Pawel Brodzinski on Leadership in Technology

Whatever it takes to lead a team, build a product, and run a business

Tag: team management

Why Collective Intelligence Beats Individual Intelligence

As long-term readers likely know I am a big fan of the idea of collective intelligence and big proponent of optimizing teams toward high collective intelligence.

First, what is collective intelligence? The easiest way of explaining that is through the comparison to individual intelligence (IQ). While IQ tests differ in type the pattern is similar: we ask an individual to solve a set of complex problems; the better they perform the higher their IQ is.

By the same token, we can measure intelligence of teams through measuring how well a group solves a series of complex problems.

There are a few very interesting findings in the original research on collective intelligence. It all starts with an observation that collective intelligence beats the crap out of individual intelligence. In highly collectively intelligent teams’ solutions provided by a group were systematically significantly better than solutions offered by any individual, including the smartest person in the room. However, even in teams with low collective intelligence the group solutions were on par with the best option provided by an individual.

It totally makes sense when we think of it. No matter how smart the solution provided by an individual is it most likely can be improved through clues and suggestions provided by others. Either directly or indirectly. And it doesn’t matter whether the others are even smarter. The thing that matters is that they think differently.

This theme is portrayed well in some pop-cultural productions. In Sherlock series the protagonist surprisingly frequently refers to his sidekick—John Watson—as not too clever or even dumb. On even more occasions Sherlock stresses that he needs Dr. Watson to inspire his superior mind. It’s not that Watson is smarter than Holmes. It’s that together they are smarter than Holmes alone, even given his prodigious mind.

The same pattern has been exploited in House M.D. series, where the team’s effort was consistently beating individual effort. It was so even if the final solution was facilitated mostly through the brilliance of the main character.

As a matter of fact, collective intelligence in play is one of those things that you can’t unsee once you’ve seen it. Like the other day, when I was sharing the idea of a workshop with one of my colleagues and I mentioned one feature I’d love to add to the app I was going to use during the workshop. The problem was that we explored an idea to add that feature before and, because of some old architectural decisions, adding the feature was no easy feat. Thus, we gave up. My colleague listened to my complaints and asked why we wouldn’t just add a simple and dirty hack just for the sake of the workshop. I was so immersed with the whole context of how hard it was to do it properly that the idea wouldn’t even cross my mind, no matter how obvious it might sound in retrospect.

And it wasn’t even a context of a persistent team; merely an ad-hoc discussion in a random group. Think, how much more we contribute in a more permanent setup—in a team which shares the same context on a daily basis.

The interesting follow-up to the observation that collective intelligence is supreme is that collective intelligence doesn’t depend on individual intelligence. As a matter of fact, there’s no correlation between the two. In other words, hiring all the smartasses doesn’t mean they’d constitute a team of high collective intelligence.

It is likely better to support a brilliant mind with folks who aren’t nearly as eloquent but provide another, diverse, point of view that to get more of the brilliance. What’s more a team built out of people of average intelligence can be better off than a bunch of smart folks gathered together.

It is because collective intelligence—the brilliance of a group—isn’t fueled by smarts but by collaboration. Two critical factors for high collective intelligence is social perceptiveness and evenness of communication. The former is awareness of others, empathy, and unselfish willingness to act for the good of others. The latter is creating a space for everyone to speak up and facilitating the discussions so that all are involved roughly equally. Neither of these attributes directly taps into individual intelligence.

That’s, by the way, where pop-cultural references fall short. Neither Holmes nor House care about the collaborative aspect of work of their teams and both make a virtue out their utter lack of empathy. It means that their teams are of low collective intelligence. I can’t help but thinking how much they could have achieved had they been optimized more toward collective intelligence.

Most of our industry fall in the very same trap when hiring. Tremendous part of our recruitment processes is optimized toward validating individual skills following a subconscious belief that this is what’s going to make teams successful.

As Dan Kahneman observes in his classic Thinking Fast and Slow, if our brain can’t easily answer to a difficult question it subconsciously substitutes the question with a similar one which is easy to and treats the answer to the latter as if it was the answer to the former. In this context we may be substituting a difficult question about how a candidate would perform in a team with much simpler one about how they would perform individually. The problem is that the assessment of a candidate may be very different depending on which question we answered.

If we truly want to optimize our teams for good collaboration we need to focus on the aspects that drive collective intelligence. We need to focus on character traits that are not that easy to observe, and yet they prove to be critical for teams’ long-term success, such as perceptiveness, awareness, empathy, compassion and respect. Ironically, such a team will outsmart one built around smarts and wits.

October 3, 2018
Emergent Purpose

There are those presentations at conferences that stay with us for a long time, even if there seems to be no particular reason for that. And yet they keep coming back for one reason or another. One of such presentations for me was a discussion between Arne Roock and Simon Marcus from Lean Kanban Central Europe years back.

Even though the topic of the discussion was broader there is one context that keeps coming back to me. Autonomy and alignment. A recurring theme was that we can’t enable autonomy unless we have alignment around a strategy, a goal, or whatever is the thing that orchestrates individual efforts.

As Peter Senge in his classic The Fifth Discipline puts it:

To empower people in an unaligned organization can be counterproductive.

It obviously makes sense. I mean, distributing autonomy is all fine but also creates a risk that everyone would pull an organization toward a different direction. Alignment, which goes through understanding of a common goal, helps up to focus on rowing in the same direction.

At the same time, watching the session back then, I couldn’t help but thinking that we at Lunar Logic hadn’t been doing that. We’d been continuously distributing more and more autonomy to everyone and at the same time there hadn’t been any official strategic purpose set for the organization for quite some time.

It the spirit of the discussion between Arne and Simon, who I both respect a lot, that should feel wrong. And yet it didn’t.

I could even remember my earlier discussions with Jabe Bloom. Jabe was pointing how important were techniques he adopted to help people connect their everyday behaviors with strategic goals.

Nonetheless, I still felt like imposing a strategy onto Lunar Logic would be a bad move.

