GUEST POST from John Bessant
‘Now the chips are down…’
‘The robots are coming…’
‘Digitalize or die!’
There’s no shortage of scary headlines reminding us of the looming challenge of digital transformation. The message is clear. On the one hand if we don’t climb aboard the digital bandwagon we’ll be left behind in a kind of late Stone Age, slowly crumbling to dust while the winds of change blow all around us. On the other we’re facing some really big questions — about employment, skills, structures, the whole business model with which we compete. If we don’t have a clear digital strategy to deal with these we’re going to be in trouble.
And it’s not just the commercial world which is having to face up to these questions; the same is true in the public sector and in the not-for-profit world. The digital storm has arrived.
There aren’t any easy solutions to this which explains why so many conferences now have the digital word scrawled across their strap-lines. They provide focal points, create tents within which people can huddle and talk together, trying to work out exactly how they are going to manage this challenge. I’ve spent the past couple of weeks attending a couple — ‘Innovating in the digital world’ was the banner under which the ISPIM (the International Society for Professional Innovation Management) community gathered while ‘Leading digital transformation’ brought EURAM (the European Academy of Management) together. Close to a thousand people gathering for more than just a welcome post-Covid reunion; conferences like these are a good indication of the scale of the questions which digital transformation raises.
A Pause for Thought
But look again at those headlines at the start of this piece. They were actually newspaper cuttings from the 1980s, close on fifty years ago. Anxiety about the transformative potential of digital technology was running pretty high back then and for similar reasons. And yet their dire predictions of disaster and massive structural upheaval haven’t quite emerged. Somehow, we’ve made it through, we haven’t had mass unemployment, we haven’t been replaced by intelligent machines, and while income distribution remains very unequal the causes of that are not down to technological change.
Which is not to say that nothing has changed. Today’s world is radically different along so many dimensions, and not everyone has made it through the digital crisis. Plenty of organizations have failed, unable to come to terms with the new technology whilst others have emerged from nowhere to dominate the global landscape. (It’s worth reflecting that none of the FAANGS corporations (Facebook/Meta, Amazon, Apple, Netflix and Google were even born when those headlines were written). So, we’ve had change, yes, but it’s not necessarily been destructive or competence-destroying change.
If we’re serious about managing the continuing challenge then it’s worth taking a closer look at just what digital innovation involves. Is it really so revolutionary and transformative? The answer is a mixture. In terms of speed of arrival it’s been a very-slow paced change. Digital innovation isn’t new. Despite the hype around the disruptive potential of this technological wave the reality is that it’s been building for at least 70 years, ever since the invention of the transistor back in Bell Labs in 1947. And there’s a good argument for seeing it date back fifty years before that to when John Fleming and Lee DeForest began playing around with valves and enabling simple electronic circuits.
The idea of programmable control was around another hundred years before that; early on in the Industrial Revolution we saw mechanical devices increasingly substituting for human skill and intervention. Textile manufacturers were able to translate complex designs into weaving instructions for their looms through the use of punched card systems, an innovation pioneered by Joseph Marie Jaquard. Not for nothing did the Luddites worry about the impact technology might have on their livelihoods. And we should remember that it was in the nineteenth, not the twentieth century that the computer first saw the light of day in the form of the difference and analytical engines developed by Charles Babbage and Ada Lovelace.
In fact although there has been rapid acceleration in the application of digital technology over the past thirty years in many ways it has more in common with a number of other ‘revolutions’ like steam power or electricity where the pattern is what Andrew Hargadon calls ‘long fuse, big bang’. That is to say the process towards radical impact is slow but when it converges there can be significant waves of change flowing from it.
Riding the Long Waves of Change
Considerable interest was shown back in the 1980s (when the pace of the ‘IT revolution’ appeared to be accelerating) in the ideas of a Russian economist, Nikolai Kondratiev. He had observed patterns in economic activity cycles which seemed to have a long period (long waves) and which were linked to major technological shifts. The pattern suggested that major enabling technologies like steam power or electricity which had widespread application potential could trigger significant movements in economic growth. The model was applied to the idea of information technology and in particular Chris Freeman and Carlota Perez began developing the approach as a lens through which to explore major innovation-led changes. They argued that the role of technology as a driver had to be matched by a complementary change in social structures and expectations, a configuration which they called the ‘techno-economic paradigm’ .
