Designing The Architecture Of Change: How Organizations Transform

at UXDX EMEA. Video: https://youtu.be/qzEFZslAwgU

Readable transcript: edited from the recording's captions for readability (fillers and false starts removed, punctuation and section headings added). Wording is the speaker's own. Timestamps are positions in the video. Names marked [?] could not be verified against the audio.

Kodak and the first digital camera

[00:00:04] If we were in a room, I would ask you to answer a question: how many of you are in a digital transformation at the moment? I would count the hands, and I would see that pretty much everybody is in a digital transformation. And what I would say to those who have not raised their hands is: don't worry, you'll get there soon. So let me talk to you about how we can think about changing large organizations from the inside, by telling you a fairly old digital transformation story.

[00:00:38] Those of you who are my age or thereabouts will recognize these immediately. For those of you who don't know, what's on the screen are film canisters, and there was a point in time where these were the gold standard for photography. There was no Instagram, there were no camera phones, there was certainly no TikTok. You would pop one of these in your camera, and then when you were done shooting, and you didn't know what you were shooting, you would take it to a lab and it would get developed. And Kodak was the undisputed ruler of the photography world.

[00:01:24] In the 1970s, however, there was an engineer who worked at Kodak who started thinking about how we can disrupt, how we can change the world of photography, how we can do things in a different way, because the first digital technologies were starting to emerge. He in fact developed the first digital camera in 1975, and when he showed it to his bosses at Kodak, they said: well, it is cute, but it is filmless photography, so just don't talk to anybody about it.

[00:02:18] The reality is that soon after, the R&D and marketing development leader at Kodak had commissioned studies, and they had discovered that digital photography was an upcoming trend, and that the film business, the canister business, was probably being threatened by these new threats. They produced a report. They again went to upper management, saying: we've got bad news and we've got good news. The bad news is that the market is telling us that digital photography will be a thing that stays, and we all know that. But the good news is that the technology is still so primitive that we have about a 10-year window of opportunity to develop a digital camera that can go to market. Again they were told: that's cute, don't worry, our business is making film, we'll get there.

[00:03:48] But eventually they succumbed to the pressure, and in fact Kodak was the first company to come out with a digital camera, and it looked a little bit like this. In fact, it was this. Now, if you were to look closely, you would notice that it is a digital camera in that you have a digital screen where you can preview the shot that you've taken, but it's still technology based on film. You should see that right in the middle there's a canister. So effectively the digital technology was applied after the film technology was employed.

[00:04:37] History shows us that Kodak is no longer a massive player in that space. In fact, it is pretty much focused on professional services, and digital photography is everything we do. What is interesting, though, is understanding why this set of events took place. It's not that they only happened at Kodak; they happened in a number of other organizations. But the Kodak example is particularly visible, shall we say.

The local optimum problem

[00:05:16] We can use a mathematical concept to explain what happened. If you're not familiar with advanced mathematical theory, I'm not either, but I looked into this. There is a concept called the local optimum. In computer science and applied mathematics, the local optimum of an optimization problem is a solution which is optimal within a neighboring set of candidate solutions. So: the problem is digital photography, we are a film business, the neighboring solution is a film camera with a digital post-view display.

[00:06:09] The opposite of a local optimum, which is what we call thinking outside of the box, is called the global optimum, which is the optimal solution among all possible solutions. Now clearly we could go into unknown unknowns; we don't know what is optimal among all possible solutions, but you look outside of the neighboring concepts. So what Kodak did was optimizing, if you will, which is what a lot of the organizations we work in do at the same time. They focus on improving the current situation, but that often, or almost always, does not lead to the best solution.

[00:07:02] This local optimum problem affects most of our transformation initiatives, because they are primarily focused on optimization activity. I've heard lots of colleagues in current and past jobs in other industries saying: we're not really doing a digital transformation, we are digitizing the same old processes that we have. The bottom line is that if you set a goal based on starting where you are today and heading in a direction that will improve your current situation, you're always going to stay in the vicinity. You will remain in your local optimum, but likely it will not be the true optimal strategy, because some competitors will end up beating you to the top of that metaphorical mountain, and it may be a mountain that we've not even seen, like what happened with Kodak.

[00:08:10] The reason we do that is because in optimization we can transform processes that we know, and process brings seductively strong near-term outcomes. This is not my saying; this is Reed Hastings, the CEO and founder of Netflix. So we use the label of digital transformation, and that is not inaccurate, because we are using digital technologies to change our business somehow. But we are still missing a huge part of the transformation picture, and the part that's missing is disruption.