It was months later when I came across the concept of emergent purpose. In its spirit it’s all about understanding organizational culture. It starts with an assumption that everyone at an organization has their individual purpose and it is only natural to pursue that individual purpose. It means that, given no other guidance, everyone would work toward achieving their own personal goals. Some people would have goals similar to others. Some would have very distinct aspirations. Some would have much stronger drive to achieve their own goals than other who would be fine going with the tide.

If we tried to visualize that as forces pulling an organization in different directions it might have looked like this.

As a matter of fact it would also mean that there is an aggregated force pulling the organization in some direction. And that aggregated force is exactly an emergent purpose.

By its design we don’t set an emergent purpose. It’s simply the outcome of individual purposes. It also means that for some people in an organization the emergent purpose may be the exact opposite of what they individually want. That’s all fine.

Despite the fact that it’s an emergent property of any organization, we have means to influence the emergent purpose. It happens through hiring. When someone leaves an organization their influence on emergent purpose disappears. At the same the organization hires someone new whose expectations may be better aligned with the emergent purpose.

Through such a change the emergent purpose has been amplified.

There is interesting dynamics in that process. If my own goals are aligned with the purpose of the organization I’m with, it is less likely that I’d leave the organization than if it was otherwise. And corollary to that, my chance of being hired and wanting to join an organization is higher if there is alignment in place.

In other words emergent purpose tends to sustain and even amplify itself, even with no conscious effort from leaders of an organization.

The final, and most important bit about the idea of emergent purpose is that every organization has an emergent purpose. It doesn’t matter whether they have an official strategic purpose or not, or how strong it is, or whether there is alignment between a strategy and an emergent purpose. It’s always there, as the only way to get rid of it would be to make people stop having any ambitions, which is an equivalent of not having any people in an organization, I guess.

That’s exactly where the fun starts. Given that there always is an emergent purpose, we’d be dumb not to listen to it. Now, I don’t say we necessarily need to pursue it actively, yet understanding it is crucial.

The reason is that whatever strategy we choose there will likely be a gap between that strategy and the emergent purpose. The bigger the gap the more people would get disengaged and likely eventually leave. From that perspective there is a price to pay for any strategy and, simply put, the better we understand the emergent purpose the better we are suited to achieve our strategic goal. Also, in simple economic terms, there may be strategies that simply are too costly to pursue.

Ideally, you can do what we did at Lunar Logic. We basically turned our emergent purpose to a company strategy. Instead of imposing a strategy on everyone we listened to each other and figured what’s the most desired path we want to pursue for the time being. That’s how we evolved our aspiration from helping to build products for our customers efficiently to helping the customers to succeed with their products. The latter isn’t focused on the building part nearly as much as the former.

Interestingly enough, out of the potential strategies that we discussed there was one which would make me leave the company eventually. Luckily for me it didn’t end up being our emergent purpose after all.

Of course I understand that few companies would go as far as we did. Even though I think it is an awesome idea I don’t encourage organizations to make that bold move. Nevertheless, knowing what the gap between aspirations of leaders of a company and everyone else is crucial if we look for any reasonable level of sustainability.

Finally, emergent purpose is also one of possible answers for autonomy and alignment issue. As long as we understand what an emergent purpose is we can decide to stick with it or just slightly shape it instead of building alignment externally through officially set strategic goals.

June 1, 2018
Empathy and Respect: What Makes Teams Great

I’ve been known to bring up research on collective intelligence in many situations, e.g. here, here, or here. In my personal case, the research findings heavily influenced my perception of how to build teams and design organizations. The crucial lesson was that social perceptiveness and having everyone being heard in discussions were key to achieve high collective intelligence. This, in turn, translates to high effectiveness of a team in pretty much any flavor of knowledge work.

Since the original work was published, the research has been repeated and findings were confirmed. Nevertheless, in software industry we tend to think we are special (even though we are not) and thus I often hear an argument that trading technical skills for social perceptiveness is not worth it. The reasoning is that technical skills easily translate to better effectiveness in what is our bread and butter—building software. At the same time fuzzy things, like e.g. empathy, do not.

The research, indeed, was run on people from all walks of life. At the same time every niche has some specific prerequisites that enable any productivity. I don’t deny that there is specific set of technical skills that is required to get someone contributing to work a team tires to accomplish. That’s likely true in an industry and software development is no different.

As a matter of fact, enough fluency with engineering is something we validate first when we hire at Lunar Logic. The way we define it, though, is “good enough”. We want to make sure that a new team member won’t hamper a team they join. Beyond that, we don’t care too much. It resonates with a simple realization that it is much easier to learn how to code than it is to develop empathy or social perceptiveness in general.

The whole approach is based on an assumption that findings on collective intelligence hold true in our context. Now, do they?

Google is known to be on their quest to find what’s the perfect team for years. Some time ago they shared what they learned in a few year-long research that involved 180 Google teams. It seems they confirmed pretty much everything that has been in the original Anita Woolley’s team work.

It’s not the technical excellence that lands teams in the group of accomplishers. By the way, neither is management style—it was orthogonal to how well teams were doing. The patterns that were vividly seen were caring about other team members and equal access to discussion time.

What’s more, the teams which did well against one goal seemed to do well against other goals as well. Conversely, teams that were below average seemed to be so in a consistent manner. The secret sauce seemed to work fairly universally against different challenges.

What a surprise! After all, we are not as special as we tend to think we are.

I could leave it here, as one of those “You see? I was right all that time!” kind of posts. There is more to learn from the Google story, though. Aspects that are mentioned often in the research are norms, either explicit or implicit. This refers to specific behaviors that are allowed and supported and, as a result, to organizational culture.

When we are talking about teams, we talk about culture pockets as teams, especially in a big organization, may differ quite a bit one from another.

It seems that even slight changes, such as attitude in group discussions, can boost collective effectiveness significantly. If we look deeper at what drives such behaviors we’ll find two keywords.

Empathy and respect.

