Eight to Late

Sensemaking and Analytics for Organizations

An introduction to emergent design

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Over the last few months, I’ve published a number of posts in which the term emergent design makes a cameo appearance (see this article or this interview for example). Some readers may have noticed that although the term is used in various contexts in the articles/interviews, it is not explicitly defined anywhere. This is deliberate. Emergent design is…well, emergent, so a detailed definition is neither necessary nor useful – providing one can describe a set of guidelines for its practice.  My main aim in this post is to do just that. To keep things concrete I will discuss the guidelines in the context of technical initiatives in organisations.

(Note: Before going any further a couple of clarifications are in order. Firstly, the word emergent as used here has nothing to do with emergence in complex systems. Secondly, the guidelines provided here are a starting point, not a comprehensive list.)

The wickedness of technology

Most technical initiatives in large organisations are planned, designed and executed in a top-down manner, with little or no attempt to understand the existing culture and / or on-the-ground realities. This observation applies not only to enterprise software projects, such as those involving Collaboration or   Customer Relationship Management platforms, but also to the establishment of new functions such as building a data science capability.

Top down approaches are liable to fail because technical initiatives display many of the characteristics of wicked problems.  In particular, they are:

  1. Are one-shot operations – for example, an ERP system is simply too expensive to implement over and over again.
  2. Have no stopping rule – technical systems are never completely done; there are always things to be fixed and additional features to be implemented.
  3. Are highly contentious – whether or not an initiative is good, or even necessary, depends on who you ask.
  4. Could be done in other, possibly “better”, ways – and the problem is that one person’s “better” is another one’s “worse”!
  5. Are essentially unique – and don’t let vendors or Big $$$ consultants tell you otherwise!

These characteristics make technical problems socially complex  – that is, different stakeholder groups have different perceptions of the problem that the initiative is intended to address.   The most important implication of social complexity is that it cannot be tackled using rational methods of planning, design and implementation that are taught in schools, propagated in books, or evangelized by standards authorities and assorted snake oil salespersons.  For these reasons, it is more accurate to refer to such problems as sociotechnical problems.

Enter emergent design

The term emergent design was coined by David Cavallo in the context of technology-driven education reforms in indigenous cultures (the original paper by David Cavallo can be accessed here). Cavallo observed that traditional systems engineering approaches that attempt to change an educational system in a top-down manner fail primarily because they do not take into account the unique features of local cultures. Instead, he found that using the existing culture as a starting point from which to work towards systemic change offered a much better chance of the new ways taking root. In his words, “[the] adoption and implementation of new methodologies needs to be based in, and grow from, the existing culture.”

Cavallo’s words hold the key to applying emergent design in the context of sociotechnical initiatives: it is to take the concerns of those affected by the initiatives seriously.

“Ah, so it’s like Agile software development,” concludes the Agilista.

Not quite. As I will discuss in the remainder of this post, although emergent design shares a few number features with Agile methods, there’s considerably more to it than that. That said, chances are that good Agile coaches are emergent design practitioners without knowing it. This is something that will become apparent as we go on.

Guidelines for emergent design

I have, for a while, been thinking about what emergent design means in the context of sociotechnical initiatives in organisations. Among other things, I have been looking at how it might be applied to a wide variety initiatives that are traditionally planned upfront – things such as offshoring and enterprise-wide projects such as data warehouse or enterprise resource planning initiatives.

In one of those serendipitous occurrences, last week I happened re-read an old series of articles entitled Confessions of a post-SharePoint Architect written by  my friend, the ace sensemaker and emergent entrepreneur, Paul Culmsee. Although the series focuses on emergent design principles in the context of the Microsoft SharePoint platform, many of the points that Paul makes apply to sociotechnical initiatives in general.  In addition to material drawn from Paul’s blog, I also borrow from a few posts on my blog. In the interests of space I have provided only a brief overview of the points because they have been elaborated elsewhere. The original pieces fill in a lot of relevant detail, so please do follow the links if you have the inclination and the time.

With that said, let’s get to it.