Why traditional management fails: three archetypes

[00:09:04] Unfortunately, our traditional management and leadership techniques don't only fail us in these kinds of circumstances, they tend to make things much, much worse. There are studies everywhere, but one of the recent ones shows that less than 30 percent of established companies that begin to falter ever rebound. That translates into a 70 percent failure [?] rate for the most successful companies when they are not staying on top of the market and are focusing on the neighboring solutions. The reason for this is that the models that these kinds of companies generally employ when they find themselves in these situations often do not work.

[00:10:03] If we want to go back to Kodak, it's not that they didn't care about their users. It's not that they didn't care about photography. It was quite the opposite. The organization was so large, and as organizations get large they become super entrenched in their communities. They become more risk-averse, and they are much more vested in maintaining the status quo. You have stakeholders, you have shareholders that you're responsible to, and taking big risks is terrifying. So we become so risk-averse that we create an environment where failure is not an option. We value consistency and predictability. And while these things in the short term can bring outcomes that are positive, the reality is that they make us much more vulnerable to disruptions and black swan events. Think about the pandemic: for those organizations that only rely on known processes, it's been a completely game-changing event.

[00:11:25] I talked a little bit about how old management techniques are not our friends, and what I've noticed is that we tend to follow three main archetypes when we look at transformation in established organizations. The first one is the Prince Charming archetype. Let me bring in a fearless leader that's going to solve all of our issues and is going to change everything for us. Yeah, we know how that went, right? He knew nothing about the politics or the domain. Generally these leaders are taken from other industries. They are high-profile, they come in, they want to apply their proven methods to an organization or a situation that they don't necessarily know, and then the results are not what was promised.

[00:12:26] The other approach is the skunkworks model, experimentation at all costs, the Google Xs of this world. Let's take the most brilliant minds, chuck them in a corner with money and all the toys that they want. It's generally called an innovation department. And let's hope that they will come up with a splendid idea that's going to turn things around. The reality is that sometimes a good idea comes up, right? But because it's so detached from the reality of the organization, it's a little bit like Milli Vanilli: a one-hit wonder never to be repeated. So it's not a recipe for sustainability.

[00:13:14] And then the third archetype is around consultants, whether management consultancies or external partners. These people are incredibly smart. They conduct tons of ethnography, testing, validating. They have all the strategy, they explore potential business models. Then they give you a report and you're supposed to implement it. Again, this is often a recipe for disaster, because all the domain knowledge that they have acquired then leaves with the experts. The understanding, the customer empathy, is never transferred to those who have to implement.

System one, system two and the ladder of inference

[00:14:06] I've seen these cycles repeat themselves over and over, and for me the interesting question is: why does that happen? Why do we keep making the same mistakes? The best explanation of why this happens I've heard from a colleague, and so I'm going to try and paraphrase how they explained it to me.

[00:14:35] The book you see on this slide is Thinking, Fast and Slow by Daniel Kahneman. It's the book that every designer says they've read, but it also has the potential to put an insomniac to sleep, because it's very dense. It's very interesting, and I love it in small doses. Kahneman talks about our way of processing information and decision-making as system one and system two. System one is the fast, unconscious, automatic way in which we make decisions. We rely on experience, information that we know, and we just go on autopilot. It's like when you get in the car: you don't think about all the steps after you've been driving for a while. Sometimes you don't even know how you got from A to B, if it's something that you know. This is system one.

[00:15:38] System two, which is an area of cognitive strain, is how we think we are when we make decisions. It's slow, conscious, it takes effort, it's applied to complex decisions. The reality is that if, while I'm talking to you, I were to start thinking, oh my God, does my hair look okay, now I'm going to move my hand this way, now I'm going to breathe, and if I were on a stage I would think about how I walk, I'm getting anxiety and hyperventilating already. We tend to be in system one much more than we are in system two, so we tend to make decisions in a much more impulsive and automatic way.

[00:16:41] This is in fact how we make decisions. This is called the ladder of inference, from an organizational psychologist called Chris Argyris. The reality is that we take, from the bottom, reality and the facts that we see. We select, completely automatically, the ones that we think are relevant. We interpret them, we make assumptions, we jump to conclusions, we develop beliefs, and that's how we act.

[00:17:19] Interestingly, that is aligned to Maslow's hierarchy of needs, where the decision-making is part of self-actualization. We want to get to that state where we belong, we have esteem, and we are realized, but we tend to do so by using our automatic system. We want to be in cognitive ease [?].