Empathy is the enabler of social perceptiveness. It is this magic powder that makes people see and care for others. It pays off because empathic person would likely make everyone around better. Note: I’m using a very broad definition of empathy here, as there is a whole discussion how empathy is defined and decomposed.

Then, we have respect that results in psychological safety, as people are neither embarrassed nor rejected for sharing their thoughts. This, in turn, means that everyone has equal access to ongoing conversations and they are heard. Simply put, everyone contributes. Interestingly enough, it is often perceived as a nice-to-have trait in organizations but rarely as the core capability, which every team needs to demonstrate.

Corollary to that is an observation that both respect and care for others are deep down in the iceberg model of organizational culture. It means that we can roughly sense what are capabilities of an organization when it comes to collective intelligence. It’s enough to look at the execs and most senior managers. How much are they caring for others? How respectful are they? Since the organizational culture spreads very much in a top-down manner it is a good organizational climate metric.

I would risk a bold hypothesis that, statistically speaking, successful organizations have leaders who act in respectful and empathic way. I have no proof to support the claim, and of course there’s anecdotal evidence how disrespectful Steve Jobs or Bill Gates were. That’s why I add “statistically speaking” to this hypothesis. Does anyone have a relevant research on that?

Finally, there is something that I reluctantly admit since I’m not a believer in “fake it till you make it approach”. It seems that some rules and rituals can actually drive collective intelligence up. There are techniques to take turns in discussions. On one hand it creates equal access to conversation time. On the other if fakes respect in this context. It challenges ego-driven extroverts and, eventually, may trigger emergence of true respect.

Similarly, we can learn to focus on perception of others so that we see better how they may feel. It fakes empathy but, yet again, it may trigger the right reactions and, eventually, help to develop the actual trait.

In other words we are not doomed to fail even if so far we paid attention to technical skills only and we ended up with an environment that is far too nerdy.

However, we’d be so much better off if we built our teams bearing in mind that empathy and respect for others are the most important traits for candidates. Yes, for software developers too.

September 5, 2016
Hierarchy Is Bad For Motivation

Whenever a topic of motivation at work pops up I always bring up Dan Pink’s point. In the context of knowledge work, in order to create an environment where people are motivated we need autonomy, mastery, and purpose.

The story is nice and compelling. However, what we don’t realize instantly is how high Dan Pink sets the bar. Let me leave the purpose part aside for now. It is worth the post on its own. Let’s focus on autonomy and mastery.

First of all, especially in the context of software development, there’s a strong correlation between the two. Given that I have enough autonomy in how I organize my work and how the work gets done, I most likely can pursue mastery as well. There are edge cases of course, but most frequently autonomy translates to mastery (not necessarily so the other way around though).

The problem is that the way organizations are managed does not support autonomy across the board. Vast majority of organization employs hierarchy-driven structures. A line worker has a manager, that manager has their own manager, and so on and so forth up to a CEO.

The hierarchy itself isn’t that much of an issue though. What is an issue is how power is distributed within the hierarchy. Typically specific powers are assigned to specific levels of management. A line manager can do that much. A middle manager that much. A senior manager even more. Each manager is a ruler of their own kingdom.

Why is power distribution so important? Well, ultimately in knowledge organizations power is used for one purpose: making decisions. And decision-making is a perfect proxy if we are interested in assessing autonomy.

Of course each ruler has a fair level of flexibility when it comes to decide how the decision-making happens in their teams. There are, however, mechanisms that discourage them to change the common pattern, i.e. a dictatorship model.

The hierarchical, a.k.a. dictatorship, model has its advantages. Namely it addresses the risks of indecisiveness and accountability. Given that power is clearly distributed across the hierarchy we always know who is supposed to make a decision and thus who should be kept accountable for it.

That’s great. Unfortunately, at the same time it discourages attempts to distribute decision-making. As a manager I’m still kept accountable for all the relevant decisions made so I better make them myself or double-check whether I agree with those made by a team.

This in turn means that normally there’s very little autonomy in hierarchical organizations.

It brings us to a sad realization. The most common organizational structures actively discourage autonomy and authority distribution.

If we come back to where we started – what are the drivers for motivation – we would derive that we should see really low levels of motivation out there. I mean, vast majority of companies adopt the hierarchical model as it was the only thing there is. Not only that though. Even within hierarchical model we may introduce a culture that encourages autonomy, yet very, very few companies are doing so.

We could conclude that if the above argument is true we would expect really low levels of motivation globally in the workforce. It is a safe assumption that high motivation would result in engagement and vice versa.

Interestingly enough Gallup run a global survey on employee engagement. The bottom line is that only 13% of employees are engaged in work. Thirteen. It would have been a shock if not the fact that we just proposed that one of the current management paradigms – a prevalent organizational structure – is unsuitable to introduce autonomy across the board and thus high levels of motivation.

In fact, active disengagement, which would translate to being openly disgruntled, is universally more common that engagement. Now, that tells a story, doesn’t it?

What we look at here is that modern workplace is not well-suited for achieving high motivation and high engagement of employees. There are certain things that can change the situation within structural constraints. There are good stories on how to encourage the right behaviors without tearing down the whole hierarchy.

It is also a challenge for a dominant management paradigm that makes a rigid hierarchy a prevalent and by far the most popular organizational structure out there. While such hierarchy addresses specific risks it isn’t the only way of dealing with them. The price we pay for following that path is extremely high.

I for once consider that price too high.

June 22, 2015
Why We Want Women in Teams

One of the messages that I frequently share is that we need more women in our teams. By now I’ve faced the whole spectrum of reactions to this message, from calling me a feminist to furious attacks pointing how I discriminate women. If nothing else people are opinionated on that topic and there’s a lot of shallow, and unfair, buzz when it comes to role of women in IT.

Personally, I am guilty too. I’ve been caught off guard a few times when I simply shared the short message – “we need more women in our teams” – and didn’t properly explained the long story behind.

