By Henry Doss
Innovation has a magic solution triumvirate: Brokers, role models, and risk-takers.
“Send lawyers, guns, and money… to get me out of this.” – Warren Zevon
When the mythical narrator in the above-quoted Warren Zevon tune found himself in a bit of trouble, he knew he needed three things: “lawyers, guns, and money.”
Innovation, likewise, has its own magic triumvirate solution. But in the case of innovation, it’s not “lawyers, guns and money” – it’s brokers, role models, and risk-takers.
If you are faced with building or nurturing a culture of innovation in your organization, you may be looking for an approach that will give you a total solution – sort of the “lawyers, guns, and money” approach to innovation challenges. One place to start solution hunting is in the innovation culture’s informal trifecta: Brokers, role models and risk-takers. When these roles are properly filled, you’ll find cutting-edge innovation. When these roles are not being filled, you’ll find a relatively bland, safe and predictable organizational dynamic.
Each of these roles serves to break down some common barrier to innovation, and to foster the kinds of interactions and collaboration from which innovation emerges. Brokers break down silos and build links between information groups, supporting cross-functional conversation and exchange. Role models inspire others by setting observable behavioral examples. Risk-takers overtly demonstrate the potential return on risk-taking, both for individual careers and for organizational benefit.
You won’t generally find these functions in job descriptions, nor will you find someone with a title like “risk-taker.” (Well, you shouldn’t, although given the wantonness with which job titles are created nowadays, you might!) Neither will you find these roles being directly incentivized, or formally evaluated, or even recognized, as a rule. Like many aspects of an innovation culture, they happen – serendipitously – or they don’t. And because the roles are elusive and difficult to measure, they can go unappreciated and unnoticed. And then they gradually fade away.
But if you look hard in your organization, trust your own judgment, and use your best observational skills, you can find, nurture, and acknowledge these key individuals and keep their critical skill sets alive and growing.
Here’s what they do and why they’re important.
BROKERS: The greatest threat to innovation is an information silo. Free and open information exchange and conversation is the fuel that drives innovation in organizations. But all too often, information is walled off in divisions, functional areas, or geographies. Brokers in organizations provide the critical function of “information arbitrage.” They are the ones who seek out connections between people and ideas and foster conversation and interaction. If you see cross-discipline and inter-team work that is producing new ideas and thinking, there is someone playing the role of broker – someone who saw a connection between this and that and took steps to bring people together. Brokers are often somewhat rogue or indifferent to convention and hierarchy, and the value they create is often so intangible as to go unnoticed. But look hard and you’ll see them working to bring ideas and people together.
ROLE MODELS: A role model is much more than simply someone who is visible. Role models embody an authentic and purposeful set of values and demonstrate those values through their actions. Innovation role models may not be risk-takers themselves, but they will understand the importance of risk and actively support risk-taking in their organization. They may not be active connection brokers themselves, but they will understand, look for and encourage connection making. The old saying, “Do as I say, not as I do,” gets turned on its head by the innovation role model. In the presence of powerful, authentic role models, members of an organization will model actions more than words. They will seek to “do what the role model does.”
RISK-TAKERS: Risk taking is not about being foolhardy. The innovation risk-taker brings good judgment and self-awareness to everything, but understands that there is a point – just there, beyond the safe, beyond the secure – where there are disproportionate rewards. In any organization, there are plenty of fumbles, missteps, train wrecks, and failures. But these are less the result of risk-taking, and more of ineptitude. Look for the places in your organization where there is lots of homework being done, lots of direct, plainspoken conversation, and a strong sense of fun, and you’ll find the risk-takers there. Risk-takers resist the temptation of the status quo and continuously push organizations into new – and quite possibly innovative – areas.
Like Warren Zevon’s answer to his problem, “guns, lawyers, and money,” – there is an answer to the innovation challenge: brokers, role models and risk-takers. Take an informal look around your organization. See if connections and links are being made between areas of operation and expertise. See if there are visible, authentic champions of innovation, and see if there are strong, well-prepared risk-takers pushing boundaries. If you are finding these kinds of activities, congratulations – you are in a strong innovation culture. But if you don’t, maybe you better make that phone call for “guns, lawyers, and money.”
Notes on the practice of innovation and technology commercialization
A colleague at T2VC posed this question:
- We know that if everyone is an entrepreneur, a society will not function.
- We know that if everyone is a producer, a society will wither.
- Thus, is it possible to determine the exact proportion of innovators versus producers to maximize the productivity of an ecosystem?
This question is, I think, part of a broader one: in an innovation ecosystem is it possible to adjust all the various elements so that the ecosystem optimize its outcomes (measured in some way)? These elements may be universities, technology commercialization systems, public sector financing funds, and so forth – more about this later. You may remember that in the February 2013 Imperfect Works http://innovationrainforest.com/2013/02/06/imperfect-works/ blog in this series we talked about optimizing which means “make something as good as possible within a whole system” although each individual element may not be operating in the best way it can.