Be a midwife rather than an expert

In a paper entitled, On the planning crisis, Horst Rittel  (the man who coined the term wicked problem) wrote:

You do not learn in school how to deal with wicked problems…expertise and ignorance is distributed over all participants in a wicked problem. There is a symmetry of ignorance among those who participate because nobody knows better by virtue of his degrees or his status. There are no experts (which is irritating for experts), and if experts there are, they are only experts in guiding the process of dealing with a wicked problem, but not for the subject matter of the problem.

The first guideline of emergent design is to realize that no one is an expert – not you nor your Big $$$ consultant. The only way to build a robust and lasting system or process is for everyone to put their heads together and work towards it in a collaborative fashion, dispensing with the pretence that one can outsource one’s thinking to the “expert”. Indeed, the role of the “expert” is to create and foster the conditions for such collaboration to occur. Paul and I elaborate on this point at length in our book and this paper (summarized in this post).

In brief, the knowledge required to successfully implement a  sociotechnical initiative is distributed across all stakeholders (analysts, consultants, architects and, above all, users). Pulling all this together into a coherent whole has more to do with facilitation and people skills than technology.

Ensure that governance is about enablement rather than control

Most organisations have onerous procedures to ensure that people do the right thing. I submit that many of these encourage a checkbox-based compliance mentality aimed at ensuring that people comply in letter, but not necessarily in spirit (actually, never in spirit).  As Paul mentions in this post  governance ought to be about enablement rather than compliance or control.

There’s a very simple test to tell one from the other: when you come across a procedure such as an SOP or a methodology that you are required to follow, ask yourself this question: does this help me do my job?

If the answer positive, the procedure is an enabler; if not, it is likely a control that is primarily intended as a CYA mechanism.

Do not penalize people for learning

The main rationale behind iterative and incremental approaches to technical projects is that they encourage (and take advantage) of continuous learning. Incremental increases in functionality are easier to test exhaustively and errors are also easier to correct. Reviews and retrospectives also tend to be more focused leading to a better chance of lessons actually being learnt.  Thanks to the Agile movement, this is now well known and understood in the world of tech.

However, learning is not just a matter of using iterative/incremental methodologies; one also needs to build an environment that encourages it. This is trickier matter because it depends on things that are outside an individual manager’s control; indeed it has more to do with the entire  function or even the organization. In an organisation with a strong blame culture, the culture tends to win against pretty much any methodology, agile or otherwise. Blame cultures preclude learning because mistakes are punished and people are scapegoated as a result. Check out this article on learning organizations for more on this topic, and this post for a more nuanced (realistic?) view.

With that said for the importance of learning, it is also important to note that there are some situations where learning is less important. This is the case for work for that can be planned and scripted in detail up front.  It is important to be able to distinguish between the two types of situations…which brings us to the next point.

Understand the difference between complicated and complex initiatives

Requirements analysis is one of the first activities in traditional system development. Most technical initiatives that are driven by a vendor or consultant will have many sessions for this at the front-end of an engagement. Enterprise wisdom tells us that things need to be specified in detail at the start. The rationale behind this is to set requirements in stone so that the entire project can be planned in detail before actual implementation begins.   Such an approach is fine if one knows for sure:

  1. How the future is going to unfold and has appropriate contingencies in place for adverse events;
  2. That users have a clear idea of what they want, and
  3. That requirements will not change (or will change minimally).

It is obvious that this approach will be disastrous if any of the above assumptions are incorrect. Unfortunately it is more often the case that the assumptions do not hold for sociotechnical initiatives.

So how does one distinguish between initiatives that can be planned in detail upfront and those that can’t?

The distinction is best illustrated via an example: consider a project to replace a fixed line phone system by VoIP versus an initiative to set up a data science function. The first project has a fixed set of requirements across different groups. The second one, in contrast, involves diverse stakeholder groups, each with their own unique expectations of the system. Both projects are complicated from the technology point of view, but the second one has elements of wickedness arising from social complexity.  Consequently, the two projects cannot be run in the same way.  In particular, the first one can be planned in detail upfront while the second one cannot.  Borrowing from David Snowden’s Cynefin framework, we call the first type of project complicated and the second one complex. You need to understand which kind of initiative you are dealing with before deciding which approach would be appropriate.

