Adaptive Iteration: a practical path to the ‘Routines of Agility’

Agility Routines


In their Agility Factor article, Thomas Williams (Booz & Company) and Christopher G. Worley and Edward E. Lawler III (both of the Center for Effective Organizations, University of Southern California), defined ‘agility’ as:

a cultivated capability that enables an organization to respond in a timely, effective, and sustainable way when changing circumstances require it.

They identified four ‘routines of agility’ that differentiated the high performing organizations: strategizing dynamically, perceiving environmental change, testing responses, and implementing change as outlined in their Exhibit 2 below and discussed in my previous post.

Agility Routines

How do these agility routines relate to Adaptive Iteration?  I believe that Adaptive Iteration is a way of building and embedding the organizational capabilities and individual expertise necessary to support the agility routines as follows:

Strategizing dynamically

Sense of shared purpose: is incorporated as a key element of Adaptive Iteration.  A clear and shared overarching purpose is critical to guide the many Adaptive Iteration decisions and choices.  It also provides the high-level scope necessary to enable and stimulate creative and innovative improvement options.

A change-friendly identity: is encouraged and stimulated by Adaptive Iteration.  The Observation and Interpretation capabilities embedded in Adaptive Iteration encourage and develop a realistic assessment of the alignment of the organization with its purpose and operating environment.  The resultant shared recognition of any misalignment creates the collective tension and energy necessary for constructive change.

Robust strategic intent: is an outcome of applying Adaptive Iteration to the organization’s business strategy.  Adaptive improvement approaches, such as Adaptive Iteration, excel in the emergent and partially predictable context of most business strategies.  Adaptive Iteration is a fractal capability that can be used not only to change and improve a business strategy but also to stimulate and guide the necessary changes to the organization’s capabilities and resources.

Perceiving Environmental Change

Sensing: corresponds with the Observe phase of Adaptive Iteration.  Adaptive Iteration requires, encourages and develops incisive and unbiased observation of all aspects of the organisation and its environment.  It provides a framework within which observations (sensing) can trigger adaptive responses.

Communicating: is embedded in the iterative nature of Adaptive Iteration; in the emphasis on multiple perspectives during the Observe phase; in the emphasis on diversity during all phases, but especially Interpret and Design; and in the progressively expanding nature of the Experiment phase.  Open communication is embedded in Adaptive Iteration and should grow throughout the organization as a result of its use.

Interpreting: corresponds with the Interpret phase of Adaptive Iteration.  Adaptive Iteration recognizes that many situations are an ordered/random hybrid that cannot be analysed with precision or predicted with certainty.  However, interpretation of observations can identify important change-related characteristics such as early signs of failure or success, non-linear patterns, key constraints and decision points, and driving influences.

Testing Responses

Slack resources: are made available when adaptive improvement (ie Adaptive Iteration) is recognised and acknowledged as a necessary and routine organizational capability. Embedding a framework such as Adaptive Iteration creates the structures, capabilities and triggers within which ‘slack resources’ can be released and applied.  It bridges to the existing discipline and rigor required for operational excellence.

Risk management and learning: are embedded in the iterative nature and emphasis of Adaptive Iteration and in its requirement that all iterations be guided by a clear and commonly understood overarching purpose.  The explicit separation of observation, interpretation, design and experimentation reduces the potential influence of biases and predetermined expectations.  Adaptive Iteration and its enabling capabilities promote and encourage purposeful learning-based action throughout the organization.

Implementing Change

Management and organizational autonomy: is encouraged and facilitated by the structured and rigorous nature of Adaptive Iteration.  Adaptive Iteration is not an ad-hoc or occasional approach to developing and implementing change and improvement.  It does not require close management involvement and oversight.  Rather, as with the organization’s operational processes, it is a disciplined processes supported by well-defined capabilities and routines and governed by the organization’s overall purpose.

Embedded change capability: occurs when Adaptive Iteration (or any other structured approach to adaptive improvement) is adopted throughout the organization.  The clear discipline and structure of Adaptive Iteration is a natural extension of the organization’s operations.

Performance management: arises from the use of an overarching purpose or intent to drive decisions and choices throughout Adaptive Iteration.  Care must be taken to ensure that narrowly set performance objectives and measures do not limit creative or innovative options and do not drive the iterations to a premature and sub-optimal conclusion.

In summary, the work of Williams, Worley and Lawler has highlighted a correlation between organizational agility (as assessed by four agility routines) and company profitability.  Adaptive Iteration is an approach to developing organization agility and in this post I have explored how it maps to the four agility routines described by Williams, Worley and Lawler.

Photo: Derek Jensen – Wikimedia commons

‘Routines of Agility’ deliver superior profitability

Agility Chart

The Strategy+Business web site recently reported on an in-depth study that looked at the relationship between agility and long-term business performance.  The authors, Thomas Williams (Booz & Company) and Christopher G. Worley and Edward E. Lawler III (both of the Center for Effective Organizations, University of Southern California), report that:

When the measure of performance is profitability, a few large companies in every industry consistently outperform their peers over extended periods. And they maintain this performance edge even in the face of significant business change in their competitive environments. The one factor they seem to have in common is agility. They adapt to business change more quickly and reliably than their competitors; they have found a way to turn as quickly as speedboats when necessary.

The study looked at the Return on Assets (ROA) for 243 large firms in 17 industries over the 30 year period from 1979 to 2009 and identified the percentage of years for which the firm performed above the industry average.  Return on Assets was compared with survey results that evaluated each company against each of four ‘agility-related routines’: strategizing dynamically, perceiving environmental change, testing responses, and implementing change.  As with the ROA scores, each ‘agility-related routine’ result was assessed to be above or below the average of all companies in the survey.  The following chart summarizes the results.

Agility Chart

The study results indicate that above average agility performance generally correlates with above average financial performance in terms of Return on Assets.  Further, the results reinforce that agility is a systemic capability in organizations.  The companies in the study needed above average performance in at least three of the four ‘agility routines’ to achieve consistent above average financial performance.  This finding is consistent with my conviction that organizational agility and adaptive capability requires Adaptive Iteration – a purpose-driven iterative cycle of observation, interpretation, design and experimentation.  More on that in my next blog post.

Seven benefits of framing activities as experiments


BeakersImagine that you are considering turning your passion for digital photography into a part-time business, or that you have several ideas you think will make your monthly management meetings more effective.  How do you go about progressing these aspirations and ideas? You could go down the ‘analyze-plan-implement’ path and have one major shot at making them successful.  Or you could treat them as an experiment towards your objective of generating a second source of income, in the first instance, or of making your management team more effective, in the second.

