Innovation & sowing the seeds of Disruption

It is inevitable that, when an industry sees companies struggling to lead, grow or even maintain homeostasis, organisations will shift focus to innovation or disruption. They have to justify continued financial support from investors or parent companies; they have to prove the vision of the CEO is in line with the board’s goals; they have to prove they are providing value to return profit.

We are in an unfamiliar market landscape, populated by a new, populous generation who don’t think, act or engage like the old. Everything is moving faster than ever, and entire sectors (such as retail) are struggling. I’ve spoken about the shift of global economy and the failures of older management styles to keep up in earlier posts, and I’ve also spoken and posted about innovation a lot recently, as it’s becoming ever more a focus as more organisations try to find their footing in the Cycle of Woe, so perhaps now is a good time to explore the current market approach of many companies in more detail and collate my thoughts overall; I’d also like to explore disruption, which I’ve spoken about and worked with before, and I am seeing many more people discussing.

There is a reasonable rule of thumb here:

All Disruptors are Innovators, but not all Innovators are Disruptors.

I think this usually holds true, but not always; sometimes disruption isn’t innovation, but provision of something already needed, existing, and known, but simply not being provided (or that was provided incorrectly and failed). If that need is identified, or a company fails to find a key differentiator or novelty by which to dominate an often saturated market, the focus may shift to disruption in an attempt to change the market itself. Often, a business conflates the two; sometimes, they are both possible at once.

So, let’s explore innovation, disruption, market S-Curves, and more. This is (as usual) by no means conclusive!

 

A Hot Needle

For many years I worked with an executive named Johan in the IT sector, who at the time headed EMEA. When he came on board, he took the entire area from a dead sales stop to market traction and regained relevancy within ~6 months, which was a phenomenal result, but he didn’t do it by being steady and organic.

Instead, he quite forcefully pushed and demanded, both internally and externally; he made waves, acted quickly, innovated with pricing structures and products that were different and attention-catching, and disrupted people’s expectations and business alike. He didn’t always make friends, but he did make a big difference.

One of his more infamous moments was when he upset a few other players in the market (and I’m sure ruffled a few feathers internally) by publicly announcing that we were going to give the VAR channel “a poke with a hot needle” when the industry least expected it (and due to a lacklustre rebrand, had possibly mis-recognised the company as a potential newcomer).

It was a provocative comment, which had the desired result – people sat up, took notice, argued, laughed, or queried, and the market realised a quite radical shift allowing SME resellers a way into cloud against the larger players, both from the process focus and from new innovations we offered them. It also conveniently spread the rebranded name of the company quickly and re-established the technology as relevant. It wasn’t a total orthodoxy change, but it was a new way of thinking – previously only large providers were offering this type of service, and it caused a great stir in the way the industry was viewed, at least short-term. The disruption process was driven by complementary innovation products. Because of Johan we did, briefly, become a hot needle jabbing at the market, and it reacted.

 

 

In a complex/borderline chaotic situation, Johan acted, monitored the feedback, and introduced or re-positioned novel products and offerings. Not everything worked, but it didn’t have to. We picked up on an untapped gap in the EMEA market and enabled smaller companies to compete in the Cloud at the right time – innovation with products, and a relatively quick disruptive shift as a process – in only a few years. Quite an achievement.

It didn’t last, possibly because the company didn’t capitalise on the disruption or gain context from EMEA markets and customers. EMEA wasn’t their core focus, and they didn’t understand it very well; once the company had stabilised, grown, and moved on, it very quickly ossified again, lost a lot of impetus, and methodology became as or more important than results. The Cycle of Woe began again (without Johan, myself, and a number of other people, who had moved to other things). Even a jab like that can be quickly forgotten.

Working with Johan was an interesting experience – we didn’t always agree, but we respected each other’s specialities, and I don’t think either of us would argue with the results we were individually getting. He certainly could be a hot needle (and I’m sure still is!).

 

Types of Innovation

Innovation is often automatically associated with a product. Whilst of course this can apply to services (Spotify is a hybrid example), it’s still essentially a brand.

Many professionals categorise innovation into 3 areas:

 

Incremental

Definitive

Breakthrough

 

There are purists who will insist that true innovation can only be “Breakthrough”, but innovation isn’t necessarily only world-shattering and huge, so I don’t agree with this. It is, however, what most people mean when they speak about innovation as a buzzword: a differentiator that is a breakthrough to success.

