Jay van Zyl @ ecosystem.Ai


Innovation models are diverse, with the options and approaches companies can follow when implementing innovation programs being highly dependent upon their specific circumstances. However correctly gauging the nature of these circumstances can be especially difficult given the diverse and complex social, organisational and governance structures that have come to be deeply embedded within the culture of a company.

Over the last several years social networks have educated people to use information about themselves and their friends more actively. This has caused a spill-over effect in organisations where the benefits of social networks can be contrasted against formal structures to deliver more effective and efficient innovation practices. Innovation Agency is at the forefront of developing analytical tools and approaches that enable us to identify these ‘shadow networks’ and leverage them effectively in order to energise collaboration and stimulate innovation across a broad range of industries.

Here is an example view of the Microsoft’s acquisition of LinkedIn can be viewed as a key strategic drive in ecosystem innovation.

The creation of an innovation portfolio rests on the ability to recognize your capabilities and offerings and visualize their potential impact on the market. The innovation portfolio is the foundation for growth and success. To understand the emergence of a successful innovation portfolio, place it in context and view the processes leading up to its establishment systemically. The innovation portfolio is a means to assess your abilities as an entity operating within a larger landscape, and based on these abilities it assists in directing your actions toward a conceptual future—where you should be. The creation of the innovation portfolio sits within three paradigms forming a holistic and systemic view of the world and how you make sense of it.

The three paradigms are: your understanding of the external landscape, your ability to use this understanding to transform the way you think and motivate your own actions, and the manifestation of these understandings in your future actions as you move toward your place in the conceptual future.

The first paradigm, viewing your landscape holistically, consists of your ability to reframe your views. Companies need to consider the current trends of the market, the actions of your competitors, and the changes within different spheres of the landscape such as technological change, hype curves, and innovation assimilation. They also need to understand social changes that occur, such as a move toward co-production, crowdsourcing, and swarm theory, as well as how innovation has previously disrupted the landscape and how it has been adopted in the past. The first paradigm consists of the initial undertakings of an entity and feeds into the second paradigm: applying these understandings to an understanding of the self.

The second paradigm is based on the entity’s acknowledged position in relation to the market in intends to target, as influenced by the external landscape or ecosystem. By understanding how the external environment affects it, an entity can visualize who its intended market is with clarity and with insight into how the market is affected by its landscape. This feeds into the development of an ecogenetic understanding as well as the development of an embedded philosophical mantra, both of which form the groundwork for the establishment of an innovation ecosystem.

The paradigm of the model dealing with impact consists of incremental, disruptive, and radical impact, each affecting innovative growth differently. The approach, which encompasses your offerings, capabilities, and business model, and the impact it has, creates a means to view your innovation portfolio.

Prediction models can augment your portfolio by taking an automation view on your capabilities required to run the business. Intelligent automation is used to remove the areas where human error and inefficiencies are most prominent. A financial services example shows how machine learning and related artificial intelligence related algorithms can be used to increase business performance.