Framing these activities as experiments, rather than as firm plans, has several important practical and psychological benefits:

  1. The fear of failure is removed, or at least diminished.  Experiments are not expected to always be successful.  If they fail, there is little or no risk to our reputation and credibility.  As discussed by John Caddell at 99U, in some instances it may even be appropriate to undertake an experiment in which failure is expected.
  2. As a consequence of the reduced fear of failure, you are more likely to be more creative and innovative.  You are more likely to be open to trying some things that may fail because they are new or unusual, but have the potential to provide a high return. Peter Sims over at the HBR Network Blog describes fear of failure as “the No. 1 enemy of creativity”.
  3. There is an increased focus on evaluation and learning rather than on achieving a predetermined implementation plan or being successful (at all costs). By framing the activity as an experiment, we acknowledge that we cannot fully predict the outcome and all the associated implications.  As a result we undertake the task with an inquiry-oriented and learning mindset.
  4. The learning mindset stimulates a focus on the key assumptions we are making and/or on the most important design decisions.  We ask ourselves: what areas of uncertainty are going have the most impact on the success of this idea or proposal?  We then ensure that the experiment and the resultant observations assess and evaluate those areas of uncertainty.
  5. We are likely to take a more objective perspective of the outcomes.  Because we are not protecting our reputation and because we have more than one shot at success, we are less likely to delude ourselves, and misreport to others, that the activity has been successful.  This is a particularly important benefit in those organizations where there is strong pressure to report only good news and positive outcomes and where there is a blame culture.
  6. Experiments can usually be achieved faster and with less resources than a complete implementation.  Experiments do not have to use ‘polished’ products or services, nor do they need to incorporate features unrelated to the assumptions or design decisions being tested.
  7. Design modifications are expected, not resisted.  Because experiments are designed to test and evaluate uncertainties, it is expected that the design will be changed as a result of the learning generated.  Change will be expected, if not encouraged, and the designers will more open to the perspectives and suggestions of others.  The discussion and debates will be about what to change, not whether to change.  Further, if the experiment included comprehensive data capture and interpretation, the discussion and debates will be well informed.

Approaches to adaptive change – a brief review

As mentioned in the previous blog, I have recently finished the draft of a comparative review of eight existing approaches that are similar to Adaptive Iteration.  These eight approaches are listed in the table below, together with a listing of how each approach describes the the four phases of adaptive iteration.   As can be seen from the table, there is considerable similarity between the eight approaches, but the review also identified a range of subtle but significant variations, most of which are due to the context for which each approach was first developed.  A brief summary of each of eight approaches is provided below.  A more detailed summary, together with some comparative analysis is provided in the White Paper.



Phase of Iteration



Observe/ Sense





Experiential Learning

Reflective Observation

Abstract Conceptualiz-ation

Active Experimentation

Concrete Experience


OODA Cycle



Decide (Hypothesize)

         Act           (Test)

Deming/ Shewhart


Check/Study (Observe)

     Act       (Learn)


       Do          (Test)


Adaptive Loop






Organizational Learning Cycle






Cynefin: Complex Domain





Lean Startup





Adaptive Action


So What?

Now What?


Adaptive Iteration





Table 1.  Variants of the Adaptive Iteration approach

Kolb’s Experiential Learning model emphasizes concrete experience (action) as the source of learning.  Kolb highlights that Experiential Learning requires that the learner have four kinds of abilities – concrete experience, reflective observation, abstract conceptualization, and active experimentation. The effectiveness of Experiential Learning depends on the extent to which the learner can apply and integrate all four abilities to an experience.  Kolb believes that the ability to resolve and integrate the inherent conflicts between the four “adaptive modes” (abilities) is a “hallmark of true creativity and growth”.

The OODA (Observe-Orient-Decide-Act) loop was developed by John Boyd, a fighter pilot in the US Air Force, to explain why U.S. pilots flying F-86s fared so well in air-to-air combat against enemy MiGs during the Korean War.  Boyd subsequently developed a reputation as a  military strategist and ‘thinker’.   The simplicity of the OODA Loop model led to some criticism of its suitability for more general use (outside combat situations) and this led Boyd to propose a more elaborate version of the model.  In particular, the elaborated version included more feedback paths and provided for contextual considerations, especially for the Orient step.

The PDSA/PDCA (Plan-Do-Study-Act/Plan-Do-Check-Act) Cycle was developed in an early form by Walter Shewhart and refined and enhanced by W. Edwards Deming.  It was developed primarily as a participative approach to the continuous monitoring and improvement of manufacturing (production) processes.  It is one of the core methods of the Total Quality / Continuous Improvement approaches developed and refined in Japan through the 1950s, 1960s and 1970s and subsequently adopted widely by manufacturers in western countries.  Deming continued to refine and adapt the ‘Shewhart Cycle’, as he called it, and the terminology in the version published in 1986 is more applicable to less structured and predictable processes:  ‘Observe’ replaces ‘Check/Study’, ‘Learn’ replaces ‘Act’, and ‘Test’ replaces ‘Do’.

Stephan Haeckel incorporates his Adaptive Loop into his design for Sense-and-Respond organizations.  Haeckel’s Sense-and-Respond organizations are those that deliberately pursue the ability to dynamically sense market needs and rapidly organize to fulfil them.  This is in contrast with the more conventional Predict-and-Plan organizations which, because they are not structured for rapid response to market changes, place greater emphasis on predicting the market and then using those predictions as the basis for future plans.  The Adaptive Loop is less of a loop and more a repeated application of a linear Sense-Interpret-Decide-Act process.  It is distinguished by the precision and short cycle time of that end to end process.

Nancy Dixon uses an iterative model, called the Organizational Learning Cycle, to describe the requirements for organizational learning.  Not surprisingly given Dixon’s focus on learning rather than action, her emphasis tends be on people-related structures and processes.  The emphasis is on creating shared and contextually relevant meaning through widespread collection, sharing and interpretation of information.  As with Haeckel, there is limited emphasis on the creation of feedback based learning.

In their Cynefin sensemaking framework, David Snowden and Mary Boone propose a Probe-Sense-Respond approach as one of a suite of leadership actions in the Complex domain.  The Cynefin Complex domain corresponds with the domain of interest in this review, that is, domains that are partially structured, emergent and partially predictable.  Snowden emphasizes that the Probe phase consists primarily of safe-to-fail experiments that both test a hypothesis about potential improvements and generate additional learning about the nature of the Complex System.  He also allows the possibility of multiple parallel experiments to increase the scope and depth of the learning.  Snowden also advocates the use oblique or indirect approaches to identifying improvements and encourages the introduction of diverse perspectives by incorporating related-but-different expertise.