I have found that innovation as a concept also tends to be subconsciously considered in two other ways:

 

Being Innovative

Innovating

 

The first is often a goal in and of itself. I’ve attended many companies where they are desperate to differentiate, to become a market leader with any product; they want novelty, and work towards it without direction. It doesn’t matter what it is, just that they do it. It’s an outcome; a badge of worth.

The second is a pathway on a journey that is a coherent, contextual path forward, where they innovate with a product at the core of the business. It’s a part of the process of value delivery; whilst being an achievement, it is an enabler, part of the overall narrative.

I’ve posted plenty about Cynefin in this blog, but as a quick summary in terms of innovation, the main domains all have their place. Because of this I distinguish four types of innovation, not three:

The Cynefin Model showing Order/Unorder, with Disorder in the Centre. All rights reserved Cognitive Edge

 

Incremental Innovation happens in the nicely ordered, rigid, boring, safe space of Obvious, where all works as expected and predicted, and if innovation happens at all, it’s in small, by-the-numbers ways (often debatably innovative, often borderline nice-to-have).

Definitive Innovation happens in the governed, ordered, expertise-driven space of Complication, where all works as expected and predicted but the rules are looser to allow for multiple causes and effects, via key differentiators – stand-out features that not every product has.

Breakthrough or Radical Innovation occurs in the unknown, unordered, dispositional and uncertain space of Complexity, where we can only say what is likely to happen, and there is no clear cause and effect. All we know is that any changes will effect everything, and could be good or bad. It will likely be both serendipitous and unexpected, and often is game-changing both in terms of value delivery and direction. More organisations than you’d think are here.

Disruptive Innovation lies amidst the total unorder and crisis of Chaos where everything is, well, PFU. A very bad place to be – we don’t know what is likely to happen, what is happening, or what we can do, only that we MUST do something. We act or die, essentially. Innovation that occurs here is make or break – if it works it will not only help manage the crisis and differentiate the company, but is likely to be so novel it disrupts entire markets. There is a crossover with Radical innovation here, as chaos can be used as a guarded area to spark disruptive innovation in relative safety using safe-to-fail probes.

It is important to innovate in context, but a vast majority of companies are in Disorder, that red spot in the middle; that is, they believe they are in a specific domain when in fact they are not. This usually errs on the side of believing themselves in very ordered situations when they are in very complex situations, but that is not always the case; the key is that they aren’t where they think they are (or should be). Unless you are extremely lucky, making strategic decisions or trying for innovation here will likely be unproductive or damaging based on a lack of real-world context. Randomness generally lacks coherency; emergence doesn’t necessarily.

So, to summarise: Innovation is change/novelty of varying types, and it can be mild or extremely disruptive.

 

Types of Disruption

When we talk about market disruption, we may mean something that is not necessarily innovative, but may be so required in a market it gets widely adopted enough to become the new orthodoxy. This can happen in such a subtle fashion that it may not be realised until after the fact, unlike innovation, which relies on being extremely visible to spark things like The Hawthorne Effect, i.e. humanity’s interest and adoptive reactions to novelty.

Disruption can include:

New Market Disruption  – Targeting a market where needs are not being met by existing dominant orthodoxies

Low-End Market Disruption – Targeting a market where not all features offered by existing dominant orthodoxies are valued, except by high-end customers

Innovative Disruption – A process where in the short-term a new market is created and grown based on a product or service, and in the long-term finally displaces an existing market

Market disruption is often not a fast process, unless the gaps in the market are crying out for it. It often happens through general adoption over time – thus, a subtle ubiquity – rather than early adoption, “the next big thing”, and ambassadorial representation. It’s perhaps better thought of as a displacement rather than a spearhead. Kickstarter is a great example of tons of innovation which clearly doesn’t disrupt entire markets immediately, or even at all.

There is also another type of disruption which is a result very often of innovation and market disruption, that isn’t often considered by organisations, and which needs to be understood, and that is role disruption – the effect where changes and progression in the marketplace, new technologies, and paradigm shifts all contribute to previous roles no longer being required or substantially changed. This matters, especially to individuals in an ecosystem.

There will be new roles in this Brave New World, of course – for example, automation doesn’t automatically equal removal of humans, often a shift in their expertise and role, or an opportunity to learn new skills – but a lot of change is coming, and has come before. This is especially of concern to the current largest generation – who are no longer Baby Boomers but Millennials – because they face less security, more uncertainty, and more difficulty by a considerable amount than the previous generation.

