Innovation in the digital era, an adaptation capability?
From one view point, a game is just a search (perturbed by opponents) in which the object is to find a winning position. The complementary viewpoint is that a search is just a game in which the moves are the transformations (choices, inferences) permissible in carrying out the search.
“Every game is a search, every search is a game”
— John Holland, Adaptation in natural and artificial systems
In this new environment where change is part of an organization’s daily activities, mobilizing necessary energies to define and design their adaptation capability is a challenge. This environment is already ruled by new modes of innovation enabled by digital technology. Digital infuses every historical innovation practice and allows us to regenerate them or make them evolve. For instance, our concept generation and idea exchange capabilities used to be limited to the meeting room. Today those capabilities are dematerialized and take place on corporate social networks and other specialized platforms. Moreover, rather than simply adapting to the environment, companies and their environment are now 2 complementary innovation and interaction dimensions. Simultaneously modifying these 2 dimensions enables companies to develop new adaptation capabilities and to redefine who can and should finally adapt.
During the past 2 years, WDS in collaboration with organizations, has explored the potential of customer experience, aligning the experiences of both future and current final users, as a common horizon for all the organization’s actors. Having a common horizon is key to manage organizational change, the experience environment and all the stakeholders involved. We published a book on this topic at edition Eyrolles.
This exercise required a deep dive into the different modes of innovation needed to deal with the complexity of those transversal customer experience projects. Technological, process, use, business model and in fine organizational innovation are all dimensions on which organizations should learn and explore, not one or the other.
It is better to manage change rather than be passive. However, new organizational and thinking modes will enable enlightened management whether in situations of incremental changes to improve performance or breakthrough innovation to reinvent your activities. Uber is often cited as an example for its disruption capabilities, less often for its permanent learning capabilities. Did you know that all the support teams at Uber (marketing, operations,…) share on a common platform the processes they reinvented and challenge them to improve them? This way, teams make their processes evolve continually.
In her latest book Peers Inc, Robin Chase, a leading actor of the current redefinition of mobility, introduces the notion of “exponential learning” enabled by digital technologies. One of the examples detailed in the book is the language learning app Duolingo. At Duolingo, the teams endlessly test learning journeys on hundreds of thousand of users and develop new more efficient ones. Developing those new exponential learning capabilities and applying them to the innovation process could allow organizations to continuously adapt and redefine the way they innovate.
Examples of incremental innovation exponential learning are numerous and they participate to some of the greatest success in the digital economy. Learning objects and services are becoming part of our daily life. Smartphones now have predictive keyboards capable of answering for you based on your past conversations. The common objects in our environments are also gaining new functionalities. For instance Amazon’s speakers, Echo, transforms into a digital controller thanks to its API. Or Telsa’s vehicules “automatic driving” feature is acquired by a software upgrade. This phenomena is spreading to the industry as well. Industrial robots are now capable of introducing learning into their processes. Those learning capabilities are now part of those objects, services and processes. In light of those examples, one question comes to mind: What if the organizations producing those objects and services became themselves learning organizations as made possible by digital technologies?
Scientific literature on the subject of the learning organization has expanded rapidly in the past three decades. Beyond theoretical implications, it seems that a new paradigm becomes possible in the era where technology facilitates exponential learning at the process level. This new paradigm gives a new meaning to the learning organisation. What if our innovation and design processes were now part of an ultra-fast iterative learning process? This could be a way to continuously redefine the best design processes whether incremental or breakthrough in a similar fashion as Duolingo.
In the case of Duolingo, teams look for ways to reduce the time spent on learning a new language and to improve user experience. New processes and learning journey hypothesis are rapidly tested by new users. This enables Duolingo to build new more efficient processes and user journeys. Our organizations can learn from leaders in the digital sector and rapidly test new hypothesis on their best user journeys (employees, partners) and innovation processes or inversely improve their idea generation capabilities to challenge the status quo. Whether you explore incremental change (for instance process improvement measured with established KPIs) or breakthrough change (for instance new processes with new evaluation criterias) these new learning capabilities open new horizons.