According to Wikipedia: “Lean Startup” is an approach for launching businesses and products, that relies on validated learning, scientific experimentation, and iterative product releases to shorten product development cycles, measure progress, and gain valuable customer feedback.  The Lean Startup approach began as a methodology for (software based) entrepreneurial startup activities.  However, it is increasingly being applied within large companies to address situations with that have significant levels of uncertainty – a defining characteristic of the Lean Startup approach.

Glenda Eoyang and Royce Holladay of the Human Systems Design Institute use a three phase iterative ‘Adaptive Action’ approach to address uncertain and unpredictable situations.  The approach draws heavily on the concepts and perspectives of Complexity Science, particularly those relating to the self-organizing behaviour of Complex Adaptive Systems.  Central to the Adaptive Action approach is the Containers, Differences and Exchanges (CDE) construct.  The language and case examples in Adaptive Action: Leveraging Uncertainty in Your Organization focus primarily on organizational development, organizational behaviour and leadership.  No doubt, the general approach has wider application, but it is unclear whether the current formulation and language of Adaptive Action will stimulate broad application outside the organizational/leadership context.  That remains to be seen.

We propose Adaptive Iteration as a framework to develop the core capabilities necessary to develop agility and adaptive capacity in organizations.  Adaptive Iteration emphasizes the co-evolution of learning and action and the importance of a clear and shared purpose to guide the adaptive process.  It draws on the lessons and approaches of those who deal with unpredictability and uncertainty every day – entrepreneurs, designers, researchers and developers.

Principles for Adaptive Iteration – crystalized by a comparative review of similar approaches

I have just completed a Discussion Draft of comparative review of eight existing approaches that are similar to Adaptive Iteration.  I have published the comparative review as a White Paper here.  One of the benefits the review is that it has helped clarify and crystalize the essence of Adaptive Iteration in the form of a set of principles – still in draft at this stage.  I listed the principles towards the end of the White Paper and repeat them here for you to review and comment.

Principles for Adaptive Iteration

  1. Adaptive Iteration applies to all non-random situations where behaviour and performance cannot be predicted with confidence in advance.  In the main, this applies to situations that are partially structured and emergent.  Because of dramatically increased connectivity generated by developments such as globalization and by technologies such as the internet, situations that require Adaptive Iteration are increasingly prevalent and of increasing significance.

  3. Adaptive Iteration involves the co-evolution of learning and action in an iterative four phase approach:
    • Clear and unbiased observation of the situation, including contextual influences
    • Interpretation of the observed data to improve understanding of the trajectory of and the influences on the situation
    • Incorporation of the interpretation insights into a hypothesis for an improved design
    • Conduct of one or more experiments to assess the impact of the proposed improved design.
  4. Adaptive Iteration is underpinned by a clear and shared understanding of the fundamental drivers for and constraints on the adaptive decisions.  This provides coherence and alignment for all decisions and provides lower level scope for the adaptive process to explore novel and unconventional options.  It also provides a clear driving force for adaptation and against the status quo.

  6. Adaptive Iteration may be a nested approach in which a lower level Adaptive Iteration cycle is used as the experimental phase for a proposal for a higher level design change.  Because of its multi-level applicability, Adaptive Iteration should be a core capability throughout any organization.

  8. Rapid iterations of Adaptive Iteration are important during the early stages of an adaptive transition.  These iterations promote critical early learning and integration across the four phases.  They also help shake-out any lack of shared clarity about the fundamental drivers and constraints.

  10. Adaptive Iteration co-exists with analytical action.  Where the impact of a design variable can be predicted efficiently and with confidence, Adaptive Iteration is not required and the design changes should be subject to ‘analytical action’.

How many ball passes between the white team?

Before you read any more of this blog post, please watch the video below (even if you think you have seen it before).

(Video courtesy of

As the developers of this experiment, Christopher Chabris and Daniel Simons, emphasize in their book, The Invisible Gorilla: How our intuitions deceive us, the key message is that we are strongly biased to only see what we pay attention to and what we expect to see.  They call this inattentional blindness (blindness to what we do not pay attention to).

A related experiment demonstrated that the percentage of people who fail to see a prominent, but unexpected, object directly in their field of view increases to 90% when the complexity of the ‘attention’ task increases.  In other words, the likelihood of inattentional blindness increases if we need to concentrate hard to pay attention.

Now let us translate these findings to the Observe phase of Adaptive Iteration.  Adaptive Iteration applies primarily to situations that are unpredictable, uncertain and emergent – precisely the situations in which the unexpected may occur.  Add to this the characteristic that these situations are usually complex – unclear cause and effect relationships, many elements, interactions and decision points, dynamic context, etc – and require much effort (attention) to attempt to determine what is happening.  So, in these situations, we have a relatively high likelihood of something unexpected occurring at the same time as we need to concentrate hard to attempt to understand what is happening.  We have the perfect conditions for inattentional blindness.

What can we do to reduce the potential for inattentional blindness?  I think there are several things:

  • Deliberately observe the situation from multiple perspectives. I have summarized this approach, including a range of observational perspectives, in a previous blog post.
  • Separate, as much as possible, understanding the situation from observing the situation.  We can reduce the distraction of attempting to analyze and understand the situation by separating it from the effort to observe the situation.  We focus on observing what is happening rather than attempting to explain what is happening.  In particular, we suspend our theories and preconceptions so that we do not focus on those observations that confirm them.  (This point summarizes one of the key reasons why we have explicitly separated Observe and Interpret in the Adaptive Iteration framework.)
  • Capture a data rich record of the situation.  As with the basketball situation, if we capture a data rich record we can replay it multiple times so that we can observe things that we may have missed.  Replaying the basketball video provides very strong proof that a gorilla did walk through the players even we are convinced from our initial observations that it did not.   One word of caution with this approach – we need to ensure that we do not focus our data capture only on those things that we think beforehand are relevant and important.  This will formalize our observational bias.  The record capture needs to be as broad, detailed and unbiased as possible.
  • Don’t attempt to understand the situation – just focus on what is and what is not working.  Many unpredictable, uncertain and emergent situations are so complex that it is impossible to understand and analyze them in detail.  Often, we should just accept this and rather than attempting to understand and explain the dynamics of the situation, focus on what is obviously working and and what is obviously not.  During the subsequent Interpret phase of Adaptive Iteration, we can then identify what sort of things we can do (design changes) to promote that things that are working and to suppress those that are not.
  • Use multiple observers, some of whom are not specific experts.  The researchers experience with the basketball video is that approximately 50% of observers see the gorilla walk through the players.  Increasing the number of observers will increases the likelihood that at least one observer will see the unusual and unexpected.  However, it is important that there is diversity among the observer.  Especially, it should contain some people who are not experts in the situation (see earlier blog post) and there should little or no opportunity for the group to develop ‘group think’ beforehand.
  • Practice mindfulness. Sam McNerney in an article in TheCreativityPost highlights research that suggests that  mindfulness, defined as “paying attention to one’s current experience in a nonevaluative way,” may provide an effective means of observing a situation or circumstance without seeking to confirm our intuition and expectations.  In other words, by being consciously aware of our own perceptions and thoughts we can learn to recognize and compensate for the filters they place over our observation and attention.
  • Iterate rapidly.  The iterative nature of Adaptive Iteration means that we do not have to be ‘right first time’.   If the unusual or unexpected event is not just a result of natural variation in the situation (i.e., ‘noise’), it is likely to occur again in a future iteration, especially if the iterations are rapid and involve relatively small design changes.  Although this is not the main benefit of rapid iteration, it is a significant one.