Role disruption, and the concern over role disruption, can have a number of knock-on effects that need to be addressed by culture, learning, and realistically projected prospects rather than the age-old adherence to inaccurately modelled outcome-based measures and falsely-positive forecasts – some of this was covered in The Red Pill of Management Science.

So to summarise: disruption can occur at the market, the company, or the role level, and is a process, not a product, affecting ubiquity.

 

The conflation of Novelty and Displacement

One thing I find interesting is how many businesses seem to conflate the two concepts, as there is a lot of crossover between the two approaches, which muddies execution. Both of these things represent stages of paradigm shift, and although they often occur independently, they can occur together as well. For example:

When someone says “smartphone”, what phone do you think of?

Chances are it’s an iPhone. It was first to define the form factor as the first true multi-functional smartphone (in the current long-term form), and hasn’t significantly changed since that incredible step forward (past incrementally innovating).

Apple used an incredibly clever and aggressive marketing strategy to make this product unspeakably novel, desirable, functional, and elite. It worked. Everyone who was anyone wanted one. The drawbacks, of which many still exist today, simply didn’t matter.

What’s interesting is that the i stood for two things: individuality, and internet (given everyone then seemed to have one, I consequently also decided it stood for irony). A subtle message which worked; iPhones became the de facto communications device.

Apple managed the rare feat of both innovating AND utterly disrupting the market very quickly. This happened because the market was at a point of orthodoxy change, and it was the exact right time to become the new paradigm, shifting Microsoft’s Apex Predator dominance of software to a new orthodoxy of software and hardware combined in the form of an object of material desire, which not only enhanced functionality and ability for users, but also image and self-worth.

This ubiquity came from incredible brand awareness, and a melding of the OS and hardware into one product. There is only one “iPhone”, which merely manifests in different forms.

However, these drawbacks (high price, poor non-Apple integration, availability, lack of customisation, fragility (especially of screen), constantly changing power adapters, slowdown over time, lack of memory card, sealed battery, requirement for costly Apple store for many minor issues, punitive action for jailbreaking), although intially ignored, became better understood, and slowly the market began to change. Dominance shifted, market presence shifted.

This was due to Android. The first cellphone running it was released a year after the iPhone (the HTC Dream G1), but the OS had actually preceded the iPhones’ – it just hadn’t yet been refined or named.

Android phone manufacturers didn’t particularly innovate, certainly at first; they simply allowed more people the chance to do more, for less, to belong, and they spread, quietly. A much wider range of devices filling the gaps in the market were developed; different combinations of hardware, price point, customisation, and the adoption of a universal charging standard, as well as memory expansions, battery changes, variety of materials, and so forth. People who couldn’t or wouldn’t buy an iPhone took up the diverse legions of Android phones instead, and discovered in many cases they were more capable, less restrictive, and mostly affordable, if not as smooth or elite. With the sheer variety of OS customisation and hardware options, Android phones became far more individualised than iPhones. A plethora of companies sprang up; where they couldn’t compete with the desirability – apart from companies such as Samsung – they competed by offering a piece of the smartphone pie.

Looking at the % of smartphone market share by OS, we see this:

 

As always happens with orthodoxies, once they become widely adopted they are no longer disruptive, or innovative in anything more than small increments. The slow disruption of Android has told over time; iPhone OS phones are still mostly the poster-phone for smartphones, but they are now so omnipresent in consumer consciousness that there is no more novelty. They hold perhaps 23% of the platform market, impressive for one company.

Meanwhile, Android-based phones in their extreme variety hold nearly 75%. They slowly but surely disrupted the market, and became the new norm.

If we then look at smartphone manufacturer market share %, you might expect Apple to be the top of the tree based on the above, but we find:

(Courtesy of https://businesstech.co.za/news/mobile/314372/smartphone-market-share-samsung-vs-apple-vs-huawei/)

 

So when you think “smartphone”, you may think iPhone first through conditioning – but now you also might instead think Samsung, which has gained a larger market share than the original innovative disruptor. And, with a huge presence in Asia and now the west as well as definitive innovations, Huawei is becoming a new name to recognise. The smartphone market is also due a huge upheaval, which is likely to come from foldable screens – which Huawei, again, seems to be at the forefront of.

It is important to remember that innovation doesn’t automatically equal disruption, and that they can be independent or happen together, but they WILL happen. The one constant in business is that change is inevitable – which is why rigid, dominant paradigms eventually fall foul of complacency.