Collective Intelligence

The first part of the story is the one about collective intelligence. We can define the core of our jobs as solving complex problems and accomplishing complex tasks. We do that by writing code, testing it, designing it, deploying it, but the outcome is that we solved a problem for our customer. In fact, I frequently say that often the best solution doesn’t mean building something or writing code.

If we agree on problem solving frame a perfect proxy for how well we’re dealing with it is collective intelligence. Well, at least as long as we are talking about collaborative work.

Anita Woolley’s research pointed factors responsible for high collective intelligence: high empathy, evenness of communication in a group and diversity of cognitive styles. These are not things that we, as the industry, pay attention to during hiring. Another conclusion of the research is that women are typically stronger in these aspects and thus the more women in a team the higher collective intelligence.

Role of Collaboration

There are two follow up threads to that. One is that the research focused only on one aspect of work, which can be translated to collaboration. That’s not all that counts. We can have a team that collaborates perfectly yet doesn’t have the basic skills to accomplish a goal. Of course all the relevant factors should be balanced.

This is why at Lunar Logic, during hiring process, we verify technical competences first. This way we know that a candidate won’t be a burden for a team when they join. Once we know that somebody’s technical skills are above the bar, we focus on the more important aspects, but the first filter is: “can you do the job?”

The decision making factors are those related to the company culture and to collaboration.

Correlation and Causation

Another thread is that “more women” message is a follow up to an observation that women tend to do much better in terms of collective intelligence. I occasionally get flak for mentioning that women are more empathetic. It would typically be a story about a very empathetic man or a woman who was a real bitch and ruined the whole collaboration in a team.

My answer to that is I don’t want to hire women. I want to hire people who excel at collaboration. If I ended up choosing between empathetic man and a cold-blooded female killer it would be a no-brainer to me. I’d go with the former.

What is important though is that statistically speaking women are better if we take into consideration aforementioned aspects. It’s not like: every woman would be better than any man. It’s like: if we’ve been hiring for these traits we’d be hiring more women than men.

And that’s where a discussion often gets dense. People would imply that I say that women are genetically better in, say, collaboration. Or pretty much the opposite, they’d say that in our societies we raise women in a way that their role boils down to “good collaborators” and not “achievers.”

My answer to that is: correlation doesn’t mean causation. I never said that being a women is a cause of being empathetic and generally functioning better in a group. What I say is that there is simply correlation between the two.

The first Kanban principle says “start with what you have” and I do start with what I have. I’m not an expert in genetics and I just accept the situation we have right now and start from there.

The Best Candidate

A valid challenge for “hire more women” argument is that it may end up with positive discrimination. My point in the whole discussion is not really hire women over men. In fact, the ultimate guidance for hiring remains the same: hire the best candidate you can.

It just so happens that, once you start thinking about different contexts, the definition of “the best candidate” evolves. A set of traits and virtues of a perfect candidate would be different than what we are used to.

And suddenly we will be hiring more women. Not because they are women. Simply, because they are the best available candidates.

Such a change is not going to happen overnight. Even now at Lunar I think we are still too much biased toward technical skills. And yet our awareness and sensitivity toward what constitutes a perfect candidate is very different than it was a few years ago. That’s probably why we end up hiring fairly high percentage of women, and yet we’re not slaves to “hire women” attitude.

Finally, I’d like to thank Janice Linden-Reed for inspiration to write this post. Our chats and her challenges to my messages are exactly the kind of conversations we need to be having in this context. And Janice, being a CEO herself and working extensively with IT industry, is the perfect person to speak up on this topic.

May 12, 2015
Culture Pockets

Organizational culture is one of these areas that I pay a lot of attention to. Over years I started valuing the role of the culture increasingly more and more. The biggest difficulty though is that organizational culture is a challenging beast to control.

Organizational Culture

organizational culture
the behavior of humans who are part of an organization and the meanings that the people react to their actions
includes the organization values, visions, norms, working language, systems, symbols, beliefs, and habits

If we look at how organizational culture is defined there are two things that are crucial. One thing is that is a culture is formed of behaviors of all people in an organization. The other is that it’s not only about behaviors but also about what drives these behaviors.

When we look at it we realize that there’s no easy way to mandate a culture change. We can’t simply say: from now on we are a learning organization or that we will value collaboration starting on June the 1st.

If we want to see a change of a culture we need to see change in behaviors. Bad news though is that change of behaviors can’t really be mandated either. I mean we can install a policeman who will make sure that everyone behaves according to the new policy we issued. What would happen when a policeman is gone? We can safely assume that over time more and more people would retreat back to the old status quo – behaviors they knew and were comfortable with. The change would be temporary and ephemeral.

Identifying Culture

If we want to approach a cultural change we first need to understand the existing culture. What is valued? What principles the organization lives by? How is it reflected in everyday behaviors? Without understanding the starting point changes would be rather random and doomed to fail.

How to identify the culture then? Look at behaviors. Ultimately the culture is a sum of behaviors of people who are a part of the organization.

There is a serious challenge that we’re facing on that front. Not everyone has equal influence over organizational culture. In fact, the higher in the hierarchy someone is the more influence they typically have.

The mechanism is simple. Higher up in the hierarchy I have more positional power and my decisions affect more people. One specific type of decision I make, or at least strongly influence, is who gets promoted in my team. Given all my biases, I will likely promote people who are similar to me, share similar values, and behave in similar way. I perpetuate and strengthen the existing culture.

That’s by the way the rationale behind an advice I frequently share: if you want to figure out what the organizational culture of a company is look at its CEO. The CEO typically has the most positional power and thus their influence over the company is the biggest one. The way they behave will be copied and mimicked across the board.

Of course we need to pay attention to everyday behaviors and not to what is the official claim of the CEO. Very frequently there would be a gap between the two. That’s something I call authenticity gap. An organization claims one thing but everyday behaviors show another. For example they claim to care about customer satisfaction and then they bullshit their customers when it comes to share the project status.

This alone says something about culture too (and not a good thing if you need to ask).

