Month: May 2019

behaviour changecomplexitydesign thinkingevaluation

Innovation, Change and The Leopard

Innovation is a misunderstood and often misrepresented concept that can provoke fear, indifference, resentment, confusion, or irrational exuberance. To understand the reasons why we can’t ignore innovation we need to look no further than the sage advice in The Leopard, a story of change.

It’s been said that the only true constant is change. Funny that something so constant and pervasive — change — invokes such strong reactions from people. It’s partly why innovation can be such a contentious term. Whether we like it or not, the dynamics of change in our world are forcing us to recognize that innovation is not a luxury, it’s more than just a means to competitive advantage, it’s increasingly about survival.

New Canadian research (PDF) looking at citizens attitudes toward innovation and their perception of it suggests there is much to be done to understand what survival and innovation mean for our collective wellbeing. Before getting to that, let’s first define innovation.

There are many definitions of innovation (see here, here, and here for some), but let’s keep it simple:

Innovation is doing something new to generate value

Innovation is effectively a means to create change. Design is the discipline and practice of how we create change intentionally. While change is often thought of something that takes us from one state to another, it is also something that can help us preserve what we have when everything else is changing around us. To help understand this, let’s look at a lesson from The Leopard.

Change Lessons from The Leopard

One of my favourite quotes comes from the Italian novel The Leopard by Giuseppe Tomasi di Lampedusa where one character (a young nephew speaking to his aristocratic uncle who seeks to preserve the family’s status) says to another:

If want things to stay as they are, things will have to change

The Leopard (translated from Italian)

I’ve written about this quote before, looking at the psychology of organizations and the folly of fads in innovation and design thinking. It’s about change, but really it’s about innovation and survival.

While individual humans are pretty resilient in the face of changing conditions, organizations are not so easily adaptive. Our family, friends, neighbours, and maybe governments will look after us if things get really bad (for a while, at least), but there are few looking after organizations.

For organizations – non-profit, profit-seeking, and governmental alike — innovation is the means to improve, to adapt, or maintain the status quo. It’s no longer a luxury — it’s about survival. Just as a real leopard has spots that are part of it’s form to survive, so too must we consider examining what kind of survival mechanisms we can build into our forms. That means: design.

Survival, by Design

A recent survey of 2000 Canadians by the Rideau Hall Foundation looked at Canadians attitudes toward innovation and whether Canada was creating a culture of innovation.


A culture of innovation is one where the general public has shared values and beliefs that innovation is essential for collective well-being

David Johnson, Governor General of Canada

There are many flaws with this study. There’s a reason I defined what I meant by innovation at the beginning of this post; it’s because there’s so much confusion surrounding the term, its meaning, and use. I suspect that same confusion entered this study. Nevertheless, there are some insights that are worth exploring that may transcend the context of the study (Canada).

One of these is the tendency among young people (age 18-25) to view innovation as something more likely to be generated from individuals. There is also differences in perceptions between men and women about who is best suited for innovation. What is shared is this perception of innovation as being ‘out there’, which is part of our problem.

If we view innovation as something needed to survive — to change or to keep things as they are– then we need to shift the thinking from innovation being something novel, technology-dependent, and fitting the fetishistic perspective that dominates corporate discourse. That requires an intentional, skillful approach to designing for change (and survival). It means:

From surviving to thriving

The interconnection of social, technological, and environmental systems has created an unprecedented level of complexity for human beings. This complexity means our ability to learn from the past is muddled, just as our ability to see and predict what’s coming is limited. The feedback cycles that we need to make decisions — including the choice to remain still — are getting shorter as a result and require new, different, and better data than we could rely on before.

Survival is necessary, but not very inspiring. Innovation can generate a means to invigorate an organization and provide renewed purpose for those working in them. By connecting what it is that you do, to what you want, to what is happening (now and in the near-present) on a regular basis and viewing your programs and services more like gardens than mechanical devices, we have the chance to design with and for complexity rather than compete against it.