Dancing Guy – when would you join in?

The ‘Dancing Guy’ video below provides a clear and striking example of non-linear behaviour.  While you are watching the video put yourself in the shoes of one of the observers.  What would be your reactions?  What sort of things would influence them?  Would they change over time?  What would it take to trigger you to join in?

Are there parallels here with change in organizations and in businesses?  Perhaps the adoption of organizational change sometimes happens this way.  Or possibly the video mirrors the progress of the introduction of a new product, or the introduction of an existing product into a new market.

For me, the video highlights a range of key observations about creating change in unpredictable and uncertain contexts:

  • For the first third of the video all we see is one man and then a second dancing enthusiastically by themselves.  But is this all that is happening?  It is all we can see externally, but a lot is also happening in the brains of the observers.  I suggest that many of them are starting to, consciously or subconsciously, think about what it would be like to join in.  They are weighing the fun and exhilaration of dancing against the risk of standing out and potentially appearing silly.  In a sense, the observers are progressively being ‘primed’ to join in.  So, in our change initiatives, we should not just rely on our external observations to monitor progress.  There is likely to be much happening below the surface.
  • What would have happened if the ‘dancing guy’ had stopped just before the first person joined him?  Would that second person have still got up and danced, this time by himself?  Would a cascade still have happened?  I think not?  This suggests to me that in complex change perseverance is critical.  If we are convinced that the change is important and valuable, we need to continue to, metaphorically, ‘dance and wave our arms’ to ensure that we are noticed and our message is heard.
  • If creating a dancing crowd was his objective, considerable effort was expended by the ‘dancing man’ before he saw any return for it.  When he did start to get a return for his effort, he achieved a very large return for little additional effort.   A traditional benefit-cost review at any time during the first minute would have suggested that he ‘cut his losses’ and stop dancing.
  • Would the ‘dancing man’ have attracted the same attention and had the same impact if he was not dancing so extravagantly and enthusiastically and so obviously enjoying himself?  I think not.  Sometimes, if we want to create change in uncertain and unpredictable situations, we need to be extravagant and enthusiastic to rise above the noise and the fear.  This creates risks, because the crowd might not join us, but they are much more likely not to join us if we don’t take that risk.
  • Would there have been the same impact if a small group of people, say six, had started dancing together in the same way?  I am not sure.  From one perspective, I think group may have been perceived as a clique and it would have been more difficult for the first additional person to join in.  On the other hand, the risk of standing out would have been reduced for the first additional person.  I think there is a lesson here when we start a change initiative with a pilot, especially if the pilot is in a distinct and very coherent area or group.

Perhaps the ‘Dancing Guy’ video triggers other thoughts and perspectives for you.  If so, please respond with a comment.

The ‘snowball effect’ – a powerful force in emergent situations


SnowballHave you ever wondered what is happening when a social media video goes viral?  Viral videos are the result of self-reinforcing feedback (sometimes called the ‘snowball effect’ or ‘virtuous/vicious cycles’).  If a video is sufficiently engaging, those who watch it will share it with their friends.  There is a good chance the friends will also enjoy it and share it with their network of friends, and so on.  Similarly, the more hits or ‘likes’ a video gets, the more likely it is to move up popularity based ranking lists which will, in turn, stimulate more hits and ‘likes’ that will drive it further up the ranking lists.  So we see at least two positive self-reinforcing feedback loops driving the success of viral videos.  One based on awareness generated through social media and the other based on attention generated by rising up ranking lists.  Because of the multiplier effect each time around the self-reinforcing loop, rapid non-linear growth occurs.

Viral video causal loopMy experience tells me the snowball effect is one of the most significant features of complex and emergent situations.  It is one the key reasons why small initial changes (weak signals) can sometimes have unexpectedly large consequences or impacts.  These, in turn, contribute to the unpredictability of such situations.

The snowball effect (self-reinforcing feedback) is at the heart of the development of most deep personal expertise.  Several feedback loops are happening in parallel:

  • I develop initial expertise.  This proves valuable for me in work or social settings, so I am motivated to develop the expertise further and to apply it with more confidence and more deeply.  This, in turn, makes the expertise even more valuable to me, and so on.
  • My expertise is noticed by others.  This makes feel good and so motivates me to develop the further and apply it more widely which, in turn, increases the number of people that notice my expertise, and so on.
  • The more I apply my expertise, the more I understand the nuances of the expertise and how it applies in various circumstances and this, in turn, creates more aspects that I can learn and develop (I like to think if it in terms of creating more ‘edges’ to my expertise that I can work on) which creates more and deeper opportunities to apply the expertise, and so on.
  • The more I apply and develop the expertise, the more likely it is that it will come to the notice of people who can help me develop further, either through coaching or advice, or the provision of more challenging opportunities to apply it.  This creates further opportunities to develop and apply the expertise, and so on.
  • The more I apply my expertise, the larger the library of related patterns and associations I develop in my brain and this, in turn, increases the speed and confidence with which I can apply my expertise.  This, in turn, grows the opportunities to apply my expertise which builds an even larger and more nuanced library of patterns, and so on.

As an aside, I think we see the snowball effect evident in many students that excel in a particular area.  They are not learning to get a good grade, they are learning because one or more of the above self-reinforcing feedback loops have been triggered.  The excellent grade is just a by-product or confirmation.

The growth and development of expertise via the snowball effect is not controlled, managed or predictable.  Although the broad progression path for the type of expertise may be able to be anticipated, the specific opportunities for development are not prescribed, planned and programmed in advance.  They are determined by the unfolding (emergent) situations triggered by one or more of the above self-reinforcing feedback loops.