 

Orthodoxies, Paradigms, and Apex Predators

So where does this change occur?

There are a number of places disruptive paradigm shifts and innovation can happen more easily, and a couple where it MUST happen or dire consequences will be faced. I won’t go too deep in this post, but there are several things to be aware of here: the market arranges itself around the Apex Predators, there is a lifecycle to all orthodoxies, and (as ever) context is crucial.

Market Lifecycles

Many people are familiar with the basic market lifecycle, and indeed this is typically used in strategy because of the assumption that you always start with a “green field”, or a standard approach.

But this does not take context into account, which is critical, especially for innovation and disruption to work. You very rarely start from a green field. A more realistic version is Moore’s “Crossing the Chasm” depiction:

Which shows the chasm which must be crossed. Failing here means you never become relevant; crossing means you will see a drastic shift in focus.

It is important to note that many companies do not cross this chasm. Using Kickstarter as an example again, innovation is wildly high and wonderful, but only 36% of companies reach successful funding. The actual % of companies that go on to become a force or even a long-term blip in the market is much lower than that. Very few end up disruptors.

84% of the top projects ship late; many of them find resource problems, even liquidate soon after creating the successfully funded product. Creating a stable, profitable company afterwards requires other funding and skills (Angel, Venture, etc) and a continuous value delivery stream. If the whole company is based on one innovative product, and people quickly lose attraction to novelty, and that’s all you had, you’re dead in the water.

A key shift here is from Sell to Make to Make to Sell. Leadership often assume or require that initial early adopter sales will continue linearly, and they don’t. A decline in sales usually leads to reduced funding, lost confidence, and not enough push to get across the chasm. Innovation is not a guarantee of success; market disruption is not a guarantee of success.

So, how do we introduce radically new, innovative products on the other side of the chasm?

S-Curves

One method is to add novelty to what people already know they want; the desire for novelty then crosses the chasm because it becomes, for a time, more important than the product. This sparks mass uptake and desire for the product which breaches the gap; this is definitive innovation from Complication.

Markets are constantly in flux with this behaviour; phone cameras are a good example. All phones have them, and they have gone from being ignored to being used constantly for everything from selfies to business receipts.

Huawei’s standout features on their P20 Pro flagship (at the time) were the triple lens camera that delivers incredible pictures that still blow many other phones – iPhone included – out of the water, an insane battery life, and an eye-catching 2-tone twilight colour reminiscent of the old TVRs. A smartphone is a smartphone – but this differentiated them enough to lead to a huge surge in Huawei sales in the west (which continued until the recent widely-broadcast concerns about the technology and security, which consequently have led to a current decline). It didn’t hurt that they had less reports of batteries exploding than the two leaders, either. The novelty told: P20 Pros became more desirable than Samsungs or iPhones for many people within the last 2 years.

So how we achieve this symbiosis? How do we innovate and disrupt at the opportune point?

The best way is to divine a point where the dominant way to do things is becoming commodified or coming to an end, whether it’s known or not by most players, and find space for novelty or a regime change. This can be additional innovation alongside the orthodoxy, radical or disruptive emergent innovation at the right point, or you may be able to alter the course of the whole paradigm akin to switching a train onto alternative tracks via the subtle spread of process (i.e. disrupt the whole market).

One way to view this is to look at an extension and expansion of the Gartner Hype Cycle:

Credit goes to Dave Snowden of Cognitive Edge, who pioneered the linking of the curves, theories and applications.

 

I’ll discuss S-curves another time, but here we can see a narrative of the relevance to novelty and the hype-cycle at the lower left, and the subsequent establishment of orthodoxy to create Apex Predators within a market. This leads to the eventual beginning of commodification and complacency by the Apex Predators due to being too invested and effective over time. Think of past extinctions caused by becoming a food-chain dependent megapredator that is too specialised, and you’re not far off.

The two key decision making points for understanding where change can integrate with the adoption curve will be reached: the pre-chasm point where weak signals tell you there are opportunities to be explored, and the end-chasm point where you have fallen in, unseeing, and must change to climb back out. If these are ignored, the fall or irrelevancy of an Apex Predator causes a trophic cascade (the radical reshifting of the entire ecosystem, which tend to be defined by Apex Predators).

Meanwhile, the point of “crossing the chasm” and uptake either via disruption, novelty, or both – in context and at the right moment – leads to the rise of new Apex Predators and/or the effects of total market disruption.