Culture Change

How do we influence the cultural change then? If we can influence the factors that drive behaviors, and thus the culture, resulting changes would influence the culture. It’s even better. When we’re changing organizational constraints we potentially influence change of behaviors across the board and not only in an individual case.

We already established though that not everyone has equal influence on the culture. People at the top, in the long run, will have an upper hand. First, they control who gets promoted and as a consequence who has positional power. Second, that power is needed to change organizational constraints: introduce new rules, change the existing ones, and establish what acceptable and what’s not.

A simple answer how to change organizational culture would be to get top management on board, and help them understand what it takes to influence the culture.

Unfortunately, few have comfort of doing that.

Does it mean that we are doomed? Does it mean that without enlisting top ranks any attempt to change organizational culture will fail? Not necessarily so.

Culture Pockets

I believe I learned about the concept of culture pockets from Dave Snowden in one of his presentations. The basic idea is that within a bigger, overarching culture we can develop and sustain a different culture.

Another label that is used to describe this concept is a culture bubble.

When we think about this frame, from the top of our heads we can come up with some examples. One would be multinational organizations that have offices all around the world. Because of geography and cultural differences each of the local offices will have at least slightly different organizational culture. You would expect to see a different vibe in an office in India, in Poland, and in USA, even if they are the parts of the same company. Even if that company has pretty uniform culture.

There are examples of introducing culture pockets or culture bubbles when everyone works in the same building too.

One such idea is Lean Startup. One obvious context of applying Lean Startup ideas are startups. Another, and quite a common one, is when big organizations decide to build their product according to Lean Startup principles.

Such a team would operate very differently and very independently from the rest of the organization. Constraints would be different and so would be everyday behaviors. We’d have a culture pocket.

Another similar example is Skunkworks. It’s an idea developed by Lockheed Martin and it boils down to a similar pattern. Lockheed Martin would occasionally run a project in Skunkworks – a very independent team that has a lot of freedom and autonomy. Clearly without all the typical constraints enforced by the company their culture is different than one seen in majority of the company. By the way, a project in this case means designing and building a whole new fighter aircraft or something of similar complexity.

If we go by that analogy, every team can be a culture bubble. It is enough that the constraints within which that team operates are different from those that are standard for the whole organization. This type of culture pocket can go only as far as the team has positional power to redesign their constraints of course. The more positional power there is the bigger the difference of what is happening within and outside of a culture bubble.

Creating a Culture Bubble

Creating and maintaining a culture pocket is a balancing act. One thing is kicking off the change. That would typically mean someone defining different rules for a part of an organization. It can simply be a team of a few people.

Normally any positional power would be an attribute of a manager. This means that such a change needs to involve that manager. They need to change rules, norms, and expected behaviors. Alternatively they need to let others decide about such stuff, i.e. give up on the power they’ve been assigned.

There is another role for mangers in a setup too. They main responsibility is to sustain the culture bubble. When a culture pocket is established there’s effort needed to keep it going within broader, sometimes even unfriendly, culture of the whole organization.

To give you an example, from a perspective of the whole organization it doesn’t matter at all how decisions are made in a team. What matters that there is no problem with indecisiveness and accountability. The way most organizations understand these concepts would mean that a manger has to be decisive and can be kept accountable. It may still be true even if decisions are made by the whole team using e.g. a decision making process.

Fragility of Culture Pockets

The biggest risk related to culture pockets is that they are fragile. Typically they base on the fact that some people, who were in power, distributed that power for a better good. It doesn’t mean, however, that when they are replaced with someone else a new person will keep a similar attitude.

A safe thing in such a situation is to adjust to whatever is the overarching culture of the whole organization. It means that a culture bubble is gone as there’s no longer anyone who take cares of translating the two cultures back and forth.

The message I have is twofold. On one hand if we want to see a fundamental and sustainable cultural change we need to get top ranks involved eventually. Without that we won’t address the risk of fragility of culture pockets. On the other hand, a simple fact that in a big organization we can’t simply change the culture of the whole company doesn’t mean that we have no options whatsoever.

From my experience culture pockets, even if fragile and to some point ephemeral, are a perfect vehicle for self-realization of people inside. For people in leadership and management positions they are sometimes the only way to maintain internal integrity.

Finally, sometimes it is the only option if we want to influence the cultural change.

April 30, 2015
Why We Fail to Change

I’d love to get a beer each time I hear a story about management imposing a change on teams and facing strong resistance. It would be like an almost unlimited source of that decent beverage. Literally every time I’d fancy a beer I’d be like “Hey, does anybody have an agile implementation story to share?”

One common excuse is that people don’t like the change. That is surprising given how adaptable humankind has proven to be. I rather subscribe to the idea that people don’t mind the change; they don’t like being changed.

Unfortunately being changed part is the story of oh so many improvement initiatives. Agile implementations are among most prominent examples of these change programs of course.

So how is it really with responding to changes?

First, it really helps to understand typical patterns of introducing change. The model I find very relevant is Virginia Satir’s Change Model. Let me walk you through it.

We start with existing status quo that translates to a performance level. Then we introduce something new, which we call a foreign element.

Then we see an expected improvement and they lived happily ever after. Actually, not really. In fact whenever I draw that part of the model and ask what happens next people intuitively give pretty good answers.

After introducing a change performance drops.

It is kind of obvious. We need time to learn how to handle a new tool, practice, method or what have we. Eventually, we get better and better at that and we start seeing the results of promised improvements. Finally, we internalize the change and the cycle is finished.

Because of its shape the curve is called a J-curve.

It is an idealized picture though. In reality it is never such a nice curve.

What we really see is something much bumpier. It is bumpy already when we maintain status quo. It gets much bumpier when we start messing with stuff. It’s not only that rough average goes down but also worst case scenario goes down and by much more.

It’s pretty much chaos. In fact, that’s exactly how this phase is called in the original Virginia Satir’s model.

An interesting observation we can make is that the phase called resistance is a short one that happens just after introducing a foreign element. Does it mean that we should expect resistance against the change to be short-lived?