The proceeding discussion has focused on the development of individual expertise, but I believe that it also applies to the development of capabilities at the group or organisational level.  It also applies to the development of many successful product or business models.  Initial success creates increased learning, exposure and reputation and that drives increased funding (sales and investors) and product improvements that, in turn, creates further success, and so on.

I believe the snowball effect (self-reinforcing feedback) is at the heart of successfully navigating many emergent and complex situations.  I will explore this further in future blog posts.

Experiments change as we progress through Adaptive Iteration

Observe>Interpret>Design>EXPERIMENT>One of the key characteristics of situations that are uncertain, unpredictable and emergent is that we cannot rely on knowledge, expertise and experience to deduce what is happening and how to change the situation to achieve our objectives. The interactions are usually too numerous, complex and/or too subject to human choice or unforeseen events to be able to determine in advance the nature and sequence of all the cause and effect relationships.  In these situations we need to rely on observing what is happening to determine ‘what works’ and to discern the broad-based influences – we need to let “the situation talk back”.

Observing natural behaviour will give us some insights about the situation and how we may be able to influence it to achieve our objectives (create organizational change, launch a successful new product, improve the performance of a team, etc), but often we also need to perturb or probe the situation to test our ideas and insights and to generate further insights.  This is the ‘experiment’ phase of Adaptive Iteration.

An interesting way to look at experiments is by analogy with natural selection in the process of evolution.  By conducting experiments and then retaining what works in the next round of design improvements, we are imitating the evolutionary process in which the naturally occurring variants that survive selection pressures become the basis for further variants, and so on.  In Adaptive Iteration we let the situation give us feedback about what is working and was isn’t, and we then incorporate this learning into the next design variant.  Using this analogy, it often may be appropriate to conduct a number of experiments in parallel to maximize the feedback we get.  Note that these experiments are not like scientific experiments that attempt to determine and quantify governing relationships.  In scientific experiments we need to be careful that we don’t change more than one variable at a time.  In Adaptive Iteration this is much less of an issue, we are looking for the best design among a landscape of options, not the formula for the one right design.

What sort of experiments can we do?  Initial small, low risk experiments include:

  • Thought experiments (see earlier blog post)
  • Sketches
  • Story boards
  • Prototypes (mock-up style)
  • Probes or ‘sighter’ trials

The objective of these initial experiments is to quickly canvas a broad range of design options and to learn more about the design context.  The focus is on establishing and testing the broad design concept and structure.  The objective is to develop one or more outline designs that are likely to satisfy the design objectives, boundaries and constraints.  This also helps test early in the design process whether we need to question and review those objectives, boundaries and constraints.  It is much more efficient to learn at this early stage that the stated design objective unnecessarily constrains the design.

A key aspect of these early low risk experiments is to make the design ideas as tangible as possible – to give a clear voice to the creative and conceptual thoughts of those involved in the design process.  Forcing the early design ideas to become tangible has two key benefits.  First, as noted above, it tests the whether the high level design criteria are appropriate and whether those criteria are clearly and commonly understood by all those involved.  Second, it creates a common and tangible focus for evaluation and improvement of the design.  It is much easier to identify and explain possible improvements if you are working with a representation of the design that is tangible and contains a practical level of detail than if you are working with a broad description.  (Although thought experiments are not tangible, they should include sufficient descriptive detail to enable those involved to become immersed in the imagined situation.  They must also be structured as a forward looking exploratory ‘experiment’, not as a rationalization or explanation of a desired outcome.)

One the characteristics, and risks, of these early adaptive iterations is that all four phases – ‘design’, ‘experiment’, ‘observe’ and ‘interpret’ – are often tightly coupled and performed by the one individual or group.  This enables rapid early iterations and the early testing of a wide range of design options.  However, it also runs the risk of ‘group think’ leading to premature narrowing of the design options.  It is important that these early experiments are observed and interpreted with open minds and from multiple perspectives (see earlier blog post).

As Adaptive Iteration moves into detailed design, the experiments use more detailed and realistic representations of the design.  The design is increasingly tested in the context in which it will used.  The experimental options include:

  • Functional prototypes
  • Low risk trials
  • Simulations
  • Pilot implementation

These increasingly realistic experiments need to be structured to evaluate how the design performs in context.  They need to reveal any unintended consequences, any unanticipated constraints, any emerging systemic behaviour (especially non-linear behaviour) and any lessons for wider implementation.  At this stage it is also important to be open to any surprises – positive or negative.  In summary, these experiments need to be sufficiently realistic and broad to assess what works and what does not.  They also provide another opportunity to test the foundational design choices (objective, boundaries/ constraints, architecture and metaphor).  The results of these more realistic experiments may indicate that some or all of the foundational design choices need to be fine tuned, or even substantially revised.

The final form of ‘experiment’ is the real implementation of the design.  Although, it is not structured as an experiment, it is, by definition, the most realistic opportunity to observe what works and what does not.  This is especially important for the situations of interest to us, those that are uncertain, unpredictable and emergent.  In these situations, the introduction of the design is likely to have a ripple effect on the context (resistance to organizational change might increase – or decrease, competitors may bring forward the timing of a new product, reinforcing complementary initiatives may be stimulated in the industry, etc) .  Consequently, it is important to continue the Adaptive Iteration approach throughout the life of the design, but especially during the early stages of introduction.  So the final experimental option is:

  • Evolving implementation.

Composing an email – an example of foundational design choices

Observe>Interpret>DESIGN>Experiment>How does the concept of foundation design choices that I introduced in the previous blog post apply to a real design situation?  To explore this let us look at it in a context familiar to most of us – the challenge of designing an important email.  (If you do not think that writing an email is design, please keep reading with an open mind.)  As a manager for many years, a significant percentage of my time (probably more than 20%) involved composing, reading and responding to emails.  I believe the ability to compose (design) a clear, succinct and relevant email is a very underrated personal skill in most organizations.

In the previous post I proposed four foundational design choices:

  • Objective
  • Boundaries/constraints
  • Architecture/game plan
  • Metaphor.

Let us see how each of these could apply to composing an email.

Objective:  Each email will (should) have a specific principal objective.  It could be to persuade, to propose, to inform, to update, to report, to call to action, to instruct, to admonish, to praise, to entertain, to enlist, etc.  The overall objective must be determined before we start composing the email so that the entire email supports that intent and so that the intent is absolutely clear to the reader.  (I will leave aside here the question of whether an email is the best way to achieve this objective.  That is a higher level design choice that I assume we have already taken.)

