ICYMI: Our SXSW talk “A Language To Foster Innovation”

tl;dr — There is now a post-produced video of our talk: Learn a product innovation language that lowers the risk of innovation failure significantly, and how companies can adopt it.

When SXSW last took place as a gigantic IRL event, Wolfgang and I had the pleasure of giving a session about the power of language to get innovation right (or wrong for that matter).

Here’s how we pitched our talk “A Language To Foster Innovation” back then:

Pilots, divers, firefighters or surgical teams can’t afford misunderstandings. Neither can organizations when trying to innovate to survive. The former use strict language codes to avoid fatal miscommunication. Yet for some reason, organizations do not. Learn a product innovation language that lowers the risk of innovation failure significantly, and how companies can adopt it.

Join our session to learn what to think of accelerating creative destruction, how a missing shared language for innovation hinders a discussion about that (Wolfgang spends the first 10 minutes to surprise the audience with a digression about the incorrect use of data to illustrate creative destruction and then relates it to the use of different language models), and why sense-making frameworks help us remedy that.

Since then, quite a few people asked us if there was a video recording of that session, and all we had to offer was a link to the official SXSW audio recording, and another link to our slides on Speakerdeck.

Now, two years later, we took the initiative to merge the two separate pieces of content into one video that gets the audio in sync with our slides.

Why now, you might ask?

Well, we are about to launch Field, a tool that gathers people from all parts of the organization around products to share their perspectives, see the big picture, make sense of the ever changing context and align their actions. That's how languages diffuse, by engaging in the same routines and habits, and by using tools that facilitate these actions and conversations. To me, our talk from back then now feels like the perfect plea for the creation and use of a tool like that. In hindsight, it feels like Field just had to be built.


Good afternoon everyone, and welcome to “A Language to Foster Innovation”!

So are you all having a great first day at Southby? All right. We certainly do. Because you're all here and that's really fantastic. Thank you all for coming! Before we start, let's introduce ourselves.

My name is Klaus.Peter and I'm in charge of innovation management at dpa which is the German equivalent of the associated press here in the United States. And to give you a little bit of background on dpa: we are around 1100 people, organized in a pretty classical way, like a lot of departments and a lot of business units, which makes it a rather complex organization. Because it's these silos that we operate in.

In this session I'm going to talk about how we as a company address this kind of organizational complexity, and before we do that, let's get to know Wolfgang ...

Thank you. Hi everybody, I'm wolfgang. I'm a facilitator and sense-maker, and I don't have any of these silos, because I'm self-employed. But I work with a lot of companies who struggle with complexity when it comes to innovation. And this is what we will be talking about today: “innovation”. Or, as it's commonplace to call it now “disruption”.

And this is what everybody pretty much tells you right now about disruption. They tell you, as a business, you need to move very very fast because left and right companies are dying at an increasing speed, and because everything changes faster and faster. So have you to avoid being hit by creative destruction

Hands up who of you has heard this or a similar argument in the last couple of years. And hands up who has used this or a similar argument in the last couple of years. Yeah me too.

Then you have probably also seen this chart or a similar one. This chart is supposed to show how over time the average lifespan of companies in the Standard and Poors 500 index shrinks dramatically from over 60 years in the 60s to something around 15 years in the 2020s.

And again, hands up who has seen this chart in the last couple of years. And who has used this chart in the last couple of years in talks and presentations? Yeah, I did it, too.

There's one problem with both the argument and the chart. They are both completely wrong. I first began to suspect that something might be amiss with them when I saw that Innosight, the consultancy behind this chart published an updated version in 2018. And apart from the projection which still goes down over here, things look a little different now because starting in the year 2000, companies suddenly seem to live longer again.