Knowledge work and complex adaptive systems

[00:18:05] This makes things even more interesting, because if you think about processes, if you think about the three archetypes, if we think about the fact that we are focused on outcomes, being knowledge workers, our outcomes are often incredibly intangible. If you're not familiar with the term knowledge worker, it's a term that was coined by Peter Drucker, who used it to describe people who use and create knowledge to create value in their work.

[00:18:51] We are differentiated by the fact that we do a high degree of non-routine work in a non-deterministic environment, and that means that process is really hard to apply. Non-routine work in a non-deterministic environment means that we start with an idea, but we're not really sure of the outcome that is going to emerge at the end. We don't know the end state of a process at the beginning of it. And because of the complexity that's inherent in non-deterministic work, the level of collaboration and communication is infinitely higher.

[00:19:46] This is compounded also by the fact that we work in complex adaptive systems. We work in organizations that are self-organizing, have their own internal rules, and exhibit emergent properties. What does that mean? Emergent properties are those properties that literally emerge when something in the system changes. Let me use the example of a team. A team is a self-organizing system, and the dynamic in that team will change when the elements of the team, that is the people, us, the workers that are in it, change.

[00:20:40] So it's not just the work that we do that is non-deterministic. The organizations that we work in are also non-deterministic. Things change all the time. The work changes, and we call those pivots. Teams change, organizations change all the time.

[00:21:04] At this point I would show you a video, and we will embed it so that you can click on it, jump out of this presentation and refer to it. This is a video by one of my favorite systems thinkers that lands a really important message: a system is more than the sum of its parts. It's an indivisible whole, and it loses its essential properties when it's taken apart. This is Dr. Ackoff. I would like you to think about this as we go on and touch for a little bit on systems of people.

Systems of people and learning organizations

[00:21:52] We work, as I said, in teams and organizations that are not in a steady state [?]. Yet when we talk about people, this is the language that we use. We say: people are our most important assets, things that can be part of a process. It all begins with talent. The definition of talent is something that baffles me. We need to win the war for talent. Leadership matters more. And we apply this kind of thinking to our organizations, which look a little bit like this in every org chart I've ever seen.

[00:22:37] The reality, however, is that the org chart is a little more like this. There are all these emergent properties of the system that you cannot see when they're represented. You've got secret resentments, someone who's hidden but really makes all the decisions, someone who sells drugs, there's brothers, the football team, all agencies. It's an environment that's as messy and as complex as humans are.

[00:23:10] However, we reduce everything to matrices. We talk about potential, we talk about performance, and we tend to invest accordingly to drive these organizations that are struggling. So of course an organization will invest in high potential, high performance. I'd like to know what the science is behind the determination of that. And the people that are in the lower corner are left out.

[00:23:44] The reality is that if you think about a sports team, which is the analogy that I think is closest to our world of work, without those players that don't always score, without those players that maybe are smaller in a scrum, the biggest players would not be able to score. And so I argue that we need a bit of both.

[00:24:17] This is why I think we should focus on turning all of our organizations into learning organizations. Meaning organizations that, yes, are non-deterministic, yes, are complex, but organizations that attempt to arrange themselves so that they can foster learning, both in individuals and in the organization as a whole. This is where I think systems thinking comes into play. Systems thinking is about encouraging people to learn to see their work as part of the larger system: not just this thing that I'm making, but as part of this organization that needs to change. And personal mastery is about a commitment to personal learning and to the learning of the organization, as a part of the organization, to facilitate the retention of domain knowledge and expertise that otherwise is lost.

[00:25:35] Going back to organizations, they are all based on an intentional foundation. It's not that organizations just crop up. They are based on an idea, whether it's Nike, I want to make the best sneaker for running, or Vodafone, the organization where I am: we want to enable people to communicate. If we take all these elements that we've talked about, the magic spot is where the organizational goal, or the intent, and the group goal, so the team objectives and the team learning, intersect the individual goals. If we can find that spot in the middle, we will find an area where people get better, the team gets better and the people near them get better. It is a very intentional pursuit. It's a difficult one, but it's one that will yield the biggest results.

Takeaways

[00:26:54] Now, to conclude, and there's much more that I could talk about on this: when people don't understand where they stand in an organization, they will back themselves into a corner, and they will not participate in the improvements that are required to really transform from the inside.

[00:27:27] If there are a few things that I would like you to take away, I would like you to remember this. That everything is connected, and whether you're part of a transformation or not, you need to consider the system in order to make real and sustainable change. That you need to innovate in the context of the organization; don't just go and do something in the corner and then hope that it will fit. That diversity of talent is absolutely fundamental to create organizations that can respond to different pressures and to black swan events. That you need to foster continuous learning, organizationally and personally. And that humans are the key to everything. Thank you.