Yes and no. Yes, if we consider only “I’m not even going to try that new crap” type of resistance. It is typically driven by lack of understanding why the whole change was proposed in the first place. There is however the whole range of behaviors that happen later in the process that we would commonly call resistance too.

Some people aren’t ready to see, even temporary, drop in performance and once they face it they propose to get back to the old status quo. When facing a stressful situation many people retreat back to what they know best and the old ways of doing things is exactly what they know best. There are also those who are impatient and not willing to give people enough time to learn the ropes. The last group often includes managers who funded the change in the first place.

In either case the result, eventually, is the same. More resistance.

Inevitably we reach a pivotal moment. We’ve been through the bumpy ride for quite some time already and yet we haven’t gotten better. In fact, we’ve gotten worse. Not only that. We’ve gotten worse and less predictable. The whole change doesn’t seem like such a good idea after all.

So what do we do?

Of course we reverse the change and go back to the old status quo. Oh, and we fire or at least demote that bastard who tricked us into starting the whole thing.

One interesting caveat of the whole process is that a change is not always simply reversible. When we changed specific behavior and yet didn’t get expected outcomes reverting the behaviors may be difficult if not impossible.

For the sake of the discussion let’s assume we are lucky and the change is reversible. We are back to the late status quo and we simply wasted some time trying something new. Oh, and we built a stronger case for resisting the next change. We petrified the existing situation just a little bit more.

One reason why changes are reverted so often is the perceived risk of the change.

Pretty good proxy for perceived risk is predictability. Typically the more unpredictable a team or a process is the more risky it is considered. In this case, the important thing that comes along with a foreign factor is how much predictability changes. Not only does performance drops but it also becomes much less predictable.

While the former alone might have been bearable, both factors combined contribute to the perception that the change was wrong in the first place.

There is another dimension that is very interesting for the whole discussion. It is the scale of change. How much we want to change the existing environment: team, process, practices, etc.

We can imagine a series of small changes, each modifying the context only slightly. The whole series lead to a similar outcome as one big change rolled out at once.

We can call one approach evolutionary and the other revolutionary. We can use inspiration from Lean and call evolutionary approach Kaizen and revolutionary one Kaikaku.

Fundamentally the J-curve in both approaches would be shaped the same. The difference is in the scale. The revolutionary change means one big leap and rolling out all the new stuff at once. This means a single big J-curve.

The evolutionary approach introduces a lot of tiny J-curves one after the other. In fact it is possible to have a few of changes run concurrently but let’s not complicate the picture any more.

What are the implications?

Let’s go back to the scale of the risk we undertake. With Kaikaku unpredictability we introduce is much higher than what we’ve seen in the late status quo.

Kaizen on the other hand typically go with the changes small enough that we don’t destabilize the system nearly as much. In fact it is pretty likely that unpredictability introduced by each of the small changes will be almost invisible given that we don’t deal with fully predictable process anyway.

The risks we take with evolutionary approach are much more bearable than ones that we deal with rolling out one big change.

That’s not all though.

Another thing is how much destabilization lasts. In other words what is cycle time of change.

Big change, naturally, has much longer cycle time as it requires people to internalize much more new stuff. It means that exposure to the risks is longer. Given that the risks are also bigger it raises the odds that the change will be reverted before we see its results.

With small changes cycle time is shorter and so is exposure to the risks. Again, not only are the risks much smaller but also they are mitigated much faster.

One last thing worth mentioning here is that so far we optimistically assumed that all the proposed changes have positive outcome. That is not always true.

With the evolutionary approach even if some of the changes don’t yield expected results we still gain from introducing others. With a revolutionary approach each part that doesn’t work simply increase likeliness of reverting the whole thing altogether.

It is not to say that Kaizen is always superior to Kaikaku. In fact both evolutionary and revolutionary approaches have their place. Stuart Kauffman’s Fitness Landscape helps to explain that.

Imagine a landscape that roughly shows how fit for purpose your organization is. It should simply translate to factors such as productivity etc. The higher you are the better.

The simplest and safest way to climb up would be to make small steps uphill.

While the approach works very well, eventually we reach a local peak. If we continue our small steps in any direction it would result in lower fitness for purpose. Simply said we wouldn’t perform as well as we did at the peak.

If we look only at the closest terrain we might as well say that we’re already perfect and there’s no need to go further.

Obviously, someone saying that wouldn’t be treated seriously. Well, not unless we are discussing a patient of a mental facility.

The solution is seen when we look at the big picture. If we moved to the slope of another hill we can get better than we are.

That’s exactly when we need a big jump. It doesn’t have to automatically land us in a better situation than the one we’ve been at initially. The opposite would often be the case. What is important though is that we land on the hill that is higher. That translates to bigger potential for improvement.

Once there we can retreat back to good old strategy of small steps that allow us to climb up. Eventually we reach the peak that is higher than the previous one. Then we can repeat the whole cycle looking for even a bigger hill.

Of course, similarly to the case of J-curves the picture here is idealistic in a way that each change, be it small or big, is a successful one. In reality it is more of experimentation. Some of the changes would work, some not.

As you might have guessed, small steps here represent the evolutionary approach or Kaizen. A big jump is an equivalent of revolutionary change or Kaikaku. Depending on the context one or the other will be more useful.

In fact, there are situations when one of the strategies will be basically useless. That’s why introducing change without understanding current context is simply begging for failure.

One more implication of the picture is that, given lack of any other guidance, evolutionary approach is both less risky and more likely to succeed. That’s why I prefer to start with when I’m unsure about the context which I’m operating within.

One last remark on the Fitness Landscape. What you’ve seen here is a heavily oversimplified view. In reality fitness landscape wouldn’t be two-dimensional. Stuart Kauffman discussed it as three-dimensional model although I tend to think of it as of a multi-dimensional model.

It means that each change can improve our situation in some dimensions and have an opposite result in others. We will have different combination of effects in different dimensions – some more desirable and some less.