For a brilliant example of this metaphor of the garden in creative work and complexity see Brian Eno’s talk with The Edge Foundation. Eno speaks about the need to get past this idea of seeing the entire whole and enjoying the creative space that takes place within the boundaries you can see. It’s not about simple reactivity, it’s a proactive, yet humble approach to designing things (in his case, music) that allows him to work with complexity, not against it. It allows him to thrive.

More on this later.

Working with complexity means designing your work for complexity. It means being like The Leopard (the book, but maybe the cat, too) and be willing to embrace change as a way of living, not just to survive, but to thrive.

Photos by Geran de Klerk on Unsplash , Adaivorukamuthan on Unsplash,
and Alexandre St-Louis on Unsplash

evaluationsocial innovation

Innovation Evaluation and the Burden of Proof

Innovating is about doing something new to produce value and this introduces substantial challenges for strategy and evaluation. Without knowing what you can expect, it’s hard to know what you have…or where you’re going.

Innovation is one of those terms that is used a lot without as much attention to what it means in specific terms. This lack of specificity might be fine for casual conversation, but the risks (and benefits) to innovation are great and require a level of detail that has a seriousness to it.

When you’re evaluating a new product or service, the risk of developing the wrong thing or leaving something out is substantial. False confidence in success could have detrimental effects on an expected return on investment. Lack of understanding of the effects of a product or service could lead to harm.

Evaluation and strategy in the context of innovation takes on new meaning because the implications are so great.

Scoping investments

The investment in innovation requires considerable tolerance for risk, which is why many countries provide incentives to support it. In the case of many community-serving organizations, innovation is often seen as imperative given the complexity of many of our social issues.

Although Research & Development (R & D) is commonly associated with innovation, often the ‘R’ part of this work is focused on the development of the product or service, not the evaluation. Evaluation focuses our attention on the product or service as it is used, even if that is in an early, restricted setting. Evaluation provides insight into the actual use characteristics, and can be useful in supporting program design, but only if there is investment in it.

While it is difficult enough to balance all of the demands and costs associated with innovating, investing in evaluation early on can yield substantial benefits later on as have been highlighted elsewhere. However, it is precisely because many of the benefits are later in the development cycle that it is so easy to put off investing in evaluation early.

Present thinking, future investment

Innovation is principally about the future. However, as illustrated in other places, it is also about present value as a byproduct of the process of creating new goods, services, and realizing ideas. By thinking this way, program developers have the opportunity to create additional value now by integrating evaluation more fully into the present offering.

Yet it’s in the future where the biggest payoff comes. Just as you might see the glamour and glory that comes with being an innovator like the image above, that kind of success is often based on a simple set of outcomes (e.g., sales, profit, etc..). Those kind of metrics are easy to celebrate, but often disguise what the real value of something is.

Whether it’s in the form of social benefits, additional discoveries, or the role of your product or service as a catalyst for other change, the focus on the end product loses the story told along the way. By allowing evaluation into the process early, the opportunity to tell that story differently and better makes for a more interesting — and beneficial read. That is an innovation story worth writing.

Photo by Austin Distel on Unsplash

strategic foresight

Foresight, Growth and the J-shaped Curve

The business of futures is to see what possibilities lay ahead to better anticipate how to meet them when or if they become reality. When this story line follows a linear path this is a lot easier; when it follows a more complex path reality can bite.

Foresight models look at trends and curves in trajectories of things including those that might disrupt the status quo. Using tools and frameworks (PDF), foresight professionals and futurists seek to better understand the contributors (drivers) and patterns associated with decisions, activities, and circumstances to anticipate what might come and better prepare for it (strategic foresight). Foresight is being used in fields ranging from natural resource management to energy policy to healthcare planning.

A rational look at foresight finds many reasons to embrace it for an organization. Who wouldn’t want to have a better sense of what is coming and prepare for it? The problem foresight poses is that it can lead people to look for the right things in the wrong way and that has everything to do with our human tendencies to see narrative arcs in the stories we tell ourselves instead of seeing either exponential or j-shaped curves.

Both of these models for data have enormous consequences for how we understand some of our greatest challenges as humans and as organizations as we shall see.