So how do we explain this? Well, if you look at their methodology you learn that what we see here is not really a measured lifespan of companies but what they call an implied lifespan. An implied lifespan is a figure calculated from the annual turnover in the Standard and Poors index. That's the number of companies that leave and enter the index in a given year. And then this calculated figure is of course heavily smoothed. If you look at the original unsmooth turnover data it looks something like this. And apart from some spikes for example when the dot-com bubble burst and the financial crisis of 2008-2009 you really do have an increase in turnover as shown by this trendline. But does this increase in turnover really imply shrinking lifespans caused by accelerating creative destruction?

In 2017, three economists of the Credit Swiss made an effort to answer this question. And what they found completely changed or maybe disrupted my view of disruption. They first found that about one third of the turnover of the last 20 years is not due to creative destruction but simply to more proactive index management.

The threshold of companies going out of the index is simply much lower than before. And if you take account of this change in management, the increase in turnover practically disappears. You see an almost flat trend line, and notice the huge spike in 1976 that was there, I would naively interpret it as something to do with the aftermath of the oil crisis of the 70s something like this. Turns out it was just the fact that the index management took out 40 industrial companies all at once and put in 40 financial companies all at once. So these changes in index management completely skewed the data.

Then the economists asked what is the best explanation for the actual pattern of turnover in the last 20 years. So let's have a look at this. And alas, it's also not creative destruction that is the best explanation for this pattern. It's mergers and acquisitions activity, that is the volume of mergers and acquisitions of u.s firms. Which totally makes sense: companies not only are removed from the index when they die because they don't grow anymore, they aren't productive anymore or something like that, but much more often when they get acquired. And they get acquired because they are growing strongly, and they are very productive.

And this correlation also explains the waves we see in this chart. M&A activity increases in times of economic growth, only to drop in times of crisis. And this correlation also points to what we think the real problem for established companies is when it comes to fundamental change and to disruptive innovation.

And to see what that means let's have a look at another M&A related figure. We just saw that the overall amount of m a increased over the past 30 years in the U.S. But in the same time the share of venture capital exits via mergers and acquisitions compared to those via ipos, that is companies going public, that share skyrocketed.

From about 10 percent in 1985 to around 90 percent in the last decade. That means that today, only one out of 10 successful VC-backed companies goes public. Nine out of ten get acquired in the private market.

Why is that? Well, on the one hand it's because founders simply don't want the company to go public; they don't want to sacrifice their long-term goals for quarterly numbers. Just remember the pulled Airbnb IPO as an example for that. On the other hand, they don't need to go public. Venture backed tech companies scale perfectly well without money from the stock market.

The focus and technology, the business models that go with that put them in a different league than traditional companies and traditional enterprises. They leverage a different paradigm. And this difference, that is the real disruption.

So what these patterns show is: the real problem for traditional companies is not creative destruction, it’s shareholder driven short-termism, that is the inability of traditional publicly traded companies to follow through on long-term goals, and especially the inability to explore not only product variance but new product types, new business models to explore technological paradigms that are new. Because those quarterly numbers are so damn important.

And for an example of this kind of short-termism just look at GE's abrupt abandonment of a very ambitious long-term innovation strategy, for focus on very short-term profits and maximization of these profits driven by a very small group of so-called active investors. So in the end, what pundits tell you is a story about creative destruction and the need to accelerate, really is a story about short-termism and the need to decelerate.

So, we're 17 slides into the presentation, and you might ask yourselves what on earth has this got to do with the language for innovation. Well in fact, the whole story up to now is a story about a shared language for innovation, or rather the lack of such a language.

The acceleration and deceleration views not only propose different models of innovativeness and innovative change and disruption, but they use different languages to talk about it. On one view, proposed by people like Richard Foster, this is the guy who invented the chart we started with, or Clayton Christensen, creative destruction refers to products, to businesses, and to five to ten years business cycles

On the other, which goes back to Joseph Schumpeter, the original writer who coined the ter creative destruction, and the view was further developed by a venezuelan economist called Carlota Pérez, there the term refers to business models, to paradigms, and to 50 to 70 year so-called long waves. So these two views don't even talk about the same things, and thus they talk past each other.