Beware of platitudinous goals

The  technology marketplace is one that is largely buzzword driven. An in-vogue buzzwords at the time this piece was written is  AI. Buzzwords, while sounding “right”, are actually platitudes – words that are devoid of meaning because different people interpret and use them differently.  The use of platitudes, therefore, results in confusion rather than clarity. For example, does it mean (in the context of an organisation) to “implement AI”?

People tend to use platitudes as mental shortcuts to avoid thinking things through and coming up with their own opinions. It is therefore pointless to ask a person who uses a platitude to clarify what he or she means: they have not thought it through and will therefore be unable to give you a good answer.

The best way to deconstruct a platitude is via an oblique approach that is best illustrated through an example.

Say someone tells you that they want to implement AI. Asking them to define AI is not a good way to go because the answer you get is likely to be couched in further platitudes such as higher automation and insight.  Instead, it is better to ask them what difference would be apparent if AI were to be implemented. To answer this question, they would have to come down from platitude-land and start thinking about concrete, measurable outcomes.

Use open questions to broaden perspectives

A more general point to note from the foregoing is that the framing of questions matters, particularly when one wants people to come up with ideas. For example, instead of asking people what they like (or dislike) about a particular approach, it is generally better to ask them what aspects of the approach are important to them. The latter question is neutrally framed so it does not impose any constraints on their thinking

Another good way to get people thinking about possibilities is to ask them what they would like to do if there were no constraints (such as budget or time, say).  Conversely, if you encounter a constraining factor (like a company policy), it is sometimes helpful to ask what the intent behind the policy is.

If posed in the right way and in the right environment, answers to such questions get people to think beyond their immediate concerns and focus on purposes and outcomes instead.

Check out Paul’s posts on powerful questions to find out more about these perspective-expanding questions.

Understand the need for different types of thinking

One of the ironies of sociotechnical initiatives is that the most important decisions have to be made when the information available is the least. As I wrote in the introduction to this paper,

The early stages of projects are fraught with ambiguity. Yet, it is at this “front end” of projects that the most important decisions have to be made. Front-end decisions are hard because there is:

  • uncertainty about scope, i.e. about what needs to be done;
  • uncertainty about rationale, i.e. why it needs to be done; and
  • uncertainty about approach, i.e. how it should be done.

Arguably, the lack of clarity regarding any of these can sow the seeds of failure in the early stages of a project.

The standard approach is to treat uncertainty as a problem that can be solved through convergent thinking – i.e. the kind of thinking that assumes a problem has a single “correct answer.” However, the uncertainty associated with sociotechnical projects has a social dimension; different people have different perceptions of things like scope and rationale, as well as different coping mechanisms for ambiguity.  Thus sociotechnical uncertainty is a wicked problem that has no single “right answer.” This can cause anxiety for some. One therefore needs to begin with divergent thinking, which is largely about generating ideas and options and move to convergent thinking only when:

  1. The group has a shared understanding of the main issues.
  2. An adequate set of options have been generated.

As I alluded to above, people tend to show a preference for one type of thinking over the other. The strength of collaborative problem solving lies precisely in the fact that a group is likely to have a mix of the two types of thinkers.

It is perhaps obvious, but still worth mentioning that the other standard way to deal with uncertainty is to impose a solution by diktat or governance. Clearly such approaches are doomed to fail because they suppress the problem instead of solving it.

Consider long term consequences

It is an unfortunate fact of life that cost tends to be the ultimate arbiter on organisational decisions. Vendors know this, and take advantage of it when crafting their proposals. The contract goes to the lowest bidder and the rest, as they say, is tragedy. Although cost is an important criterion in technical decisions, making it the overriding concern is a recipe for future disappointment.

