When it comes to innovation portfolio design and management, it is imperative to recognise the dichotomy in the approaches, skills and metrics required to explore, test and develop new products, services, value propositions and business models; and to scale, optimise and exploit those that already exist within the organisation.
The disruptive potential of AI is perhaps unprecedented, and while the popular media focuses on a dystopian vision of the future, AI is augmenting our human capability perceptually, cognitively and physically, transforming everyday experiences as well as enabling a collaborative human and machine led transformation.
Businesses that hone their ability to design business experiments, as well as their expertise in data analytics, and incorporate experimentation as a necessary step in the process for approving new concepts, initiatives and ideas, are far better equipped to challenge convention and innovate with confidence.
Customer insights are formed by interrogating data, facts and observations and connecting empathically with customers' contextual needs. There are no shortcuts to deriving genuine customer insights. Customers don't always do what they say. Ethnographic research and techniques such as contextual immersion are the key.
A tenet of design thinking is that making visual representations and prototypes facilitates communication of concepts and ideas. The goal of data visualisation design is to communicate information in a clear and compelling manner that conveys the impact of the true meaning behind the data.