Much of the philosophy of modern business management processes, to which innovation is a companion, trace their routes through the Toyota Production System, and earlier to the PDCA (plan-do-check-act) model, attributed to Walter Shewhart in the 1920s, and W. Edwards Deming in the 1940s. Along with other pioneering industrial thinkers, such as Frederick Winslow Taylor, Henry Ford and Peter Drucker, the Models and Frameworks listed below take a lot of the lessons of these early strategists and reshape them for modern contexts.
Perhaps the biggest influencing factor that has shaped today’s innovation methods is the Digital Revolution, with the birth of software, and subsequently the internet. This broadly manifests as a move from atoms to bits, which brings with it a whole new set of priorities, goals, risks, threats and opportunities. But you don’t have to be building software to benefit from these modern models, they are widely applicable to different contexts, from social activism to national government.
New innovations are typically envisaged as going through the Technology Adoption Life Cycle, or Innovation-Adoption Curve, as they are progressively used by different maturities of audience, from the initial Innovators to the eventual Laggards. So, in a very basic sense, an innovator can think of their challenge as being one of driving the innovation through these audiences.
Technology adoption life cycle
Let us then consider another popular conceptual curve, which traces the typical growth pattern of a startup:
– From Paul Graham, via Business Insider.
There are various versions of this graph, including what’s called the J Curve.
This is all very theoretical, but hopefully it sets a context for your own innovation journey. The idea here is that if you can think like a startup. Startups are essentially vehicles for bringing innovations to the world, carrying them into and through the Technology Adoption Curve.
Startups are forged in high-pressure environments, in which only the most nimble survive. The Common Principles below try to systematise the practices of such organisations. Gradually, you can work your way through each step in these innovation processes, applying principles that help you get out of your own head and into those of your stakeholders, and move fast to validate your ideas in realistic settings.
Perhaps the biggest common development in the innovation models that emerged around the start of this century, is a shift from lines to loops. Whereby business best practices used to be linear in nature (see Linear Models) moving from each gated stage to the next, they are now exemplified by rapid iterative cycles of build-measure-learn. Then repeat.
Discovering the Atom of Innovation: the Loop
Loops are the building blocks of many types of systems, including our own ecosystem, and they form a fundamental aspect of the field of Systems Thinking, which lends itself well to developing products, services, businesses and organisations.
Innovation models are commonly structured in rapid, iterative cycles, in which a complete unit of the system is built, either as a prototype or a functioning product, then delivered, either for testing or into the actual world. Here we find the principles of continuous integration and continuous delivery (or deployment) – together “CI/CD” – that are common in software engineering.
In a general sense, a system can be perceived from the top, looking down, where the entire system is viewed together, and the small details are not important, or from the bottom, looking up, where the individual details are in focus, and from which the wider, coherent system may emerge.