Exponential complication

A linear distribution or data structure is what humans see most easily. It’s the maintenance of a status quo, gradual change, or the progressive rise and fall of something over time. It’s what we see when we see in most trends and patterns. This perspective has the tendency to view much of the system in which this change takes place as relatively stable.

Stability is largely a matter of perspective. Everything is in motion to some degree; its the rate of change that we notice. In linear systems, that rate of change is relatively consistent or at a pace we understand while exponential change (or growth curves) are more challenging to see — and potentially more dangerous as the video below illustrates.

Al Bartlett’s lecture and other notes provide just one example exponential growth and how our perception is challenged by these kind of data structures in the world and the systemic effects they can bring.

Without an understanding of the growth dynamics associated with a particular phenomenon, we are at risk of grossing under-estimating the potential implications of what might happen. In these cases we need fixes, but not just any fixes as we shall see.

Deceptive Fixes

Another type of curve that can distort foresight models is the ‘j-shaped curve’. This curve describes situations where there is a long-term trend that is briefly countered in the short-term. An example is the case of alcohol consumption and health. There is evidence that alcohol consumption (e.g., a glass of wine or beer) can have a beneficial effect on a person’s health (at the population level, individual results might vary significantly). However, beyond that certain amount — that varies by person — and alcohol becomes toxic and can substantially contribute to a variety of health problems, injuries, and premature death. The j-shaped curve forms from data showing a mild reduction in health risks associated with modest alcohol intake as illustrated below.

For alcohol use, a single drink can lower your mortality risk before the risk starts rising again. Contrast this against cigarette use where a linear pattern of risk is seen: the more you smoke at any level, the higher your risk. Both patterns have linearity to them, but one is far more deceptive in it’s short and long-term implications.

Where this can fool foresight researchers is that there may be a trend that is showing a certain set of properties assumed to be on the trajectory like that on the left hand side of the graph when it is really similar to the right. Depending on the time horizon you use to inform your decisions based on this data the implications could be markedly different and potentially catastrophic.

Our fixes or strategies to anticipate change based on the wrong model could actually serve to amplify the very problem we sought to solve. A possible example of this is the move to ban single-use plastic bags. While the evidence of the environmental impact of plastic is considerable, a shift from plastic bags has its own negative implications, including the increased manufacturing of (with resulting waste and potential increased consumerism from) reusable tote bags or the increased use of forest products to support paper bag production.

The loss of plastic shopping bags which are often re-used (despite being called single-use) as garbage liners is now resulting in more purchases of plastic-intensive garbage bags. If the systemic implications are not considered in the design of such policies, these well-meaning fixes can profoundly fail. What is needed is a change in the way we consume, store, and buy goods, not just carry them home.

Systems change changes systems

The idea that you could be surrounded by literally thousands of people, connected to most of the planet through a device that fits in the palm of your hand, and still experience profound loneliness would once be considered the most profound oxymoron to anyone born before 1980.

Yet, here we are in a state where the very fixes for connection are failing us. The benefits of social media, social connection, artificial intelligence, and new production methods (e.g. 3D printing) are now starting to show some negative effects on our social and economic systems. Are these linear progressions of technological advancement that are simply generating a few of the inevitable bumps along the way? Are they exponential trends about to explode and profoundly transform the way we live? Or are these j-shaped curved trends that once provided us the benefits of finding connection in the modern world only to entrench our social systems into being online, not off?

We are creating systems that are changing themselves and having profound effects on the fundamentals around us. Retail conveniences created by online shopping means changing the relationship we have with our local merchants and that changes their viability. Handheld computers like an iPhone are engineered to hold our attention; what happens when we stop paying attention to the world around us?

These are systems questions and ones that foresight — when applied well — holds some promise in allowing us to anticipate and maybe deal with before its too late.

We can’t see these things coming if we hold models of the future that are based on a linear framing of what is happening now and what is to come. We also can’t adapt if we assume that even non-linear change will take place and persist within the same system it started in. Systems change changes systems.

Data models are fundamental to foresight and understanding them is the key to knowing whether your ahead of the curve, behind the curve, or sitting in the middle of the letter J.

Photo Credits: Ricardo Gomez Angel on Unsplash and Cameron Norman