There is a lot more to this than that of course, and this doesn’t explain how it fits into Cynefin or other frameworks.

It’s not enough to be innovative; you can have the best product on the market. It’s not even enough to be disruptive; you can infiltrate the market at the low end and spread your net. It’s also about weak signal detection and uptake moments. It’s certainly not about being currently dominant, as that means you are more likely to be blinded to threats.

A last thought here: people (especially in mid-tier management in my experience) often choose moderate quality/profit using known contacts over potentially high quality/profit with new contacts, because it’s safe and hithertofore guaranteed. Or, to put it more succinctly, they will often choose certainty over uncertainty, especially in the context of an uncertain, unknown, changing market situation.

 

Shake-ups

From time to time, every market needs a shake-up, as does every company, preferably not through situations such as the Cycle of Woe. This is what Cynefin and safe-to-fail probes can be used for; to find a new path and avoid complacency before it becomes an issue.

Complacency induces failure, eventually, and this is a real problem, because that failure is often catastrophic. Dave Snowden likens this to falling from a cliff-edge, and when you understand how Cynefin allows you to make sense of scenarios, and how strategic and tactical complacency is widespread and usually unnoticed ESPECIALLY to Apex Predators, you realise this is a very apt analogy.

A firm that is complacent is at great risk of falling off that cliff-edge because someone else’s disruptive innovation has abruptly made them obsolete, which makes it very hard to re-establish coherency again (many companies here end up in death/rebirth unless they can secure new funding or benefactor). Bigger dominant organisations are often more complacent by nature, and the bigger you are, the harder you really can fall.

The long fall from Obvious to Chaos through Complacency-Induced Failure

 

Old giants rarely die; they can become too big for mortality. IBM is still a powerhouse; Microsoft is still gigantic. But with the shift of market and paradigm, the Big 4 in tech today are considered to be Google, Apple, Facebook and Amazon. Occasionally Microsoft joins the club as a fifth member who has some form of tenure – for how long, we don’t know.

If you look at these main examples, which of them are still both innovating and disrupting?

Understanding how to innovate and/or disrupt in context and emergently is vital for companies of any size, arguably more so the bigger they are; being able to see when you can or must do so is equally critical. They must furthermore understand how to do it in this new emergent, uncertain market landscape we’ve never been in before, for an entirely new generation. It’s become much harder to do.

Get it wrong, and you’re strolling near that cliff edge… while you’re looking the other way.

 

The Red Pill of Management Science

Further Into the Matrix

Management Science hasn’t changed much in the mainstream for decades, and people have become exceptionally skilled at navigating a system and command structure that is not always fit for purpose, but has come to be used to try to resolve everything.

I felt it of value to take a further look into some thoughts on systems, organisations, company culture, and decisions via knowledge management matrix.

Traditional and “modern” management science methods are mostly based off Mintzberg’s 10 Strategy Schools, with an expected hybrid outcome of consistent, transferable, repeatable and rigidly controlled performance with an alignment to mission statements and values which are predictable and usually single perfect goals. When this is applied out of context, problems can result.

 

The Cycle of Woe

Many organisations large and small are trapped in a loop of trying to remediate fallout from this approach to everything, whilst continuing to apply it. This produces a cycle which typically lasts 6-12 months, although it can be longer or shorter, and roughly follows this order:

This is not only woeful for the company, but the individuals creating the value streams for the company, and links into crisis management, weak signal detection, S-Curves and Complex Adaptive Systems as well as a whole raft of other subjects.

 

Understanding what lies behind the Cycle

An organisation, and the people that make it up, are complex, as are many situations. Complexity is by nature unordered and therefore not linearly causal (unlike complication or obviousness, where if I do x, I will always get y). It has dispositional states, where you can estimate, even simulate what is likely or unlikely to happen, but you cannot predict with certainty – and that’s an important distinction: prediction is not the same as simulation.

In Fearing Change & Changing Fear, I talked about the matrix below – a core precept of Cynefin, created by Dave Snowden of Cognitive Edge:

 

Cynefin Knowledge Management Matrix (Cognitive Edge)

 

…where Order and Unorder are ontologies (definition of causality) and Rules and Heuristics are epistemologies (knowledge in terms of action).