If that wasn’t enough the whole landscape is dynamic and it is continuously changing over time. In other words, even after reaching local optimum we will need further continuous improvements to maintain our fitness for purpose. The peak will be moving over time.

I know the post got long by now (thank for bearing with me that far by the way). This is however the starting point for the discussion why introducing the change very often triggers resistance. It provides pretty good explanation why some many improvement initiatives fail. This is also one of my answers to the question why many agile or lean adoptions are doomed to failure from the day one.

Trying to significantly change an organization without understanding some underlying mechanisms is simply begging for frustration and failure.

Finally, understanding the change models will influence the choice of the methods and tools we’d use to drive our change programs.

February 24, 2015
Decision Making Process

I’m a strong proponent of participatory leadership model where everyone takes part in leading a team or even an organization. A part of leading is making decisions. After all if all decisions still have to be made, or at least approved, by a manager it isn’t much of participatory leadership.

(Benevolent) Dictatorship

The most typical starting point is that someone with power makes all decisions. As a result commonly seen hierarchies are just complicated structures of dictatorships. As a manager within my small kingdom I can do what I want as long as I don’t cross the line drawn by my overlord.

Of course there are managers who invite the whole team to share their input or even distribute particular decisions to team members. There are leaders who use their power for the good of their people. It may be benevolent dictatorship. It is still dictatorship though.

This model works fairly well as long as we have good leaders. Indecisiveness isn’t a super-common issue and if it is there’s at least one person who clearly is responsible. Often leaders have fair experience in their roles thus they are well-suited to make the calls they make.

The model isn’t ideal form a perspective of promoting participatory leadership. If we want more people to be more involved in leading a team or an organization we want them to make decisions. And I mean truly make decisions. Not as in “I propose to do this but I ask you, dear manager, to approve this so that responsibility is, in fact, on you.” I mean situations when team members make their calls and feel accountable for them.

I’d go even further and propose that in truly participatory leadership model team members acting as leaders would make calls that their managers wouldn’t.

This isn’t going to happen with a classic decision making process.

Consensus

A natural alternative is a consensus-driven decision making process. A situation where we look for a solution that everyone agrees on.

This one definitely allows escaping dictatorship model caveats. It doesn’t come for free though.

Looking for consensus doesn’t mean looking for the best option, but rather looking for the least controversial option. These two are very rarely synonymous. Another issue is the tiredness effect. After a long discussion people switch to “I don’t care anymore, let someone make that decision finally and move on.”

Not to mention that the whole decision making process suddenly gets really time-consuming for many people.

While in theory consensus solves accountability problem – everyone agreed to a decision – in practice the picture isn’t that rosy. If I didn’t take active part in the discussion or my objections were ignored I don’t feel like it’s my decision. Also if the decision was made by a group I will likely feel that responsibility is distributed and thus diluted.

One interesting flavor of consensus-driven decision making is when people really care about the decision even though it is controversial. It’s not that they want to avoid participation or even responsibility. It’s just consensus is unlikely, if even possible.

Such a discussion may turn into an unproductive shit storm, which doesn’t help in reaching any common solution and yet it is emotionally taxing.

Advisory Process

There is a very interesting middle ground.

My pursuit of participatory leadership decision making became a major obstacle. I declined to use my dictatorship power on many occasions encouraging people to make their own calls. The answer for a question starting with “Can I…” would simply be “Well, can you?” That worked up to some point.

It builds the right attitude, it helps to participate in leading and it makes people feel accountable. The problem starts when such a decision would affect many people. In such a case we tend to retreat back to one of the previous models: we either seek consensus or look for a dictator to make that call for us.

Not a particularly good choice.

I found the solution while looking at how no management companies deal with that challenge. Basically, everyone acts as they had dictatorship power (within constraints). However, before anyone makes their call they are obliged to consult with people who have expert knowledge on the subject as well as with those who will be affected by the

This is called advisory process. We look for an advice from those who can provide us valuable insight either because they know more about the subject or because their stakes are in play. Ultimately, a decision is made by a single person though. Interestingly enough, a decision-maker doesn’t have to take all the insight from advisory process into account. Sometimes it is not even possible.

Accountability is clearly there. Healthy level of discussion about the decisions is there as well.

Constraints

The key part of going with such decision making scheme is a clear definition of constraints. Basically, a dictator, whoever that is in a given context, gives up power for specific types of decisions.

The moment a team member makes a call that is vetoed the whole mechanism is pretty much rendered irrelevant. It suggests that people can make the decisions only as long as a manager likes them. This isn’t just a form of dictatorship but a malicious one.

These constraints may be defined in any sort of way, e.g. just a set of specific decisions or decisions that don’t incur cost beyond some limit, etc. Clarity is important as misunderstanding on that account can have exactly the same outcomes as ignoring the rules. After all if I believe I could have made a decision and it turns are not to be true I will be disappointed and disengaged. It doesn’t matter what exactly was the root cause.

Setting constraints is also a mechanism that allows smooth transition from benevolent dictatorship to a participatory model. One super difficult challenge is to learn that I, as a manager, lost control and some decisions will be made differently than I’d make them. It’s better to test how it works with safe to fail experiments before applying the new model to serious stuff.

It also addresses a potential threat of someone willing to exploit the system for their own gain.

Learning the ropes is surprisingly simple. It doesn’t force people to go too far out of their comfort zones and yet it builds a sense of leadership across the board. Finally it provides a nice option for transition from the old decision making scheme.

And the best thing of all – it is applicable on any level of organization. It can be at the very top of the company, which is what no management organizations do, but it can be done just within a team by its manager.

February 12, 2015
Economic Value of Slack Time

I ranted on 100% utilization a few years ago already. Let me add another thread to that discussion. We have a ton of everyday stories that show how brain-dead the idea of maximizing utilization is. Sometimes we can figure out how it translates to work organization as well. Interestingly, what Don Reinertsen teaches us is that queuing theory says exactly the same.

