition can inspire innovation – but its power only unfolds in interaction with critical evaluation and verification.
- AI accompanies innovation to develop and verify ideas – constantly switching between exploration, critical evaluation, and intuition based on large amounts of data. Freely adapted from Karl Popper's scientific theory.
Innovation is holistically linked to a system (Innovation Context)
- „You cannot optimize a system by looking at its parts in isolation.“
~ Russell Ackoff - AI can be used very effectively to optimize isolated systems. The creative and responsible optimization of the overall system remains the preserve of humans.
Mistakes are an inevitable invitation to improvement and innovation.
- Application model – Four stages of innovation
1.) Functional innovations: 1:1 replacement without changing processes
2.) Business area innovation: Optimization within an isolated department
3.) Cross-area/interdisciplinary innovation: Changes that connect teams, silos, or departments
4.) Disruptive pioneering innovation: Rethinking business models, markets, or technologies.
- AI can provide support at all stages.
Innovation can be encouraged but not stopped
- “Whatever is conceivable will be conceived.” ~ Unknown author
- AI helps to open up spaces for thought, search globally, name sources, and embed them in guiding principles. AI can also help to consciously deal with undesirable effects.
Innovation in an unpredictable future
- "The future is uncertain – the context is a boundary condition, not a constant. Three possible scenarios for innovation planning:
1.) Everything is going according to plan..
2.) The whole context is changing, and the existing Plan 1 is completely inappropriate.
3.) Innovation planning free from constraints imposed by the current context, this helps with scenario 2." Freely quoted from Bill Burnett. - AI can help analyze the current context and planning and support the search for robust new solutions.
Impact Domains and Innovation Process Models
Vergleich ausgewählter Ansätze "12 Wirkungsfelder der Innovation" vs. "Doblin – Ten Types of Innovation" vs "Design Thinking" vs "Oslo Manual" vs "McKinsey 7-S"
Die folgenden Ansätze sind nur teilweise direkt vergleichbar. Die 12 Wirkungsfelder der Innovation beschreiben einen Wirkungs- und Reflexionsrahmen: also Faktoren, die das Gelingen von Innovation beeinflussen. 7C-CI/CD beschreibt dagegen ein Vorgehensmodell für die praktische Gestaltung und Umsetzung von Innovations- und Veränderungsvorhaben. Weitere bekannte Ansätze wie Design Thinking, Doblin, Oslo Manual oder McKinsey 7-S lassen sich deshalb am sinnvollsten einordnen, wenn zwischen Wirkungsrahmen, Vorgehensmodellen und Ordnungs- bzw. Referenzrahmen unterschieden wird.
Kernaussage: Die 12 Wirkungsfelder beschreiben nicht, wie Innovation Schritt für Schritt durchgeführt wird, sondern welche Faktoren bei Innovation wirksam werden. 7C-CI/CD beschreibt dagegen, wie Innovationsvorhaben praktisch, konstruktiv und gemeinsam gestaltet werden können.
A cautionary note to conclude:
“What is liked will be done” versus Change Management
“What is liked will be done” – Why I warn against change management.
Many change projects fail because they make change an end in itself. The term “change management” sounds professional, but it is often just a label for frustrating programs:
They point out mistakes, generate resistance, and demand behavioral change instead of sparking enthusiasm.
Experience Innovation right away:
An inspiring goal motivates those involved to set off immediately.
People are happy to get involved when innovation is enjoyable, meaningful, and enables participation—then change happens almost by itself.
That's why my guiding principle is:
"What is liked will be done.
Success is achieved through motivation – not through change management."
Rainer Tolksdorf
Innovation Engineer & Founder of Tolksdorf.digital
References
German sociologist and founder of modern systems theory
- About Niklas Luhmann, who understood society as a self-organizing system of communication.https://de.wikipedia.org/wiki/Niklas_Luhmann
Management and organizational theory
Beyond Continuous Improvement (Systems Thinking)
Philosopher and Professor of Logic and Scientific Methods
According to the scientific theory established by Karl Popper, progress in knowledge is achieved through trial and error (...) Falsificationism therefore assumes that a hypothesis can never be proven, but can be refuted if necessary.
Computer scientist and professor of mathematics and mathematics didactics
How can AI language systems such as ChatGPT function as learning companions for students?
Executive Director Design Program + Adjunct Professor Mechanical Engineering, Stanford
Bill Burnett TED Session «5 steps to designing the life you want“
Physicist, statistician and pioneer in the field of quality management
- Plan-Do-Study-Act (PDSA).https://www.youtube.com/watch?v=_BCPdIHsPog
- The 5 deadly diseases
https://www.youtube.com/watch?v=ehMAwIHGN0Y
Psychologist and university lecturer, winner of the Alfred Nobel Memorial Prize
Daniel Kahneman explains what intuition really is
Digital Innovation Mentor, Founder and Owner Tolksdorf.digital
- Professional practice and specialist lectures for Quadriga University, as well as contribution as co-author of “Market innovation requires a change of perspective." (Hardcover ISBN 978-3-946286-11-0).https://www.linkedin.com/showcase/68536346/
- Developer of ExperienceInnovation and founder of Tolksdorf.digital
https://tolksdorf.digital/profil-rainer-tolksdorf-de