Good at your job, but bad at innovation? Here’s one reason why.

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Innovating can be a challenge for established firms.  Some the reasons for this range from inflexible management structures, to unsuitable organisational design to inappropriate systems of incentives.  I can’t speak to all of those, but what I can speak about is the constraint that being really good at your job puts on your ability to innovate.

One of the things that makes people successful in their field is their mastery of the rules of that govern that field.  These rules can be technical in nature, for example engineering or accounting standards.  These are ‘hard’ rules that are usually written down and are relatively easy to identify.  Alternatively rules can be ‘soft’ in nature, for example collective organisation behaviours.  These unwritten rules often define what needs to be done to get ahead in a business, for example, expectations about the hours spent at your desk (as opposed to the quality of your output!).  In all cases, learning and mastering these rules tends to go hand in glove with a rise up the ranks.

While there are some obvious downsides to that, there are upsides too.  Mastering the rules means that a lot of behaviour becomes automatic, freeing you up to deal with more complex tasks.  This is one of the things that distinguish more experienced practitioners from the less experienced ones.  By being familiar with the rules, mundane activities can be completed much more efficiently as you don’t have to sit down and think about what needs to be done – you just get on and do it.  This allows you to add more value in a context where productivity is important.  So mastering the rules is a valuable capability in most business environments.

Where it falls down though, is where the environment is uncertain and non-standard behaviours are required to be successful.  This is one of the reasons that a transition from a large, established corporate environment to an entrepreneurial one can be difficult.  Predictable action based on established rules can be totally at odds with the dynamic, undefined and unstructured nature of small, start up endeavours.  Conversely, the transition from a fluid, open environment with unformed rules into a highly structured business can be traumatic as well.  What makes you successful in one isn’t likely to make you successful in the other.

This problem is even more evident where innovation is the name of the game.  Those ingrained rule-following abilities that are fused into minds over the years can be a real barrier to developing truly innovative ideas.  This is largely because follow a set of rules provides a predictable set of outcomes; this is the point of having rules in the first place.  However, innovation involves the deliberate use of uncertainty which can mean breaking the rules.  But really successful people have embedded these rules into their behaviours and make them part of their habits, habits which are difficult to because people are no longer consciously aware that they govern their behaviour and thinking.

A couple of recent experiences highlighted this problem for me.  Firstly, I had an engagement where a client had asked an engineering firm to come up with some cost savings on a new technology.  A group of engineers were gathered around the table to brainstorm the issue, and the discussion centred on optimising the engineering of the current solution.  That was great, but it wasn’t going to bring about the step change in costs that the client needed.  Refining a design based on ‘normal’ or ‘good’ practice wasn’t going to be enough.  The rules had to be broken.  Eventually we came up with an innovative solution, but it took time to break the team out of their engineering habits on what was a relatively simple piece of work.

A second experience involved a tender for significant piece of engineering infrastructure.  Upon reviewing the preliminary design provided by the client, the (very experienced) engineering team decided that it had been well designed.  However, to win the job, the business needed to bring innovation to the table.  By agreeing that the design was done well, what the team was saying was that the rules that they use for designing this type of work had been effectively applied to this project. This was a great validation of our systems of education – it had produced a cadre of skilled engineers that could efficiently design large infrastructure projects in a similar way, despite their differing organisations.

However, drilling down into those comments it became clear that a whole range of assumptions weren’t appropriate for this particular project.  The high standards normally applied to public projects of this type weren’t mandatory for this work, meaning that far more radical approaches could be taken.  Old rules could be abandoned in favour of (in this case) better ones.  Once the veil of familiarity had been lifted from the team, a whole range of innovative ideas were thrown into the mix and the challenge become one of narrowing them down, rather than coming up with them in the first place.

Both of these examples highlight that part of the challenge to coming up with innovative ideas is finding ways to see what is taken for granted.  It’s a forest for the trees type of problem, but there are tools that can help the process.

One is to get someone involved who is completely unfamiliar with the task at hand. They ask the ‘really stupid questions’ that can allow a team to see what assumptions are being made without even knowing that they are being made.

Another is to use the questioning technique outlined in the recent book by Warren Berger, called A More Beautiful Question.  This book is focussed on asking questions rather than moving straight into solutions.  Berger defines A More Beautiful Question as ‘an ambition yet actionable question that can begin to shift the way we perceive or think about something – and that might serve as a catalyst to bring about change’.  His basic framework for achieving this is start by asking ‘why’ something is as it is at the moment.  This is followed by asking ‘what if’ and then ‘how’.  To get a feel for how that works, you’ll need to read the book, but I like the process because it forces a rethink of assumptions that underpin how things are today, providing space to think about innovative ways to approach old problems.

Another good reference is Gamestorming by Dave Gray, Sunni Brown and James Macunufo.  The book is a collection of techniques for idea generation and development aimed at creating breakthrough innovations.  The book is particularly useful because it presents a wide range of tools to choose from, all of which are set into a context of creating change.

