Opening Image by Ashley & Jered Gruber.
Open Innovation has become a widespread way to boost R&D in our search for the solutions of the future. We find it across industries, problems, and technologies; it is very visible in company websites, challenges, conferences, and professional media posts. Breaking organizational boundaries leads to many benefits, but it also comes with multiple challenges to overcome. Built on our experience with R&D and open innovation projects at PickleTech, we describe here some patterns we believe are key to reaching impactful solutions with Open Innovation.
The Grand Schema
In Open Innovation processes we pursue the generation of new knowledge, solutions, and profit streams by breaking organizational boundaries [1]. Open innovation involves a collaboration between multiple organizations. In the process, we have to deal with the problem we aim to solve, and the technological aspects related to it. But we also need to respond to the communication and cooperation challenges that arise when involving multiple actors in the process.
As this post is built on our experience at PickleTech, we focus on Open Innovation projects that target R&D of new products and services leading to their operationalization. This is in contrast to other cases where Open Innovation may target marketing or scouting purposes as alternative examples. We talk mostly about solutions where the use of Data Science, Artificial Intelligence and Machine Learning are a fundamental piece of the innovation process.
The solutions we have developed are diverse, see other posts in our blog [2]-[3]. But they usually combine our Data Science expertise together with teams in the Health, MedTech, and DeepTech domains. We started these projects within both inbound, outbound, and coupled Open Innovation contexts [4]-[5].
Involve the Right People
A defining aspect of Open Innovation is the need to engage people from different organizations. It is no surprise that empathy and proficient communication practices are a must to move any initiative forward. We require these to build the needed trust between the people and organizations working on the R&D of the solution. We discuss here a couple of more subtle aspects we pay particular attention to at PickleTech when facing Open Innovation projects.
Operations People
When the scope of a Open Innovation project is to develop and operationalize a solution, we try to involve the people next to the operation of the future solution as soon as possible in the process. Their value can’t be emphasized strong enough. The insights operation’s people provide in every step of the development, and the way we promote their trust in the solution are what, sooner than later, determine if the solution is getting to production down in the initiative project pipeline.
Operations people take different roles and names depending on the context: for instance, emergency practitioners if we talk about solutions in ambulances or ERs [3]. Radiologists if the solution assists specialists on complex diagnosis [2], [6]. Or omnichannel experts if we are talking about a solution for GTM teams. We never separate innovation from the problem context!
Interdisciplinary
There is certainly a lot to gain from interdisciplinary teams and diverse perspectives in the collaboration. This is another trait we foster when assessing the convenience of a partner for an Open Innovation project. While there must be obvious compatibility in the goals and expectations of each partner, skills and domain complementarity become very powerful ingredients to boost the creativity and the impact of R&D of a solution.
In that sense, due to the already complex nature of scouting talent in initiatives like Open Innovation Challenges, it is normal to pre-define a preferred set of characteristics for our partner. In this process, we have found it turns out to be better not to be overly strict and focused on very specific domain compatibility, but rather leave room for expertise complementarity.
Both of these aspects added together, have a combined enhanced positive effect for Open Innovation. Complementarity provides a means to more critically assess the validation of the solution advances, as the process is enriched by the different team perspectives: from operations people to data science perspectives. We should not underestimate the power of proper validation.
Share Goals & Expectations
While setting and sharing clear goals and expectations is not a guarantee to success in Open Innovation projects, the contrary is certainly a recipe for failure. The type of perseverance that is required from every team member and every organization participating in the co-development of a new R&D solution, is only feasible with a common understanding of goals and motivations. As friction, difficulties, and roadblocks appear, trusting the perspectives of others in the development team, and understanding their roadmap, enables us to find ways to move forward.
Value each one in the process
One of the biggest challenges in this process is related to the difficulties behind mixing organizations from distinct backgrounds, distinct ways of functioning, different sizes, and ambitions. Many times the focus is put only on how the IP and/or other benefits of the solution are going to be distributed across the collaboration. That is of course very important. But it is very hard to get to a point where benefits are to be distributed, if on the pathway the collaboration members have been oblivious to the drivers and needs of their project partners.
It’s Innovation, think beyond the schema!
Combining multiple years of experience in R&D and impactful innovation at PickleTech, we are still recurrently surprised by how immovable are certain open innovation initiatives. It is Innovation! So long as the goals are clear, allowing for room to depart from the predefined roadmap might lead to more impactful solutions. This applies to every aspect in the Open Innovation process: from the initial partner search, to the data-set requirements, or the specific techniques to be considered in the R&D process.
If the goal is to provide real innovation that ends in operation, the co-development method is more important than the arbitrary criteria sketched in the initial checklist. Let the humans perform on what they do best: continuously learning and adapting.
Powered by Data, Driven by Science
Open Innovation has become a well accepted process to boost R&D of new products and solutions. Understanding who and how to mix in the innovation process is complex. And this means that at the same time, when the right formula is found, Open Innovation becomes the perfect recipe for success.
At PickleTech, we work developing innovative solutions to improve competitive aspects related to Health, MedTech, and DeepTech. We believe Data Science and advances in Machine Learning coupled with domain knowledge and experimentation have the potential to provide new tools to better understand, monitor, and systematically improve the competitive performance of organizations.
[1] Open Innovation: A New Paradigm for Understanding Industrial Innovation, Henry William Chesbrough, https://www.researchgate.net/publication/241712316_Open_Innovation_A_New_Paradigm_for_Understanding_Industrial_Innovation
[2] Machine Learning for Medical Devices, PickleTech: https://pickletech.eu/blog-medical-devices/
[3] Data Science for Emergency Medicine, PickleTech: https://pickletech.eu/blog-emergency/
[4] Open Innovation in Times of Crisis: An Overview of the Healthcare Sector in Response to the COVID-19 Pandemic by Zheng Liu, Yongjiang Shi, and Bo Yang, https://www.mdpi.com/2199-8531/8/1/21
[5] Open Innovation – What It Is and How to Do It, Atte Isomäki, https://www.viima.com/blog/open-innovation
[6] Multimodal AI for Brain Disorders, PickleTech: https://pickletech.eu/blog-brain-conditions/