This time, I’ve added colour to show the relationships between the elements:

Systems dynamics (Systems Thinking) and computational complexity (Mathematical Complexity) take a MODELLING approach which, in most of the popular forms of Systems Thinking, essentially removes human judgement through models and predictive process.

Scientific management (Process Engineering) and anthro-complexity (Social Complexity) take a FRAMEWORK approach, which look at things from different perspectives, and also respect human judgement.

It is important to note that I am by no means saying that Process Engineering and Systems Thinking have no place – Contextual Complexity is the idea that humans can operate in and move between all 4 quadrants of this model, either accidentally or deliberately. In some cases Process Engineering and Systems Thinking are the applicable approach, but when we move outside those quadrants and don’t realise it, their application actually damages success.

Instead, this is about understanding when they have their place, where you currently are in Cynefin’s domain model, and acting appropriately to the context you find yourself in. If you are amidst uncertainty and you cannot resolve conflicting issues within a feasible timeframe based on the evidence… you are probably in the complex unordered domain, and it’s understanding when you are and how to act that is crucial.

This is where things can become a serious problem and catalyse the Cycle above, because the two Ordered quadrants are prone to simplified “recipe” thinking, prediction based on perfect outcomes, and the unthinking application of order in unorder.

 

The worries of modern Management

Many organisations are now in a market/landscape they have no prior experience of or reference for, and this causes fear and concern because we are being forced to change at both a personal and industrial level. They push back against this by acting as they always have using the cycle of woe, but the simple procedures that once worked do not produce new benefits past the very short-term now.

Does any of this come to mind with current or past companies you are aware of?

One of the key reasons for these responses may be because of the still-existing and long-term investment in structures based in Taylorism (which dates back to the 19th century, yet is still a core of today’s management science), a root of Process Engineering. This can be interpreted as the belief and (and action upon the belief) that an organisation is a machine with people as cogs or components that will consistently deliver the exact same output in quality and quantity – or, that an organisation is both inherently ordered and conforms exactly to rules.

Despite the realisation for decades that Taylorism is actually detrimental, because that just isn’t how people work, and supposedly eschewing it in favour of a more Systems Thinking approach and a shift from a perception of “machine” to “human” (Peters, Senge, Nonaka), businesses have not changed it fully.

There has been an effort to balance the Mintzberg et al Process Engineering-centric Schools of Strategy:

 

and the Systems Thinking-centric Schools:

 

 

but this is still an attempt to balance mechanical efficiency with modelled semi-utopia, and the value of people – and thus the organisation’s own value-streams – can tend to get lost along the way. In my own experience of companies I have found a leaning to the Process Engineering side with some nods towards System Thinking, in many cases taking the worst of each to form an organisation in the likeness of a machine with an optimum goal, fresh, dynamic values that aren’t as humanly achievable as they sound, pride in a pseudo-innovative approach, and an inability to sense or react correctly to situations no longer being as desired.

Organisations often use the modified concepts of Taylorism because it is trusted and traditional, despite being proven ineffective for decades, and act as if it will forever output the exact same quality and quantity towards an outcome they are certain they can reach. When forced to drastically change, there is a tendency to jump onto a new orthodoxy or bandwagon of the latest management fad that “worked wonders for x company”. Unless scientifically investigated or proven in context, be wary of “hacks” and “secret methods” – especially if novel, yet already in a new best-selling book!

This is representative of something called The Hawthorne Effect (Snowden), which you can read more about in my post The Secret Shortcuts to Innovation, and is a good example of the trend of applying novel, popular, simplified fads to innovate and fix that are not actually applicable to your organisation, dropping you back into the Cycle of Woe, when your value usually already lies within; it just needs context and sense to emerge.

So… how to get the best value output? This will depend entirely upon your organisation’s unique situation and context.

 

You only get out what you put in… right?

Not necessarily. Whilst you see this quote around a lot, and in certain circumstances it’s true, it isn’t an immutable law, certainly not in business:

 

Cynefin Knowledge Management Output Matrix (Cognitive Edge)

 

Process Engineering is best considered as something automatable, rigid, controlled, with people as components in the process; a machine. This is a simple input/simple output scenario.

Systems Thinking is best considered as the determination of a desired (often semi-utopian) outcome, with a system set up around achieving that goal that is controlled, predicted and measured; an organism, if you like, or often more accurately the desirable model of an organism. This is a complex input/simple output desirability.