The general lesson to draw from this is that one must consider the longer-term consequences of one’s choices. This can be hard to do because the distant future is less salient than the present or the immediate future. A good way to look beyond immediate concerns (such as cost) is to use the solution after next principle proposed by Gerald Nadler and Shozo Hibino in their book, Breakthrough Thinking. The basic idea behind the principle is to get people to focus on the goals that lie beyond the immediate goal. The process of thinking about and articulating longer-term goals can often provide insights into potential problems with the current goals and/or how they are being achieved.

Build in spare capacity

In his book on antifragility, Nicholas Taleb points out that the opposite of fragility is not robustness or resilience, rather it is the ability to thrive on or benefit from uncertainty. There is no word in the English language to describe such behavior, and that is what led him to coin the term antifragile.

In a post inspired by the book, I outlined the elements of an antifragile IT strategy.  One of the key points of such a strategy is the assumption that, despite our best laid plans, it is inevitable that something important will have been overlooked. It is therefore important to  build in some spare capacity to deal with the unexpected events and demands.

Design so as to increase your future choices

This is perhaps the most important point in my list because it encapsulates all the other points. I have adapted it from Heinz von Foerster’s ethical imperative which states that one should always act so as to increase the number of choices in the future.  This principle is useful as a tiebreaker between two designs that are close in all other respects. However, there is more to it than just that.  I have found it particularly useful in making  decisions that have wider, social consequences beyond technology. There is very little critical scrutiny of the benefits of these as claimed by vendors and advisories. This principle can help you see through the fog of marketing rhetoric and advisory hype.

Parting thoughts

One of the paradoxes of life is that the harder we strive for something – money, happiness or whatever – the more unattainable it seems to become. Indeed, some of the most financially successful people (Bill Gates and Warren Buffett, for example) became rich by doing what they loved. Their financial success was a happy byproduct of their engagement in their work. The economist John Kay formalized this paradoxical notion in his concept of obliquity – that certain goals are best attained via indirect means.

If you have been patient enough to read through this piece, you will have noted that some of the guidelines listed above have a hint of obliquity about them. This is no surprise; indeed it is inevitable in a design approach that values people over processes and improvisation (or even serendipity) over planning.

I usually conclude my posts with a summary of the main message. For reasons that should be obvious I will not do that here. Instead, I will end by pointing out yet another feature of emergent design that you have likely figured out already: the guidelines listed above are actually domain neutral; they can be applied to pretty much any area of endeavour. This is no surprise: wicked problems aren’t domain specific and therefore neither are the techniques to deal with them.  For example, see my interview with Neil Preston for a perspective on emergent design in organizational change and my book co-authored with Paul for ways in which it can be applied to domains as diverse as town planning and knowledge management.

…and now I really must stop for I have gone on too long.  Many thanks for your patience!


My thanks go out to Paul Culmsee for his feedback on a draft version of this post.

Written by K

October 28, 2014 at 9:04 pm

8 Responses

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  1. Thank you for this great piece of thinking & work ^_^



    October 29, 2014 at 3:01 pm

  2. […] Kailash Awati describes how to apply the principles of emergent design to enterprise IT. […]


  3. […] problem that has wicked elements.  As such then, data modelling ought to be based on the principles of emergent design. In this post I explore this idea drawing on a brilliant paper by Heinz Klein and Kalle Lyytinen […]


  4. […] You may also be wondering why I haven’t said much about the composition of the analytics team (barring the point about not needing PhD statisticians) and how it should be organized.  The reason I haven’t done so is that I believe the right composition and organizational structure will emerge from the initial projects done and feedback received from internal customers. The resulting structure will be better suited to the organization than one that is imposed upfront.  Long time readers of this blog might recognize this as a tenet of emergent design. […]


  5. […] Finally, it is worth pointing out that an interesting feature of all the above points is that they are consistent with the principles of emergent design. […]


  6. Brilliant!!! Worth saying and again until this approach is fundamentally embedded in delivering technology solution that meet the needs of the business. This approach would go a long way to leveraging success of the projects and building successful trusted teams…common sense….



    October 19, 2016 at 12:20 am

  7. […] is one of the key principles of emergent design – and more about that in a forthcoming […]


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