During a recent team reading session, we read John Holland’s writing. His hypothesis have influenced academic research in the field of artificial intelligence for the last 25 years. They could also be applied to redefine how we look at organizations for which adaptation capabilities are constantly evolving.
In his book Adaptation in natural and artificial systems, John Holland writes “adaptation designates any process whereby a structure is progressively modified to give better performance in its environment”. He goes on to develop a mathematical framework which could inspire organizations aiming at unifying their reflexions on adaptation capabilities and mechanisms at the core of numerous sciences (psychology and learning capabilities, economics and optimal planification or artificial intelligence).
Adaptation capability is a core competence for organisation in the sense of Sanchez, Heene & Thomas capacity definition (1996): “a resource that increases the ability to sustain the coordinated deployment of assets in a way that helps a firm achieve its goals” to turn organisations into “learning organizations”.
We are entering an era where different forms of innovation converge to help our current pace of innovation. In this era, breakthrough innovation and incremental innovation will coexist on ever closer time horizons.
WDS’ activities focus on efficiently learning together and cross-fertilization of knowledge in order to manage concret and informed innovation initiatives.
Starting from an incremental innovation topic — where the reducing the uncertainty of given hypothesis will be central — or breakthrough innovation — where enlarging the field of hypothesis is of the essence we propose 4 strategic key points to start your innovation exploration:
1 — How to reduce organizational maturity uncertainty?
In other words,is my organization mature in light of my innovation ambitions and challenges?
Organize a quantified management of your organization’s capabilities development around the design or adoption of a new process.
Take for example the innovation process or more operational ones which are key for your adaptability, then:
- Start with identifying the key steps in the process of your next project or a project you are currently working on
- Define each level of incremental improvement (how much should I improve the current process) versus the degré of exploration (to which extent I should rethink the existing process) according to your needs
- Define the learning stakes at each stage and write down your hypothesis
- Decide which indicators you will use and apply them to the different stages of your learning
- Finally, identify at each stage the pain points and the transformation opportunities. Prefer the ones who are easy, impacting and short term versus medium and long term.
- Redefine the stages and generate a new process, its content and the stages. Compare its efficiency to the historical process. The devil is in the details.
2 — How to reduce the use value uncertainty in my projects?
In other terms, question the gap between the estimated user value and the value actually delivered.
Increase your organization’s capabilities in order to create minimum viable knowledge of the key users of your projects (internal or external).
Which are the best practices, methodologies and knowledge acquisition practices and how could I improve them? Ask around you for the person in your organization in charge of user knowledge management. If no name comes up, you should lead the reflexion!
3 — How to reduce the business value uncertainty of a project?
Adopt minimum viable knowledge strategies for your stakeholder ecosystem and align the expectations of the different actors.
Pertaining to the use value, you should :
- ask what is the business value for each stakeholder
- adopt design strategies which maximize business value. We use three analogies to guide you: design strategies for adoption, habit transformation or breakthrough and generation of linked knowledge*
4 — How to reduce the technical or technological feasibility uncertainty of a project?
You can reduce feasibility uncertainty with fast prototyping of all the dimensions of a project. For instance, you could prototype the journey of one of your user linked to the technical problem or the journey of one of your employees, just like Duolingo does.
Challenge the status quo. Everybody knows how to put a fire out with water but doing the same thing with sound is less common.
Finally, cross knowledge and universes like the MIT Mediated Matter lab and the Neri Oxman’s ted talk exposing research at the intersection of computational design sciences, additive manufacturing (new production processes based on addition), material engineering and synthetic biology.
We have seen open innovation as a growing learning capability for our organizations, from the previous decade. Explore your open innovation strategy along those 4 angles in our “journey exploration diagnostic” and discover some additional material.