As we go up with utilization lead time or wait time goes up as well. Except the latter grows exponentially. It looks roughly like that.

But wait, does it mean that we should strive to have as low utilization as possible? I mean, after all that’s where lead times are the shortest. This doesn’t sound sensible, right?

And it doesn’t make sense indeed. Cost of waiting is only one part of this equation. The other part is cost of idle capacity. We have people doing nothing thus they don’t produce value yet they cost something. From that perspective we have two cost components: delay cost related to long lead time and cost of idle capacity related to low utilization.

Of course the steepness of the curves would differ depending on the context. The thing is that the most interesting part of the chart is the sum of the costs which, naturally, is optimal at neither end of scale.

There is some sort of economic optimum for how much a system should be utilized to work most cost efficiently. There’s very good news for us though. The cost curve is the U-curve with flat bottom. That means that we don’t need to find the ideal utilization as a few percent here or there doesn’t make a huge difference.

We’d naturally think that the optimum is rather toward the more utilized part of the scale. That’s where the interesting part of the discussion starts.

We have pretty damn good idea how much idle time or slack time costs us. This part is easy. Now, the tricky question: how much is shorter lead time worth?

Imagine yourself as a Product Owner in a funded startup providing an online service. Your competitor adds a new feature that generates quite a lot of buzz on social media. How long are you willing to wait to provide the same feature in your app? Would keeping an idle team all the time just in case you need to build something super-quickly be justified?

Now imagine that your house is on fire. How long are you willing to wait for a fire brigade? Would keeping an idle fire brigade just in case be justified?

Clearly, there are scenarios where slight differences in lead time are of huge consequences. We don’t want our emergency calls to be queued for a couple of weeks because a fire brigade or an ambulance service is heavily utilized. In other words steepness of one of the curves varies a lot.

Let’s look how different scenarios change the picture.

This sets the economically optimal utilization at very different levels. There are contexts where a lot of slack is perfectly justified. The ultimate example I can come up with are most of armies. We don’t expect them to be fully engaged in wars all the time. In fact the more slack armies have the better. Somehow we don’t come up with an idea that if an army has no war to run we better find them one.

Of course it does matter how they use their slack time, but that’s another story.

We don’t have that drastic examples of value of slack in software industry. However, we also deal with very different steepness of delay cost curve. Even if we don’t expect instant delivery we need to move quicker and quicker as everyone else does the same.

The bottom line is that our intuition about what is the cost of wait time (delay cost) is often flawed. This means that even if we are able to go beyond the myth of 100% utilization we still tend to overload our teams too much.

Oh, and if you wondered, at Lunar Logic our goal is to keep team’s utilization between 80% and 90%.

January 29, 2015
On Feedback (Again)

I’ve heard that question quite a few times after I shared my feedback with somebody: “What am I supposed to do with such feedback?”

The question may imply that feedback e.g. wasn’t “actionable” or something. Anyway, I have an answer for that. It goes:

“Whatever the hell you want.”

Yup. Exactly that. In fact this is precisely what I’d love you to do.

As the opposite to: getting defensive, explaining yourself, finding excuses, bringing other interpretations, and so on and so forth.

Feedback is not an attack. You don’t need to defend yourself. It isn’t an interrogation either. You don’t need to explain yourself. And most of all it isn’t a goddamn appraisal. You don’t need to maximize the score.

It is feedback. I’m sharing some observations and opinions that somehow relate to your work, actions, behaviors, attitude, etc.

I don’t intend to change you. I want to provide you with more information so that your decisions about your further course of action are informed better. You can disagree with the part or the whole of the message you get. You can interpret it in a vastly different way. You can confront that with other feedback that is contrary to mine. That is all just perfect. You can ignore it altogether and I’m still fine with that.

Remember? Whatever the hell you want.

The reason is I know it is subjective. No matter how much I try to make it factual it is always about interpreting facts. And I don’t try to make it purely factual. In fact, the system in knowledge work is built of people and interactions between these people. How objective can “facts” about interpersonal relationships be? Is there even an objective truth there? Or is it rather a combination of interpretations that can be more or less aligned one with the other?

So no, I’m not trying to convince you that my point is even valid. It’s how I perceive specific situation and how I feel. Oh, it isn’t factual, someone would say. Well, the fact is that I perceive and I feel so and so. Do you want to discuss with such a fact? Didn’t think so.

I am well aware that my perceptions and my feelings aren’t universal truths. That’s why it is you who decide what to do with the feedback or whether to do anything at all.

There is the other part of the story. I sometimes receive feedback and I’m like “Thank you. I’m not going to change that.” What I see as a reaction is that someone is either discouraged or even pissed off with my reaction.

I mean, they did expect me to comply with what they shared with me. I don’t differentiate here feedback on work I do from feedback on my behaviors. It’s just, for whatever reasons, I decide that I don’t want to change that specific thing.

That doesn’t make me any less grateful for feedback I got by the way.

It’s just that now we turned the tables. Now it’s: Whatever the hell I want.

If you want to make me compliant with whatever just make it explicit. At least we’ll have common understanding.

Feedback’s role, the way I perceive it, is not compliance. It is providing information about one’s behaviors, actions and attitudes and their impact. It is, as its name suggests, about feeding one back with information, not about changing one or making them doing what somebody else want them to do.

If you give me feedback with a clear intention to change me or even worse to make me do what you want you are likely to end up being disappointed.

It will happen despite the fact that I treat that feedback as factual and fair. It is factual since fact is that you think and feel whatever you think and feel. It is fair for the very same reason.

At the same time it is subjective. Objective feedback, as long as it touches interactions between people, is a mirage. Or an oxymoron. Stop pursuing objectivity. To make it clear: it doesn’t make such feedback any less valuable.

Once we understand that it enables the whole new level of discussing feedback both ways.

Ultimately it’s: “I share that with you. Do whatever the hell you want with this.”

And: “Thank you for sharing. I will do whatever the hell I want with this, indeed.”

Only then it truly is valuable feedback.

January 26, 2015