There are two reasons for this. For one, these views deal with quite complex stuff. It's easy to disagree about the facts of innovation let alone how to interpret them. But for another reason, the proponents and their followers have very different interests: profits from punditry versus power through owning the paradigm. By the way, that's my dog, that's Molly, and she obviously has very different interests from the sheep.

Both of these reasons, complexity and different interests might also sound familiar to you. So again, by show of hands: who of you has to deal a lot with complexity when it comes to innovation? And who of you has experience with different people wanting different things from innovation? Yeah, exactly.

So you don't have to go to these fundamental questions of business cycles versus long waves. Just think of the different ways concepts like value or quality or focus are understood in your organization. Some people talk about value for users, others talk about value you can extract from customers. Some people talk about quality of design, others talk about quality of forecasts. Some people talk about focus on retention, other people talk about focus on conversion.

So, what could help remedy this situation? What are contexts where people don't talk past each other? And why don't they do it there?

People that don't talk past each other, they use fixed meanings when they talk with each other. So they use something like a controlled vocabulary. And maybe a controlled vocabulary that is based on an agreed ontology could help us here, too. So let's have a look at some examples for controlled vocabularies.

Control vocabularies are used for example by pilots or flight controllers, by surgical teams, by firefighters and by physicists. Well, all professions whose success depends on avoiding misunderstandings and who have long introduced specific controlled vocabularies to avoid misunderstandings. But is this approach really helpful here?

A controlled vocabulary is certainly very helpful for a firefighter or a surgeon, but even for physicists it becomes rather problematic when it restricts their possibility to explore new ideas, new ways of theorizing. Because as the Austrian philosopher Ludwig Wittgenstein famously said: the limits of my language are the limits of my world

A language makes us blind to everything it doesn't name or doesn't name correctly. In the disruption case this led to predictions that seem quite bizarre from today's perspective. In 2001, Richard Foster, the creative destruction guy, predicted that by 2020 the average lifetime of a corporation on the S&P will have been shortened to about 10 years, which is obviously wrong, even by Innosight’s own data.

Or even better, he said private equity firms will form the seeds of the industrial giants of the 21st century. So he obviously didn't see Google, Amazon, Facebook or Apple coming.

In the same way, framing your business to be only about users, design thinking, stuff like that, makes you blind to the interests of other stakeholders, for example employees or shareholders. Or framing it to be only about shareholder value makes you blind for the societal and environmental impact of your business.

So there seems to be a dilemma. On the one hand we need a shared vocabulary for understanding to avoid talking past each other. On the other hand, every fixed vocabulary highlights one set of aspects only to exclude or marginalize another set of aspects, another set of stakeholders.

So again what do we do? We think we need to get a little more abstract. We have to find a shared language to talk about languages, that is we have to find a way to create highly individual vocabularies that embrace the complexity of a situation. And then make differences visible and thus tractable. And this is where sense-making frameworks come in handy.

Sense-making frameworks are tools for teams and organizations to collaboratively make sense of what they're doing. They organize communication, they facilitate discussion and thus they help you build a shared language, align perspectives and navigate complexity.

In other words, more technically, sense-making frameworks offer you better languages to develop specific models and languages that suit your specific situation, and that help all stakeholders participate in that.

If this sounds a little abstract don't worry, you've probably heard of and maybe even used some of the sense-making frameworks out there

They are for example Dave Snowden's Cynefin framework. This one helps you make sense of your organization's environment and the appropriate way of dealing with it. You may have heard of Cynthia Kurtz's confluence framework which helps you make sense of how your organization works. There's Donella Meadows iceberg model which helps you understand and change systems. There's Werner Ulrich’s critical systems heuristics which helps you reflect on and critique your understanding of systems.

There's the Stacey matrix that helps you explore different modes of decision making. There's Henrik Kniberg’s alignment autonomy matrix which helps you leverage alignment and leadership for autonomy and intrinsic motivation.