Boundaries/constraints:  Any email is subject to a number of practical and contextual constraints.  They include the time available to write the email, the likely time and attention that reader will give to the email (both these constraints will influence the length of email), the confidentially of any information in the email, the level of familiarity of the readers with relevant technical terms and acronyms, the existing level of familiarity of the reader with the subject matter and context, scope of subject matter, etc.  Some of the these boundaries/constraints, such as the length and scope of the email, are within your control as a writer. Others, such as level of familiarity with technical terms and acronyms, are characteristics of the readers.  However, the extent to which the characteristics of the readers is taken into account is still a choice of the writer (designer), that is, it is still a foundational design choice.

Architecture/game plan:  The style of the email essentially defines its structure (architecture).  Possible styles include formal report style, journalistic style, ‘call to action’, conversational, and narrative.  Exactly how these styles are implemented will often depend on context.  For example, how you ‘call to action’ will depend on factors such as whether the action is mandatory or discretionary, your level of authority or influence, the complexity of the action, and the level of reader self-interest in undertaking the action.

Metaphor:  If the email is particularly complex and/or important, it may be valuable to identify a metaphor for the email before composing the detail.  If the email is instructional, a road map metaphor may be appropriate.  If the purpose of the email is to admonish or correct, picturing yourself as a coach or parent may help set a firm but constructive tone (although this may be better done face-to-face rather than use email).  If you are calling to action to address a very specific challenge, then picturing the email as a arrow may help create the required focus and emotion.

Clearly, not all emails require the effort to develop foundational design choices.  Many of our emails are routine responses to requests for information or routine elements of the business processes of our organizations.  The design for these emails are essentially determined by the business context and/or routine (best?) practice in our organization.  However, foundational design choices are much more relevant and significant when the email is part of situation that is uncertain, unpredictable and emergent. This is often the case when we are seeking to use the email to persuade and influence in the context of organizational or business change.

Design involves choices – not all are equal

Observe>Interpret>DESIGN>Experiment>Whenever we create something to achieve a purpose or objective, we are designing. In the context of Adaptive Iteration we are interested in designs that need to achieve their objectives in situations that are uncertain, unpredictable and emergent. In these situations the scope and complexity of the interactions are such that we cannot predict all the cause and effect relationships in advance. Not only are they complex, but they are also changing dynamically and are likely to change further in response to the introduction of our design.

The types of situations that require this type of emergent design include business plans and strategies, organizational change initiatives, new product development, new market entry, career development and organizational development. In other words, almost any situation that is forward looking and involves interactions of people or groups of people whose decisions will influence the outcome.

Designing involves choices. There is no one right design. The design choices will determine the extent to which the design meet its objectives. If the design context is uncertain, unpredictable and emergent, the best design choices are not at all clear. For example, we cannot be certain how customers or competitors will react to a new set of product features. Can we be confident that our new social media strategy will attract sufficient attention from our target markets? It may not be clear what coaching and training to give to our middle-managers to ensure that they can make the transition to our new project-based organizational structure?

How do we make these choices? If we use the Adaptive Iteration approach we do not have to be ‘right first time’. With Adaptive Iteration, we form hypotheses about the best design choices and then conduct low risk experiments to assess how those choices work in practice. We use the resultant observations and interpretations to refine the design choices (hypotheses) that we again test experimentally, and so on. By starting small with low risk and iterating rapidly, we home in on the best design to meet our objectives in the particular context.

However, in Adaptive Iteration, our initial design choices (hypotheses) are not random. Foundational design choices set the relatively stable core of the design on which choices about the design detail are made and adaptively iterated.  The foundational design choices are not unchangeable during Adaptive Iteration but will change much less frequently than choices about the design details.  A significant shift in any of the foundational design choices should stimulate a major rethink of the overall design. The foundational choices, in general order of significance are:

Objective:  The objective describes the intent of the design, and of the associated Adaptive Iteration, and forms the key anchor for all other design choices.  All other design choices must enable the design to achieve its objective.  The clearer the design objective, the more likely that Adaptive Iteration will proceed efficiently and effectively.  It is important to select an objective that does not implicitly constrain the design options.  In my blog post that summarized the start up stories of HP, Sony and Microsoft we saw that the objective of each of the founders was to create a profitable business based on their particular expertise and interests, rather than create a business in any particular product/market segment.  The founders of each company adaptively iterated the product/market segment until they found one that met their design objective.

Boundaries/Constraints:  The boundaries and constraints can apply to any aspect of the design. They set the scope of design variables (choices) available to the designers.  They also set any limits to or targets for the performance of the design, and they set the context within which the design sits.  In some cases, tight design constraints can stimulate focus and can help challenge the status quo. For example, a design constraint that requires that the manufactured cost of a product design be 20% less than its predecessor is likely stimulate new a search for new perspectives and step-change ideas.  In other cases, widening the design boundaries may create the scope to develop more systemic and more fundamental design improvements.  Roger Martin provides a good example of this in his book Opposable Mind where he describes how IDEO recognized that its design brief from Amtrak (redesign their railcar) was inherently limiting and convinced Amtrak that, to achieve its design objectives, it needed to look at the end-to-end train ride experience.

Architecture/Game plan:  The architecture or framework of a design will usually have a significant impact on our ability to adaptively iterate the design.  Key decisions include how much operating flexibility and how much modularity to build into the design.  For example, an organizational design that is based on a strong and commonly held set of operating values and principles will empower employees to use their discretion to meet customer needs as long as what they do is consistent with those values and principles.  On the other hand, employees in an organization that emphasizes rules and procedures will be constrained to only the situations and responses anticipated by those rules and procedures.  The nature and extent of modularity will influence the ability to adaptively iterate the design.  Coarse-grained modularity will constrain design changes to large steps (changing one or more of the coarse grain modules), whereas fine-grained modularity provides much more flexibility but may lead to costly customization and integration of the design after each adaptive iteration. Ideally we want to align coarse-grained modules with any relative stable aspects of the context and use finer-grained modules for those aspects of the context that are uncertain, unpredictable and emergent.

Metaphor:  Use of a design metaphor can help unify a design by stimulating coherence for the myriad of detailed design choices.  It can help create a distinct personality for the design and stimulate a positive emotional response from the user.  A metaphor can also help identify the mix of design features that will create both functional and emotional coherence for the design.  It can help overcome the tendency to load a design up with all manner of features and capabilities “just because we can”.  For example, a metaphor may assist the redesign of an under performing group or small organization.  Depending on the situation and objectives, the group could use the metaphor of a sporting team, an orchestra, a jazz ensemble, an operating theatre, improvisation theatre, etc.

