Open innovation - when a flash of inspiration strikes from outside!
Reinhard Willfort
November 17, 2024
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“Disruption is a process, not an event, and innovations can only be disruptive relative to something else.”
Artificial intelligence tools will also change innovation management, but can this also achieve a disruptive dimension? The idea that innovation processes could run autonomously in the background without human intervention has been circulating in the innovation scene for a few months now. Since corporate culture stands out as the top 1 answer in almost all studies on the question “What does successful innovation need?”, I approach the topic with a certain degree of skepticism. In principle, however, I also see opportunities to gain additional resources for innovation through AI, even if it is only to increase the efficiency of routine work in order to free up more time for creativity and innovation.
If it were possible to build an autonomous innovation machine, it would largely replace traditional innovation management. In this case, we would be talking about a disruptive innovation of innovation management itself. Using generative AI as a form of creative AI, it is already possible today to have ideas generated by the machine. As with all ideas, the question of whether these ideas are good lies in the eye of the beholder: what is exciting and brand new for one person may be boring for another. Even if creative AI is currently still inferior to human creativity, it is already fundamentally a disruptive innovation.
A disruptive innovation is an innovation that usually begins with a lower level of quality in a specific area of expertise. One effect is that this innovation is overlooked by established companies due to its low level. As a rule, however, these tools become better through application and further development and displace the established methods and tools. In which areas could this be the case in innovation management? I will try to describe my thoughts on this using a classic innovation process from idea generation to idea utilization:
Together with my team at isn, we built the first inspiration engine back in 2009, which networked different data in a similar way to today's AI solution and provided users with creative impulses in the form of texts or images. After initial discussions with companies, we put the tool back in the drawer. At that time, innovation management (if it existed) in many companies was not yet receptive to such a tool. Today, there are already some apps that are able to formulate ideas on the basis of ChatGPT, e.g. seenapse.ai. In addition to inspiration, AI tools could also be used to network creative people in order to form creative teams.
The internal evaluation of ideas in companies used to have the disadvantage that only a few minds were able to think and decide. Crowdsourcing and innovation platforms have significantly improved this situation. Ideas no longer disappear into the “inbox”, but are discussed and further developed transparently by many creative minds. A community evaluation of all ideas in an idea challenge brings the best ideas that resonate to the surface. Here, AI could now have the function of networking ideas with ideas and then with users in order to discover dormant innovation potential and bring it to fruition.
The creation of prototypes, especially for digital solutions, can also be supported by AI tools. Websites or program codes can also be generated automatically. This means that the realization of ideas can be initiated without major hurdles and the basis for industrial production or personalized individual production of products can be laid. At this point in the design thinking process, user tests are carried out under conditions that are as real as possible. AI could help here by means of synthetic personas (i.e. user personas generated by AI) in order to obtain rapid feedback.
The market launch is the moment of truth and in the past was often associated with the painful realization that nobody needs “this thing”. There are already AI tests that “listen to the market” before the launch, e.g. in social media discussions. This refers to AI tools that are able to detect and link traces from social media or other sources and use them to create patterns that provide conclusions about customer wishes, application feedback and more. These insights can also be the starting point for future strategies and creative processes.
Conclusion: The variety of AI tools is growing by the hour and is already providing interesting assistance systems and digital support for the discipline of innovation management. I see a good opportunity, especially for the strategy process in companies - as a basis for innovation - to deal comprehensively with trends and thus recognize the need for innovation at an early stage or proactively set disruptive impulses. The “autonomous innovation machine” is an interesting vision at the moment. We will see how the topic of AI develops in the innovation sector and how quickly this vision could actually become a reality.
Which AI tools are you already using in innovation management?
I look forward to your feedback and, as always, wish you a relaxing and creative Sunday!
Reinhard Willfort, Innovation Doctor, www.willfort.at Let’s innovate together!
Tip for support with AI issues:
The network of 228 European Digital Innovation Hubs (EDIH) pools expertise in innovative technologies and artificial intelligence in particular. 151 EDIHs provide basic funding for the uncomplicated support of companies and the public sector in digital innovation. The EDIH Crowd in Motion deals with the technological connection of human intelligence with artificial intelligence and uses crowdsourcing tools for this purpose.