Above all of these though is the explicit recognition that the things that make people successful in the past won’t necessarily make them successful innovators in the future.  The things that make someone an engineer, accountant, technician, IT guy or programmer are also the things that can constrain their imagination and ability to generate out of the box solutions.  The good news is that it doesn’t take much to turn that around.  Breaking the habits of a working lifetime can actually be relatively easy once you recognise where you’ve come from and how it shapes your thinking.  The most dangerous course of action is to assume that what’s served in the past will serve in the future.  Once that hurdle is overcome, what makes you good at your job today can make you even better at your job in the future, as it opens a whole range of possibilities that the rules simply don’t anticipate.

Convergent Innovation: Improving Adoption through Multi-Loop Learning

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Learning and innovation go hand in hand. The arrogance of success is to think that what you did yesterday will be sufficient for tomorrow – William Pollard.

 

Ensuring that innovations are taken up by the marketplace is a challenging problem.  The spread of technological innovations is influenced by a range of factors, from supply and demand factors, to learning-based effects, through to technology substitution and management techniques. The range of factors that affect this spread is very large.  One of the factors that receives relatively little attention however, is the social nature of technological innovations and the environments that supports their adoption.

As a starting point for exploring this issue, consider for a moment what ‘technology’ is.  Technology is often represented by objects, for example, a smart phone, a car or some software.  However, technological innovations are much broader in scope than this.  In addition to ‘hardware’ and ‘software’ (in a general sense), it can include the knowledge required to design, build and utilise the technology, along with socially-defined rules that determine how it is used in practice.

In thinking about this approach, consider the case of smart phones.  At the level of the object, smart phones are a collection of metals, plastic and glass combined with software.  However, what distinguishes technology from other objects we create is that technology serves a purpose and that purpose is defined by people, rather than the object itself.  That is, technology can be defined by how we use it, or what purpose we put it to.  After all, that’s why we create technologies in the first place – to do something we couldn’t do before.  So smart phone technology includes the things we do with it, from tracking our fitness to playing music and talking to friends and acquaintances.  Without those purposes, the hardware would be meaningless.

A by-product of using such a definition is that technology can be seen to change over time, even if the hardware remains static.  This doesn’t occur much in practice though, as people tend to refine technological innovations to better suit their evolving needs.  Returning to the smart phone example, the programmable nature of this technology allows it to evolve quite quickly.  And this has enabled the rapid evolution of the technology based on how it gets used by people, in many cases in ways that couldn’t even have been imagined when smart phones started their relentless rise in 2007.

Following from this, an important observation is that the creation of a technological innovation involves the creation of something that is new in both a technical and a social sense.  New technologies require new habits, routines, expectations and practices to be developed as well the hardware and software.  The technical elements of the innovation can often be created ‘in the lab’, but the other parts of the technology need to be developed by users actually using it.  The problem for innovators therefore, is to work out what these habits, routines, expectations and practices are likely to be before they spend too much time and money on development of the technical innovation.

In this context, approaches to innovation such as lean start-up and design thinking provide much stronger platforms for creating technological innovations than more traditional technology-push or technology-pull approaches.  Lean start-up does this by minimising development work (to create the ‘minimum viable product’) before testing with users, followed by further, rapid evolution of the idea. Design thinking takes this a step further by matching what is technologically possible with user needs at the very outset.  In both cases, the innovation stands a much better chance of being accepted once it’s released into the wild.

However, this approach won’t always be successful.  Users don’t always know what they need.  This is a central idea in the concept of disruptive innovation: it isn’t always possible to predict what the market for an innovation might be and extensive exploration might be required to find a market that works.  In this event, innovations that go on to redefine an industry – or even create new ones – are going to be much more difficult to create using lean or design thinking principles.

What’s required, then, is a way to recognise that all the parts of a technological innovation evolve following the initial creation.  It requires the basic cycle suggested by design thinking to be repeated at a more global level once a first cut innovation is released into the market.  By inserting a deliberate repeating loop into the process (as a type of ‘double loop’ or ‘multi-loop’ learning) it allows for the continued evolution of the artifact and the habits, routines, expectations and practices that accompany that technical innovation; that is, the technology in its entirety.  A modified version of the design thinking process might provide a method to achieve this.

Such an approach would allow the initial social and technical configuration of the innovation to converge with the social context of the marketplace; a process that could be called ‘convergent innovation’. This recognises that the evolution of a technology combines things that the innovator can address (in how the technology functions) as well as things they can’t (how people actually use the technology).  It creates a challenge insofar as innovation becomes a continual process involving relatively uncontrollable use of technology in practice.  But in return, it offers the opportunity to raise the chances that a technology will be adopted – and adopted widely – in the marketplace.

And when companies are looking to extract maximum value from a limited set of valuable ideas who wouldn’t want that…?