13 perspectives to help see things as they really are, not as we expect them to be


Observe-Interpret-Design-ExperimentWe are conditioned to see what we expect or are primed to see, not what is really there.  This is fast and efficient if our world is stable and predictable where the past is a good predictor of the future.  But what if it is unstable and unpredictable?  What if our expectations and experience are only partially useful?  The problem is that our expectations and experience are often deep within our subconscious mind.  It is difficult to switch them off even if we consciously realize they may be inadequate in a given situation.

How then do we see things as they really are, not as our subconscious would like or expects them to be?  One of the most effective things we can do is to develop the ability to step through a variety of perspectives when examining a situation.  In doing so we use our conscious brain to counter or supplement our subconscious brain.

FlorenceIn general, we need to adopt perspectives that allow us to be more empirical.  That is, we need to focus on more extensive observation and data gathering before drawing conclusions.  In most cases, we also need to recognise that the conclusions are only hypotheses that require further testing and validation.  We need to be open to revising, or even discarding, a hypothesis as we make more observations or collect further data.

I have categorised the various perspectives we can take under four broad categories – spatial, structural, psychological and effectual.

Spatial perspectives

Get close to the action to see actual behaviour and events rather than make assumptions based on past, prescribed or ‘normal’ behaviour.  This approach underpins the Genchi Genbutsu (“go and see”) principle of the Toyota Production System.

Move further away from the action so that you can see behaviour and events in a wider context (draw wider system boundaries) and therefore consider more structural influences and factors.  Moving further away is also likely to increase detachment and objectivity and therefore reduce any biases introduced by an emotional engagement to the situation.  The movement need only be psychological (for example, by imagining you are in another city), not necessarily physical.

Divide the situation into a comprehensive set of segments and examine each of them separately and then the relationships between them.  This approach reduces the risk that your focus quickly narrows on those aspects that you expect or assume to be the most salient.  It ensures that all aspects get at least some focused attention.  The segmentation basis could be physical, organisational or temporal (time based).

Examine the behaviour at the boundaries of the system or within the system.  New or changing influences and behaviours will often be first evident at the boundaries.  This is either because new external influences first interact with system at the boundaries, or because the more diverse interactions that occur at the boundaries generate new ideas and options.

Structural perspective

Consider the network aspects of the system or situation.  Are there any groupings or networks emerging?  If so, what are the ‘attractors’ for the groupings, or what is driving the linkages for the network?  Is there potential for these to be self-reinforcing, or are there likely to be fundamental constraints to their ongoing development?  On the other hand, what are the established networks and groupings?  Are they structured to facilitate or inhibit positive change?

Identify the constraints in the situation.  The constraints may relate to physical boundaries, information, expertise, resources, awareness and expectations.  If behaviour consistently occurs at or near a constraint, the behaviour is unlikely to change without first relaxing or modifying the constraint.

Look for any potentially significant decision points.   Major decisions create ‘forks in the road’ that influence the future development of the system or situation.  At the personal level, they include choice of a partner, a profession/career, a place to live, etc.  At the organisational level, these decision points are numerous and diverse and may not seem particularly significant initially.  However, they may stimulate or facilitate a self-reinforcing chain of events that eventually have game-changing impact.

Identify any emerging patterns of cause and effect.  Even though the situation appears essentially unpredictable, repeatable cause and effect patterns may be emerging.  Customers in a given segment may have begun to respond more predictably to on-line promotions.  Middle managers from one segment of the organisation may be exhibit similar resistance to a change initiative and offering suggestions for improvement that have a common underlying theme.  These emerging patterns provide early indications of what is working and what is not.  They provide potential guideposts for future action.

Behavioural/psychological perspectives

Look at the situation through the eyes of the key participants.  In other words, take a perspective of empathy.  Aspects of the situation that may be clear and positive to you may be unclear, uncertain and therefore threatening to some of the participants.  Benefits of a change initiative that you think are valuable may appear to be of marginal value when seen through the eyes of a stakeholder group.

Identify the dominant motivations and influences in the situation.  Look for the ‘why’ as well as the ‘what’?  Where do the energy and driving forces for action come from?  Are they derived from proactive aspirations or reactive defensiveness?  Is the source local or broadly based?  Is it likely to be enduring or short term?  In emergent and unpredictable situations, the driving influence is a potential source of consistency and coherence.  Although it will not enable future decisions and outcomes to be predicted with certainty, it will point to how they are likely to be biased.

Is the situation characterised by a few dominant emotions?  Emotions have a fundamental influence on behaviour and decision making.  We may be able to bring some clarity to what appears to unpredictable and illogical behaviour when we understand their emotional underpinning.  Looking at a situation through a lens of emotions may provide insights that we may find it difficult to discover otherwise.

Effectual (effects and results) perspectives

Notice what seems to be working.  Where are successes happening?  What activities and behaviour are being reinforced?  Where are people making progress in spite of their constraints and context?  What have they done to overcome the constraints?  Is this repeatable and scalable? In unpredictable and emergent situations we cannot rely primarily on what has worked in the past or has worked somewhere else.  We need to discover new principles for success.

Be alert to the surprising and the unusual.  Often, much ‘information’ is found in the unexpected events, behaviours, relationships and achievements.  These may be weak signals of the emergent direction of the situation, or they may be just random noise.  If it is random noise, and the surprise is a positive one, can the random conditions be identified and repeated consistently?  The earlier the small surprises can be identified, the more likely it is that they can be purposely enhanced if they are positive or suppressed if they are negative.

What do viral videos and ‘10 year overnight successes’ have in common? – they are both non-linear


Observe-Interpret-Design-ExperimentViral videos and ’10 year overnight successes’ are both examples of non-linear behaviour – future performance is not a simple linear extrapolation of past performance.  In the case of both viral videos and ’10 year overnight successes’ performance suddenly grows rapidly after a (sometimes lengthy) period of relatively low success.  Both the timing and extent of this rapid change is difficult to predict and plan for.  Non-linear behaviour is not always positive.  In modern cultures, a rapid fall from popularity when something unexpectedly loses its trendiness is a negative example.  A rapid loss of confidence in a business leader or politician is another.

Non-linearWhy is non-linear behaviour so interesting and important?  Because it is helps explain why certain situations change so rapidly and unpredictably.  What might appear to be just small inconsequential changes or random ‘noise’ may in fact be the early weak signs of success (or failure).  We may think that our new initiative is not working and prematurely abandon or significantly change it.  Alternatively, we may see quick success and commit further resources to an initiative or change effort only to find that it expectedly slows down or stagnates.  A deeper awareness of the nature and types of non-linear behaviour will sensitise us to the potential for these types of situations and help us look for indicators of non-linear patterns and mechanisms.  It will also help us set expectations accordingly.