Mathematical Complexity is best considered as simple rules being modelled to demonstrate complex behavioural patterns from agents within a system; an algorithmic or simulation approach (remember, simulation ≠ prediction. The former is designed to see what could happen, the latter tries to guess what will happen). This is a simple input/complex output model.

Anthro- or Social Complexity is best considered as trying to understand the dispositional state of the present (or what is likely to happen), then trying to guide the future state by modulation instead of driving (guiding emergence instead of forcing desire) and using vector measurement (feedback defining the parameters of the journey forward) to monitor for new, better opportunities, and basing all of this on all agents within the system; an ecology approach, flexible, innovative and reactive. This is a complex input/complex output emergence.

The required output of organisational value has drastically changed. Once, a local artisan may have arisen to make shoes as a basic human requirement. It required simple or obvious components, basic materials and practices going back perhaps thousands of years, and a complicated element in the form of an expert craftsman (certainly once competitors arrived). Eventually this would grow, as people need new footwear, and become a company, or trade. People had to take a set number of steps in a certain order to reproduce the quality of shoe preferred; gradually, they then expanded the quantity produced in line with growing demand.

Once sufficient complexity and saturation of market/product/company is reached, there is no longer any guarantee of staying within the realms of order and cause and effect, or balancing both quality and quantity using the old methods; you also can’t effectively innovate by modelling, or total controlled rigidity.

Today, companies have grown, globalised, diversified, propagated and moved far into abstract realms providing services as a priority, and the once-simple production of shoes by specialists is a product mass-produced cheaply, efficiently, in multiple materials and at a cost of ethics and craftsmanship; the commodification of the process itself, rather than the product, and a mantra of being innovative. Almost all business today is exponentially more complex, in a likewise exponentially more complex world where knowledge and services have become a primary global economy, 24/7. Companies are finding that you cannot operate as you once could, bureaucratically and hierarchically, because everything has changed, and they need to catch up – fast.

Entrepreneurial SMEs and the EMEA market approach are good at dealing with this. More traditional company structures aren’t, and that’s a problem for huge corporations as well as everyone else.

 

Adding to Value Production

It may also help to understand something further – in simplest terms, each of these is an attempt to augment how we approach the production of value:

 

Cynefin Knowledge Management Augmentation Matrix (Cognitive Edge)

 

As you can see, the two labelled “Cognitive Replacement” are attempts to model ideals or outcomes and replace both productivity and distributed, real-time cognition with their practices or results (almost to force utopia), whereas Process Engineering produces value logically and restrictively (but is prone to bottlenecks) by adding or removing people, components or processes, and Anthro Complexity treats this as a parallelisation of human processing power to more effectively discover the best path in uncertainty and maintain constant feedback to do so. They all have their place dependent on situational context.

 

Collaboration and Culture

Of the four quadrants, collaboration and innovation are most likely to happen in Social Complexity. It’s real-world applicable, reactive and monitored, and the output emerges in a vector-based fashion; in other words, it doesn’t try to define an outcome, unlike Process Engineering and Systems Thinking. A Vector measures intensity and speed of travel from a point (usually the present), and allows you to modulate (guide with feedback) the progress until a viable path emerges. It also takes into account something critical to success, and that is company culture and sub-cultures.

Culture is created by and for the people within the system, but also by the actions and inactions of leadership. It can be beneficial, the glue that welds the company into a cohesive value delivery platform, or it can be incredibly toxic, losing vital agents, morale, collaboration, producing gaming behaviour, cynicism, policies that impede roles, nonsense politics, focus only on immediate reward structures – in short, losing the ability to be effective anything more than short-term. When we talk about real collaboration that is self-creating and sustaining, it is found here. Understand that over-competitiveness and overconstraint via rules/policy/demand of output can be contradictory to success (inducing cynicism and gaming behaviour merely to do the job), and you understand why a holistic ecology needs good culture to operate.

Attempting a culture via Process Engineering, which relies heavily on human involvement, can fail because those humans are individuals and complex, not components in a machine. It is a framework approach, but a heavily constrained one which doesn’t allow for individuality and feedback, and although it does allow for human judgement it expects mechanical efficiency and does not allow for a lack of order within the system. It still expects rules to be adhered to, even if they impede progress and value production.

At the same time, Culture from Systems Thinking, whilst based on some good ideas, has a fundamental flaw of being a model – so whilst ostensibly Systems Thinking says “we allow this is a system of humans with individual traits”, and allows feedback, it removes human judgement in favour of prediction and order, and still expects firm adherence to that order whilst heavily measuring and metricising humans against a perfect vision.