In their paper Chaos prevailing on every continent: Towards a new theory of decentralized decision-making in complex systems, http://www.temple.edu/lawschool/dpost/chaos/chaos.htm researchers David G. Post and David R. Johnson present a method of finding optimal configurations of elements in complex adaptive systems. The authors pose the Gardener’s Dilemma: how can a gardener find the best or at least a good configuration of a collection of plants whose overall “fitness” (for example total yield) is dependent upon the behavior of all the other plants? You may begin to see how the garden is an analogy for an innovation ecosystem. We shall see shortly if this analogy is helpful.
In this imaginary garden there are plants of different species. The gardener would like to obtain the most luxuriant overall growth. The gardener must decide for each individual plant: should it be pruned or not? How can the best combination of pruned and un-pruned plants be created that will produce the greatest yield for the whole garden?
As always, we need to make some assumptions, which for this garden are:
(1) The relationship between an individual plant’s pruned or unpruned state and its growth is different for each plant. For some plants growth will be increased by pruning, for other plants pruning will reduce their growth.
(2) Each individual plant’s growth can be affected by the growth of other plants, for example as one plant grows it might block sunlight reaching another plant that needs it. Kauffman calls these spillover effects although I prefer interactions.
Even with such simplifying assumptions it turns out that this problem, and many similar ones, are “computationally intractable” or in other words incapable of true solutions by any known analytical methods. A little thought may convince us why this is the case for our garden. Suppose there are only three plants each of which may be pruned (we will call this state 0) or unpruned (we will call this state 1). We can use the tree diagram below to help figure that 8 possible configurations exist, namely 2 x 2 x 2 = 23.
A little further arithmetic will show for 4 plants there are 16 possible configurations, namely 2 x 2 x 2 x 2 = 24 and so on. To generalize this pattern we can say that in any system with N elements, each of which can take one of S possible states, there are SN different system configurations. This SN can rapidly become a very large number. For 10 plants the number of configurations to be tried is already 1,024. Hard work for the gardener!
Is this gardening knowledge of any practical use for those of us developing optimal innovation ecosystems? If this this ecosystem analogy is computationally intractable, why am I wasting your time discussing it? Stay with me for just another moment and I will demonstrate its practical use – but first one final research result.
When a problem cannot be solved by mathematical analysis computer modeling may help, even greatly oversimplified models can be used as long as their simplifying assumptions are not forgotten when applying the results to the real world. Stuart Kauffman and his colleagues have developed a family of computer models and problem-solving algorithms for complex interconnected systems, known as “NK models,” for studying various forms of the Gardener’s Dilemma in, for example, evolutionary biology and cyberspace law. We don’t have space for details (which are in Johnson and Post’s publication) but essentially the method consists of modelling interactions between ecosystem elements. The elements in the garden ecosystem are sub-divided into any number of non-overlapping but interacting self-optimizing parts called patches, like a patchwork quilt.
To quote Post and Johnson “The result is a fairly remarkable one: It is by no means obvious that the highest aggregate fitness of the system will be achieved if it is broken into quilt patches, each of which tries to maximize its own fitness regardless of the effects on surrounding patches. Yet this is true. It can be a very good idea, if a problem is complex and full of conflicting constraints, to break it into patches, and let each patch try to optimize, such that all patches co-evolve with one another.”
A typical innovation ecosystem has elements (S) such as universities and research institutes with their technology transfer offices (TTOs), new business incubators and accelerators, some form of central support organization to assist TTOs with issues such as market intelligence and so forth, financing programs such as early-stage R&D grants and seed and venture funds, economic development organizations, science and technology park, a contract research organization, and sometimes miscellaneous organizations which were formed for different times but are still functioning. All these can certainly exist in many states so N will be much larger than 2 as in our garden.
Applying what we have learned, it is good practice to divide these ecosystems into patches, for example four possible patches could be:
Universities and research institutes with their technology transfer offices (TTOs)
Central support organization to assist TTOs
Early-stage R&D grants and seed and venture funds
New business incubators and accelerators
Contract research organization
Science and technology park
Economic development organizations
Miscellaneous organizations which were formed for different time
Having the elements trying to maximize their fitness within their patch improves trust and communications between them – and as a result – decreases transaction costs. Furthermore, this should break down the rigid hierarchical structure from which some ecosystems suffer. Other practical applications of the Gardener’s Dilemma can be found, such as explaining the familiar S-shaped curves of technology growth and disruption. Climbing around the fitness landscape searching for peaks and valleys will be discussed in future blogs.
Your response to all this may be “but this is obvious, I don’t need computer models.” In complex adaptive systems what appears to be obvious is not always so (occasionally disastrously not so). Personally I’m comforted by having a foundation theory.
Next time: Games of chance? Cause and effect in innovation ecosystems.