Sources of non-linear behaviour relevant to organisational and business situations include:

  • Self-reinforcing feedback (snowball effect)
  • Preferential attachment
  • Percolation/connectivity
  • Threshold response
  • Synergistic effects
  • Pressure build-up – Catastrophic failure
  • Decision points
  • Diversity

A whitepaper that summarises each of these sources can be found here.

Even though the onset of non-linear behaviour is often difficult to predict, we can recognize when the pre-conditions for non-linear behaviour are starting to emerge.  Just as geologists know that the preconditions for earthquakes exist in various regions of the world, we can develop expertise in recognizing the development of preconditions for non-linear behaviour in our organization, economy, industry, etc.  I suggest that the following are some indicators of such preconditions:

  • Periods of (disruptive) transition
  • Emergence of imitation based perspectives and decision making
  • Emergence of numerous separate but related events, technologies, perspectives:
  • Emergence of new enabling technologies
  • Broad based constrained pressure for or resistance to change
  • Complex initiatives that require broad based integration and coherence for success

In many cases, non-linear behaviour is an emergent property of the situation, not something we can directly engineer and control.  In situations such as synergy and diversity, it is possible to take a leading role.  In most others, we need to read the dynamics of the situation and find ways to influence, adapt and take advantage of the emerging situation.  The following list of potential responses focuses primarily on responding to emergent non-linear behaviour.

  • Experiment/probe
  • Promote desirable and suppress undesirable trends by influencing the constraints and attractors
  • Practise ‘planful opportunism’
  • Ride the wave or get out of the way
  • Observe, observe, observe

The whitepaper mentioned above explores these preconditions and potential responses.  I will also discuss specific examples of non-linear behaviour in more detail in future blog posts.

Why experts are partially blind – and 5 ways to ‘restore sight’

Partially blind

Partially blindHave you ever sat in a meeting and heard two attendees discuss the same topic on entirely parallel tracks?  Or perhaps you have been in meetings where an attendee keeps bringing the conversation back to a clearly inappropriate or irrelevant perspective.  Both situations are confusing and embarrassing for the other attendees.  Why do intelligent and competent people have such conversations?  Why do they miss the point so badly?  Why do they continue, even when the disconnect is obvious to others?

The old saying, “when all you have is a hammer, everything looks like a nail”, gives a clue to one of the key causes of the ‘parallel conversation’ problem.  Our professional training and experience colours the perspective we bring to any related context.  This is particularly so for deep and successful experts.  Their narrowness of perspective is reinforced not only by confidence gained from success but also by the need to defend and preserve their reputation and ego.

Such expertise based narrowness of perspective is a significant problem during the Observe stage of Adaptive Iteration.   Adaptive Iteration is applicable when the situation is unpredictable and emergent, precisely the situations where preconceptions and narrow perspectives are most risky.  During the Observe stage we need to be open-minded, empathetic and sensitive to both detail and trends.

Does this mean that experts should not be involved in the Observe stage of Adaptive Iteration?  Not necessarily.  However, it does mean that we need to think explicitly about how we organise for and go about observing our experiments, not only to reduce the potential for expertise bias, but also to reduce the risk of other forms of unconscious biases (more on these in later blog entries).

The types of things we can do to reduce the potential for observation biases include:

  • T-shaped people:  Include people on the team who have not only deep expertise but also broad interests and knowledge and the ability to collaborate with people with other types of expertise.  These are becoming known as ‘T-shaped people.
  • Multiple perspectives:  Explicitly observe the situation (experiment) from multiple points of view.  The objective here is to quieten our unconscious biases by adopting one or more conscious biases.  I will explore a variety of possible perspectives in a future blog post.
  • Diverse team:  Include people on the team with a range of expertise and from a range of backgrounds.  Diversity reduces the potential for observational bias only if the team dynamics enables the diverse observations to be surfaced, discussed and synthesized.  The ability to do this depends on a mixture of structure, process and personality.
  • Prepared mind:  Train (prepare) your team to be a better observers.  As Louis Pasteur is reported to have said, “in the field of observation, chance favours the prepared mind”.  Techniques for preparing the mind include learning how to suspend judgement, to implicitly adopt multiple perspectives, to appreciate the impact and role of context, and to see the underlying systems dynamics.  Interestingly, research is starting to suggest that our ability to have empathy (critical when observing many human interactions) can be increased by reading emotionally engaging fiction.
  • Focus on data:  Where possible capture rich data to lead, inform or validate human observations.  The complex, unpredictable and emergent nature of situations where Adaptive Iteration is applicable means that is usually not possible to rely solely on data for our observations.  Nevertheless, some situations are amenable to supplementing observations with techniques such as video recordings or with the analysis of (often large) data sets to reveal emerging patterns, trends and relationships.

What is a design hypothesis and when is it required?


Observe-Interpret-Design-ExperimentThe concept of a design hypothesis is central to Adaptive Iteration.  But what is it and when is one required?

All designs have some degree of freedom and therefore involve choice by the designer.  How does the designer make that choice?  It depends on the stability and predictability of the context in which the design is to be used and on the extent to which past practice has evolved optimal designs or design variants for similar situations.

If there is substantial precedence, it is likely that design choices will be determined by past best practice.  Past best practice may exist in a number of forms, including:  formal design rules, modular components or established design heuristics.

In the organizational context, best practice is often promulgated by consultants and the management literature.  However, quite often not enough attention is given to describing the context for the supposed best practice or to assessing the contextual factors critical to the success of the ‘best practice’.  As a result, many ‘best practice’ organizational improvement initiatives fail to achieve expectations because of significant contextual differences between the best practice context and that of the implementing organization.  As I will discuss in a future blog post, adaptive iteration should be used to tailor and refine the design and implementation of many such initiatives.

If there is limited relevant precedence and the context is stable and predictable, it is likely that the design choices will be made by experts through a combination of their experience and analysis based on existing information and codified knowledge.  If the context is complex and unpredictable, experience and past practice are significantly less valuable in determining optimal design choices.  In such cases, the designer should consider an iterative hypothesis based approach.  The initial design choices are recognised as informed predictions, usually involving input from experts, that need to be evaluated and refined through repeated research and testing (experiments).  In a sense, the designers have a dialogue with the context.

In comparison with expert driven design, hypothesis driven design involves greater use of multi-disciplinary and multi-perspective teams.  It favours early action (prototypes, concept outlines, story boards, ‘sighter’ trials, etc) to generate feedback and learning.