As Cynthia Kurtz, the originator of the confluence sense-making framework put it: sense-making frameworks open discussions, pull apart assumptions and surface problems.

And they do this on two levels on a very abstract level. They define a conceptual space in which stakeholders locate their position, their perspectives, their interests, their activities.

For a very simple example of a conceptual space we use every day: think of color. You can describe every color you see in the dimensions from black to white, from green to red and from yellow to blue. These dimensions define our conceptual space for color.

Other conceptual spaces obviously work with different dimensions. You might know these.

So, the concepts these frameworks define are building blocks of what I call a generic meta language to describe and discuss concrete models and their specific object language, like the generic dimensions in the color example let us describe concrete specific colors. On this level these frameworks help us by unifying. On a very concrete level, the frameworks are tools for everyday use.

They work with rooms and post-its and time boxing and simple rules. They're used in workshops and retrospectives and stand-ups and board meetings. They help people talk with each other, not at each other. And they do this by not providing answers but by opening up a space for searching for them. On this level they help us by diversifying

So, now that we have learned a lot about the nature and the value of sense-making frameworks and the meta languages in general, iId like to get a little bit more specific, more practical.

Let's see how sense-making frameworks and their meta languages work by using one that has been specifically designed for product innovation. Because that's why we're here after all, learning about language to foster innovation. The framework I'm going to introduce today is called the Product Field. And in terms of a full disclosure I'd like to mention that it's actually us, Wolfgang and myself, and also Michael who can't be here today, he stayed in Germany, who created that because we felt the need to really get better at product innovation.

Okay, here it is, and it's actually really simple: Every product in the world originates on the inside of an organization. It doesn't have to be a company, it can also be a distributed company, a corporation of some kind, or a one-person endeavor. It would still be called the “inside”. And the product needs to get introduced to the outside, to its users and customers. This dimension is called “introduction”.

Also, every product innovation has a purpose represented by an idea how this product creates business value and the value it creates for users and customers. Without a proper implementation, though, no product innovation ever becomes real. So the second dimension is called “realization”.

We are now looking at a two-dimensional orthogonal coordinate system that forms the conceptual space of product information, which is actually pretty awesome because now we can define all relevant aspects of product innovation along these two dimensions inside our conceptual space. And that's exactly what we did: aspects on the upper left are about purpose on the inside, that's the actual business idea. Aspects on the lower left are about implementation on the inside, that would be the resources at play. Aspects on the lower right are about implementation on the outside or the focus on the outside, also called the market. And implementation aspects on the upper right are about purpose on the outside that would be the value you create for users, right?

Okay, now you might ask how is this helpful? How can we make use of that as an organization? And that is actually pretty simple, namely by using this conceptual space as a canvas to work with. Let me show you what I mean. Let's consider the Product Field itself a product innovation that we put right here into the center of our attention, right?

Then we map all the stuff we know or assume, which can be a lot, to the aspects onto the canvas. Then we can check the fit of our innovation by phrasing sentences that ought to make sense like this one: product managers who want to create successful products but lack a shared language with stakeholders reach out to the Product Field because that's the value. I think this makes perfect sense to me. And then we can do this with every sticky note on the canvas.

And while aspect labels constitute our vocabulary, their logical order literally shapes our grammar. Together they form the meta language we use to talk about all the specific terms we're using and what they mean to each of us. And this helps us to check if the things that we are thinking and the things that we are doing or about to do actually make sense. If we find sentences that don't make sense, there's probably something wrong in the concept of our product innovation.

And because it's a canvas we can do it in a session with all stakeholders, and this way we will start establishing a language for innovation in our organization. And with each time you, your team, your organization uses this framework, it's conceptual space, it's vocabulary, it's grammar, it becomes more ingrained and more natural.

