As Charlie Ebdy has adeptly pointed out chasing shiny new trends too early can easily result in wasted resources and time. The timing and speed with which new technologies find genuine use cases, and genuine scale and maturity can be notoriously difficult to predict (and some never take off at all). So making smart decisions about where and when to experiment and invest can be very hard.
As Ray Kurzweil has said, ‘invention is a lot like surfing; you have to catch the wave at the right time’, which requires an appreciation of the entire lifecycle of a technology. Kurzweil described this lifecycle in the form of seven stages which represent the evolution of a technology: precursor, invention, development, maturity, false pretenders, obsolescence, and antiquity.
At the first stage, the enabling factors may come into place but it still requires imagination and determination to invent. Often, says Kurzweil, the invention enters the world as an ungainly and impractical device or technology that requires further refinements or developments to enable it to scale. As these developments progress, real use cases emerge and the technology can mature and scale. There then may be ‘false pretenders’ (assaults on the new technology which claim to fill feature gaps or even replace it, yet actually have feature gaps themselves) before a genuinely disruptive new replacement emerges and the existing technology becomes obsolescent and eventually an antiquity.
Over time, this has famously become represented as an ‘S-curve’ (or the sigmoid function), representing a slow emergence (which may take years) followed by a rapid period of development and growth in adoption, before the growth plateaus as the technology moves into an advanced state of maturity and eventually obsolescence.
The S-curve is everywhere. Clay Christensen used it to talk about value to innovation in The Innovator’s Dilemma. In her 2002 book Technological Revolutions and Financial Capital Carlota Perez used the S-curve shape to show the development of entire technological revolutions. She split the S-curve into two parts. An initial ‘installation phase’ happens where the new technology enables and establishes new infrastructure and ways of doing things. This is then followed by a ‘deployment phase’ which, as Ben Thompson describes it, is when:
‘…the fabric of the whole economy is rewoven and reshaped by the modernizing power of the triumphant paradigm, which then becomes normal best practice, enabling the full unfolding of its wealth generating potential.’
In the context of disruptive new technologies the S-curve is a particularly useful way of showing their trajectory but also revealing the key challenges at the heart of technological-driven change. Charles Handy, in The Empty Raincoat, used the S-curve to talk about the trajectory of systems and posit the idea of overlapping S-curves to show how one paradigm can replace another. He emphasises how important it is to begin focusing on the next curve before the current one begins declining (he describes this as ‘second curve thinking’) and to always assume that you’re at the peak of the current curve and be vigilant for the next one. But the critical challenge in navigating technological change happens in the so-called ‘dilemma zone’ precisely when the curves overlap.
As technology 1 moves through the early phase of emergence (A), rapid development and growth in adoption (B), plateauing growth (C) and eventual obsolescence (D), an organisation will be establishing and then embedding and optimising systems and models around that technology.
As the curve reaches point C, everything probably looks pretty good for the business since it is well optimised around that technology/platform/model. So there’s little reason to change. Processes and thinking are all embedded in the existing system making it hard to unravel, but the will is not there to do this anyway since there is no existential threat or significant economic challenge (yet). The problem with this is that if that organisation does nothing it can easily end up at point D, where it may well be too late to play catch up.
So the value of experimentation with forward looking technologies (and ‘second-curve thinking’) is not only that it helps you to learn about where the value may lie for the business, but also that it breaks the inertia which is likely to be a natural state of being but which may leave the organisation at risk of stagnation and disruption.
The second challenge with the ‘dilemma zone’ is that it is likely to involve a multiplicity of options, prioritisation decisions, and even a rethink of the existing paradigms, assumptions and thinking that may have been around for decades. The legacy business is likely to be tangled up with assumptions that have grown so embedded over the course of the previous S-curve that they are no longer recognised as assumptions or things that need to be challenged – it’s just the way things are.
Let me give you an example. When consumer adoption of the internet really started to take off in the late 90s newspaper and magazine publishers, who had long generated value from scarcity (there were few alternatives to the kind of content and value that newspapers and magazines could provide), struggled to understand how to create value from a commoditised environment (technology commoditises and the internet meant that suddenly everyone was a publisher). It took them many years to unlearn embedded assumptions about value creation, content, audience and financial models.
The risk in the dilemma zone is that the business does not think differently enough, does not question the assumptions which may serve to hold it back, and looks at the new through the lens of the old (the first versions of online magazines were PDFs of print magazines uploaded online). Meanwhile nimble and disruptive incumbents or new entrants who are not encumbered by legacy thinking can quickly move to the next S-curve and start to scale fast with different models and thinking. As Ray Kurzweil has said it can help to invent for the world that will be rather than what is now – to imagine new possibilities and work back from these to understand what we need to do now to bring them to life and move from one S-curve to the next.
The other challenge with the dilemma zone of course is that it is often full of uncertainty. More than this, the incumbent organisation will need to manage two potentially very different ways of thinking within the same business. And it will need to deliver against short term targets whilst still thinking longer term about the next big thing and making intelligent bets on the future. But all of this is possible with the right approach and the right kind of leadership.
Considering the ‘goldilocks zone’ of investment (not too much, not too little, just enough) is helpful in avoiding over-investment in the earliest stages of a hype cycle but also under-investment in the latter stages which may leave an organisation with gaps in capability or advantage.
Charlie Ebdy is right that many organisations probably have more time with new technologies than they imagine and that it’s unhelpful and a waste of resource to (as David Wheldon put it) be the dog that barks at every passing car. Chasing fads rather than focusing on underlying shifts is a fools game. But the risk is that if the organisation does nothing it will succumb to inertia, and fail to challenge the assumptions which can prevent it from moving forwards at all.
Be selective about which trajectories to follow for sure. But don’t do nothing at all.
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