Importantly the upswing of such a change would be characterised by attempts to use the new technologies in ways which mainly substituted for things which already happened, improving them and enhancing productivity. But at a key point the wave would break and completely new ways of thinking about and using the technologies would emerge, accelerating growth.
A parallel can be drawn to research on the emergence of electricity as a power source; for a sustained period it was deployed as a replacement for the large central steam engines in factories. Only when smaller electric motors were distributed around the factory did productivity growth rise dramatically. Essentially the move involved a change in perspective, a shift in paradigm.
Whilst the long wave model has its critics it offers a helpful lens through which to see the rise of digital innovation. In particular the earlier claims for revolutionary status seemed unfounded, reflecting the ‘substitution’ mode of an early TEP. Disappointment with the less than dramatic results of investing in the new wave would slow its progress — something which could be well-observed in the collapse of the Internet ‘bubble’ around 2000. The revolutionary potential of the underlying technologies was still there but it took a while to kick the engine back into life; this time the system level effects are beginning to emerge and there is a clearer argument for seeing digital innovation as transformative across all sectors of the economy.
This idea of learning to use the new technology in new ways underpins much of the discussion of what is sometimes called the ‘productivity paradox’ — the fact that extensive investment in new technologies does not always seem to contribute to expected rises in productivity. Over time the pattern shifts but — as was the case with electric power — the gap between introduction and understanding how to get the best out of new technology can be long, in that case over fifty years.
Strategy Matters
This model underlines the need for strategy — the ability to ride out the waves of technological change, using them to advantage rather than being tossed and thrown by them, finally ending up in pieces on a beach somewhere. Digital technology is like any other set of innovations; it offers enormous opportunities but we need to think hard about how we are going to manage them. Riding this particular wave is going to stretch our capabilities as innovation managers, staying on the board will take a lot of skill and not a little improvisation in our technique.
It’s easy to get caught up in the flurry of dramatic words used to describe digital possibilities but we shouldn’t forget that underneath them the core innovation process hasn’t changed. It’s still a matter of searching for opportunities, selecting the most promising, implementing and capturing value from digital change projects. What we have to manage doesn’t change even though the projects may themselves be significant in their impact and scalable across large domains. There’s plenty of evidence for that; whilst there have been notable examples of old guard players who have had to retire into bankruptcy or disappearance (think Kodak, Polaroid, Blockbuster) many others continue to flourish in their new digital clothes. Their products and services enhanced, their processes revived and revitalised through strategic use of digital technologies.
If the conferences I’ve been attending are a good barometer of what’s happening then there’s a lot behind this. Organizations of all shapes and sizes are now deploying new digitally driven product and service models and streamlining their internal operations to enable efficient and effective global reach. If anything the Covid-19 pandemic has forced an acceleration in these trends, pushing us further and faster into a digital world. And it’s working in the public and third sector too; for example the field of humanitarian innovation has been transformed by the use of mobile apps, Big Data and maker technologies like 3D printing. Denmark even has a special ministry within government tasked with delivering digitally-based citizen innovation.
Digital Innovation Management
Perhaps what’s really changing — and challenging — is not the emerging set of innovations but rather the way we need to approach creating and delivering them — the way we manage innovation. And here the case for rethinking is strong; continuing with the old tried and tested routines may not get us too far. Instead we need innovation model innovation.
Take the challenge of search — how do we find opportunities for innovation in a vast sea of knowledge? Learning the new skills of ‘open innovation’ has been high on the innovation management agenda for organizations since Henry Chesbrough first coined the term nearly twenty years ago. We know that in a knowledge-rich world that ‘not all the smart people work for us’ and we’ve developed increasingly sophisticated and effective tools for helping us operate in this space.