This is a real problem with most popular Systems Thinking – it instils a habit of thinking where you want to be in an ideal world and then trying to close that gap, in other words using outcome based measures – which may have no actual basis in reality. Depending on these then for forecasting, culture, and organisational direction can be dangerous, as can attempting to then control and apply policy to humans, who live in a real complex world not an ideal world, and act accordingly. Systems Thinking does not always apply well to HR, for instance, because measuring complex agents on outcomes which may be unrealistic or require gaming of the system to reach, or demanding people map to a model when they are all individuals, is a decidedly failure-prone way to try to make sense of knowledge, achieve job satisfaction or good morale, or deliver value.

Where both of these approaches often fall down is that they still assume that circumstances and organisations are ordered, even if this is not the case. Forecasts, company message, and guaranteed output heavily rely on a firm goal that must be achieved whatever the cost, or after a tipping point reassessed; all of these induce an initial tunnel-vision that then cannot be seen outside of.

For me, one of the most unforgivable aspects of popular Systems Thinking is that positivity and adherence to the perfect desired outcome is far valued over realism and the achievable – think about how many times management is unhappy with a forecast because “it seems negative” – and the mavericks and heretics who suggest other approaches are often suppressed or punished.

I’d rather a realistic prediction than a comfortable one; and these are the people most likely to spark innovation in an organisation.

 

Why Social Complexity is so effective in uncertainty

Social Complexity takes a different approach, saying that this is an ecological framework, based on individuals working as collaborative agents within an unordered system, and that real-world feedback is critical to assess and modulate goals which may change significantly. The flexible vagary of human input (including outliers) can be harnessed positively instead of suppressed to produce productive, innovative, beneficial output, which may even improve from original preferences. It accepts that predictions cannot be accurate, and allows looser constraints to allow the system to achieve the contextual coherency required to achieve an appropriate goal, find new goals, or spark innovation. This is the closest we get to a cohesive ecosystem delivering the most effective output and value, self-monitoring and constantly feeding back and adjusting.

Contrary to popular management approaches, it says if you find yourself in an uncertain scenario, you must identify where you are NOW, and then see where you can make changes (via probes and contextual constraints), and then monitor vectors as close to real-time as possible as you go forward, allowing for all agents within the system. If you find a path of coherency where you dampen negatives and amplify positives, you may be able to then stabilise this emergent path until you have discovered or even created causalities, transitioning through a liminal domain into linear causality (Complication), whereupon you can breathe a little more easily and create governing constraints.

How you get here and where you get to may not be where popular Systems Thinking had you start out, trying to attain an idealistic outcome, because you have probed for new possibilities to close the gap between here and a REAL outcome – which could be even better than the original goal, and is likely to be more realistic.

In other words, the journey, which never ends and allows novelty, serendipity, and new paths to be discovered en route, is really more important than a goal where you are so set on the target that you don’t see alternatives – or the fact you might never actually be able to get there.

 

What does this mean for an Organisation?

Well, not that you have to drop everything and instantly decide to attack every situation as complex, remember. It’s more about understanding internally to the company the cultures, departments, and people making them up as reactive pieces of a holistic ecological whole, and learning how to divine what is a complex situation and how to make sense of it; Contextual Complexity and appropriate action. The simple fact is you probably aren’t experiencing issues if you are within the ordered quadrants you think you are. It’s when you think you’re still there and you’re not that problems rapidly arise.

As mentioned in a few posts on this blog, there are a number of reasons many organisations today (and many of those don’t understand they have issues yet) simply don’t seem to understand their markets, their employees, where they are going, or how or if they can get there.

There are several ways to approach these issues, and as companies become aware of them they get inevitably caught on buzzwords and “certified approaches”, but one of the best is simply engaging a multi-methodology consultant who – rather than come in as an expert in one specific popular approach to do disassociated work for the company then leave – advises, jiggles, and helps the organisation learn how to sense-make (instead of reflexively categorise) for themselves, then change from within using a mixture of appropriate methodologies and frameworks. This helps create a sustainable, learning organisation one step closer to a collaborative ecology, and lets them focus on the value they deliver instead of the internal struggles they faced.

It’s all about dowsing for context and coherency – decoding where you are in the matrix and acting accordingly – and that’s what we’re here for – nudging, education, and paradigm shifts.

…I don’t recommend the blue pill. It leads to a Cycle of Woe.