This happens when you check the state of the same product innovation periodically, say once per quarter because things change over time. And also if you use that framework across all silos. This is the way you build up a shared frame of reference to understand and discuss complexity in a way that is open for everyone to understand. Or even beyond if organizations around you use the same framework. And that's how languages diffuse, rinse and repeat.

Let me just give you some examples of language patterns, language codes that derive from the continuous use of this framework in our organization. About two years ago, a guy from marketing that I rarely speak to came to me and said something like “there seems to be a problem in the upper left again”. In German, “ich glaube, wir haben wieder ein Links-oben-Problem”.

Thanks to the Product Field, I immediately understand what that means. Because in our organization this has become the code for our product managers lacking the competence and/or power to define strategic goals, to create strong enough uniqueness and to drive innovation initiatives to success. You can imagine that our CEO didn't really like that meme. That's kind of not a good situation you're in, so he decided to invest in product management. He decided to empower them, to invest in their education. He even used the word intrapreneur which is kind of the superpower product manager.

This was two years ago and since then we have launched twice as many experiments and twice as many products, with notable growth rates to be honest, than in the four years before that. Needless to say, there can also be issues in the lower left or in the lower right or in the upper right, and of course, these would require completely different actions, right?

Another term we use in a certain way is “context”. Let's take a look at the “context” again. If someone says that it's not about any kind of context but a very specific and clearly defined context of product innovation that, thanks to the Product Field, everyone understands. Like you see it here on the slide, the stuff with a gray background. When we say context, we are referring to the outermost aspects of our conceptual space.

And if someone refers to the innovation “core”, she or he is actually talking about its value proposition. You see, a value proposition that is actually a solution for a problem that outperforms alternatives by a strong uniqueness. And everybody involved understands that. There is no need for clarification or room for misunderstanding when somebody says product core or product context. And then there are also new terms that are being generated by a certain framework. In our case for example “idea push” and “value pull”. Are you curious what that means? I'm going to show you.

Well, idea push means that your innovation starts with a genius idea by a top executive or any other person with overriding power, often creating some sort of reality distortion field, think guys like Steve Jobs or Elon Musk. They literally push their idea forward and eventually into the market. The problem with this kind of innovation is that you really need guys like Steve Jobs, because regular top executives won't do, if you know what i mean.

Value pull means that innovation starts by finding a tricky problem that bothers a lot of people big time and offer them useful solutions. That would be, you know, that the lean startup approach, something like that. This way users will adopt your product without you having to invest heavily in pushy distribution. The value gets sucked out of your organization.

To complete the picture, there's also “market pull”. Think gaps in the market that require strong distribution measures to satisfy customer demands. And “resources push”. Think of existing assets that can be turned into a new product, for instance Amazon's IT infrastructure that eventually became AWS, one of the most valuable product lines ever.

So we basically have four characters of innovation here that can be identified by using the Product Field as our sense-making framework. They can be expressed by the meta language of that framework: idea push, value pull, market pull and resources push. Again, everybody involved understands that, which is actually really powerful because we are dealing with actual strategy issues here.

So you get the idea, sense-making frameworks use language to foster collaborative thinking and action. In our example the Product Field establishes a language to foster innovation. This way, we include everyone in the discussion and build a shared understanding. Thus, we create sustainable alignment and buy-in. And you get a tool that empowers you to think and to act autonomously.

By using such a sense-making framework, we resolve our dilemma. We create a language for shared understanding without marginalizing or excluding relevant aspects of stakeholders. So to answer the question implicit in the title of the session, a generic language to foster innovation can only be a meta language, embodied in a sense-making framework for everyday use. When you use such a language, start small, start with your team, use it with your stakeholders, use it whenever you need to find common ground and create a shared understanding.

Don't wait for your boss to propose it. And if you're the boss, do propose it. Whenever you start to try it out, start also making a habit out of it, by using it every day, and most importantly, use it to think for yourself. Don't let pundits blind you with under complex models and with exclusive language.

In other words don't panic, at least not over innovation. Thank you.

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