Digital technologies make this much faster and easy to do. Internet searches allow us to access rich libraries of knowledge at the click of a mouse, social media and networks enable us to tap into rich and varied experience and to interact with it, co-creating solutions. ‘Recombinant’ innovation tools fuelled by machine learning algorithms scour the vast mines of knowledge which the patent system represents and dig out unlikely and fruitful new combinations, bridging different application worlds.
Broadcast search allows us to crowdsource the tricky business of sourcing diverse ideas from multiple different perspectives. And collaboration platforms allow us to work with that crowd, harnessing collective intelligence and draw in knowledge, ideas, insights from employees, customers, suppliers and even competitors.
Digital innovation management doesn’t stop there; it can also help with the challenge of selection as well. We can use that same crowd to help focus on interesting and promising ideas, using idea markets. Think Kickstarter and a thousand other crowdfunding platforms and look at the increasing use of such approaches within organizations trying to sharpen up their portfolio management. Simulation and exploration technologies enable us to delay the freeze — to continue exploring and evaluating options for longer, assembling useful information on which to base our final decision about whether or not to invest.
And digital techniques blur the lines around implementation, bringing ideas to life. Instead of having to make a once for all commitment and then standing back and hoping we open up a range of choice. We can still kill off the project which isn’t working and has no chance — but we can also adapt in real time, pivoting around an emerging solution to sharpen it, refine it, help it evolve. Digital twins enable us to probe and learn, stress testing ideas to make sure they will work. And the whole ‘agile innovation’ philosophy stresses early testing of simple prototypes — ‘minimum viable products’ — followed by pivoting. Innovation becomes less dependent on a throw of the dice and a lot of hope; instead it is a guided series of experiments hunting for optimum solutions.
Capturing value is all about scale and the power of digital technologies is that they enable us to ‘turbocharge’ this phase. The physical limits on expansion and access are removed for many digital products and services and even physical supply chains and logistics networks can be enhanced with these approaches. Networks allow us not only to spread the word via multiple channels but also enable us to tap into the social processes of influence which shape diffusion. Innovation adoption is still heavily influenced by key opinion leaders but now those influencers can be mobilised much more rapidly and extensively.
The story of Tupperware is a reminder of this effect; it took a passionate woman (Brownie Wise) building a social system by herself in the 1950s to turn a great product into one of the most recognised in the world. Today’s social marketing technologies can draw on powerful tools and infrastructures from the start.
In the same way assembling complementary assets is essential — the big question is one of ‘who else/what else do we need to move to scale? In the past this was a process of finding and forming a series of relationships and carefully nurturing them to create an ecosystem. Today’s platform architectures and business models enable such networks to be quickly assembled and operated in digital space. Amazon didn’t invent remote retailing; that model emerged a century ago with the likes of Sears and Roebuck painstakingly building their system. But Amazon’s ability to quickly build and scale and then to diversify across to new areas deploying the same core elements depends on a carefully thought-out digital architecture.
Digital is Different?
So yes, digital is different in terms of the radically improved toolkit with which we can work in managing innovation. Central to this is a strategy — being clear where and why we might use these tools and what kind of organization we want to create. And being prepared to let go of our old models; even though they are tried and tested and have brought us a long way the reality is that we need innovation model innovation. That’s at the heart of the concept of ‘dynamic capability’ — the ability to configure and reconfigure our processes to create value from ideas.
The idea of innovation management routines is a double-edged sword. On the one hand routines enable us to systematise and codify the patterns of behaviour which help us innovate — how we search, select , implement and so on. That helps us repeat the innovation trick and means that we can build structures and processes and policies to strengthen our innovation capability. But we not only need to review and hone these routines, we also need the capacity to step back and challenge them and the courage to change or even abandon them if they are no longer appropriate. That’s the real key to successful digital transformation.
If you’re interested in more innovation stories please check out my website here
And if you’d like to listen to a podcast version you can find it here
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I was watching our Seattle Seahawks lose to the Green Bay Packers on Sunday and was surprised to see a series of television ads air during the game from GE, not touting how great their products are, but why GE is a great place for software developers to come work.
