By Darell Mann, guest lecturer at DTU Executive MBA
|Creativity and innovation are increasingly looking like the last bastions of human advantage over the ever-evolving world of computer technology. With 98% of innovation attempts failing, however, perhaps the world of computer-aided innovation has nothing to fear: the only way is up.|
Two-thirds of innovation attempts end in failure because the innovators measured the wrong things or measured the right things incorrectly. We misunderstood what our customers wanted, for example, or assumed they merely wanted more or less of what they were already getting.
The second biggest source of failure comes from a mistaken belief that the problem being solved is unique and requires some kind of a Eureka-like flash of inspired genius to get to ‘the’ solution.
The third biggest source can be attributed to a failure of the innovation team to successfully manage the innate complexities of execution, and specifically, getting the idea all the way through the existing (operational-excellence-driven) business.
On all three counts – meaningful-measurement, solution-finding and complexity-management – it seems clear that the human brain has not evolved the necessary skills to do the required job well enough. This shouldn’t come as such a big surprise. Never before in our evolutionary history has so much change happened in such short periods of time. My grandfather had one job his whole life; my father had two. It looks like I might end up with five or more. The generation that comes after me look like they may well reach double figures. We don’t possess the evolutionary-wiring for step-change, and more often than not find step-changes traumatic and painful. Our instincts are evolutionarily tuned to stability, and consequently, when it comes to creating or thriving through step-change, our instincts tend to point us in precisely the wrong directions.
Computers, on the other hand, don’t have to overcome the psychological inertia associated with having badly evolved instincts. They merely have to do what we tell them to do. When we programme them to overcome the failings of our stability-oriented brain, they will do a far better job of following such instructions than we humans will.
Thus, when we instruct our computers to roam the Internet looking for paradoxes, conundrums, trade-offs and contradictions, we’ve just taught them to be much better at finding innovation opportunities than we humans have ever been. It’s taken humans several hundred years – from the philosopher Hegel through to the more recent research conducted in the name of TRIZ – to understand the importance of contradiction-elimination as the seed corn of innovation. But it has only taken a couple of years to train computers to trawl through terabytes of social media content to go and find them.
In a similar fashion, it takes an individual human considerable time and effort to derive even one contradiction-breaking solution to a problem. But when we train a computer to go and look for those solutions – by trawling through the global patent literature or mountain of academic knowledge – we can very quickly create a database of contradiction-solving strategies. The TRIZ world has accumulated over 5.5 million such examples, and continues to add over 5000 new cases a month.
Moreover, when we step back from this database of breakthrough solutions, we begin to see an important picture: there really is nothing new under the sun. The problems being solved in one industry are, sooner or later, going to be encountered in another. Thus, by codifying the solutions being derived across every industry, we merely need to train the computer to make connections between those industries in order to make an enormous database of Eureka-shortcuts. Have a problem? Here are all the industries that already solved that problem, and here’s how they did it. ‘Someone, somewhere already solved your problem’ is the philosophy forming the heart of TRIZ. Now that the philosophy has been incorporated into the world of computers, we increasingly know it is true. Ask Samsung, a company that is currently using this kind of solution-bridge-making technology to average over 200 patents per week.
So, finally, what about the ability of the computer to manage the difficult job of executing the breakthrough solution once it has been found? The job of identifying allies and enemies, people who say they support your innovation project, but in reality don’t, people who have lots to lose if you succeed? This is surely a much more difficult computational challenge. Or at least it would be, except, when we spend the time to analyse these kinds of emotion, we quickly realise that they’re both universal and very finite. We, all of us, are driven by the emotional desire for Autonomy, Belong, Competence and Meaning (ABC-M). When we perceive that any one of these things is becoming worse, we are unhappy. When we realise that innovation success comes through making ABC-M better for every stakeholder, we’ve just had a big insight into how we make sure our innovation project finds itself in the 2% of successful projects. And when we programme our computers to go and listen for ABC-M amongst the team and in the people surrounding the team, we just gave ourselves the best possible opportunity of making sure we know what the real execution challenges we’re going to have to face are.
Only humans will be able to navigate a project through these challenges – that’s perhaps our last bastion of dominance over the technology? – but right now, we absolutely need the technology to tell us what the challenges are.