Revising the S-Curve in an age of emergence

Exploring how the S-Curve can help us with leadership, strategy and decisions making in an age of emergence: (properties or behaviours which only emerge when the parts interact as part of an inclusive whole)

History and context

There is a special place in our business hearts and minds for the “S” curve or Sigmoid function, calling it by its proper maths name. The origin of the S curve goes back to the study of population growth by Pierre-Fran├žois Verhulst c.1838. Verhulst was influenced by Thomas Malthus’ “An Essay on the Principle of Population” which showed that growth of a biological population is self-limiting by the finite amount of available resources. The logistic equation is also sometimes called the Verhulst-Pearl equation following its rediscovery in 1920. Alfred J. Lotka derived the equation again in 1925, calling it the law of population growth but he is better known for his predator: prey model.  

In 1957 business strategists Joe Bohlen and George Beal published the Diffusion Process. Taking the adoption curve and adding cumulatively the take up the product to gain a “classic S curve.”  

The market adoption curve became the basis for explaining innovation and growth as a broader market economic concept by the late 1960s. We started to consider the incubation of ideas to create new businesses and how we need a flow of products/ services within big companies.

From this thinking emerged two concepts, as the shareholder primacy model became central to growth.  The first is the concept of “Curve Jumping” to ensure that you continue growth by keeping shareholders happy through the continuous introduction of new products, as the existing ones have matured. Of course, the downside is that if a business cannot jump because of its current cost base or ability to adopt the latest technology to perpetuate the ascension of the curve, new companies will emerge with competitive advantages (product or cost) as they jump to new technologies.  Milton Friedman’s emphasis on shareholder value maximisation at the expense of other considerations was driving companies to keep up with the next curve of fear of being left behind competitively. Some sorts of competition are healthier for markets than others, and it appears that competition and anxiety relating to retaining technology leadership at all costs have been driving capitalism in a particularly destructive direction, rather than encouraging useful and sustainable friendly innovation.  There is an economics essay to be written here, but this piece is about the S curve.


Right here, right now.

We live in a time when crisis and systematic “emergent properties” are gaining attention and prominence.  Emergence by definition occurs when an entity or system is observed to have properties that its parts do not pose or display on their own. Properties or behaviours which only emerge when the parts interact in the broader system, as we see our businesses, we understand as complex adaptive systems. 

Whilst shareholder primacy as an economic driver faded in 1990 to be replaced finally in 2019 with Colin Mayer work on the Purpose Model, a modern standard for corporate responsibility which makes an equal commitment to all stakeholders. Shareholder primacy’s simplicity has remained a stalwart of leadership training, teaching and therefore, management thinking.  Its simplicity meant we did not have to deal with contradictions and conflicting requirements that a broader purpose would expose. The Business Round Table Aug 2019 and Blackrock's CEO Larry Fink letters to CEOs/ Shareholders are critical milestones in turning thinking away from pure shareholder returns as the reason for a business to exist. The shift is towards eco-systems and ESG (Environmental sustainability, Social responsibility and better oversight and Governance) as primary drives.  The FCA Stewardship code, Section 172 of the companies act and decision reporting are some of the first legislative instruments on this journey. With now over 50 series A funded startups active in ESG reporting, impact investing has become a meme as the development of a more standardised and comprehensive purpose reporting has strengthened.   

Shareholder primacy’s simplicity meant we did not have to deal with contradictions and conflicting requirements that a broader purpose would expose.

With this new framing, it is time to revisit the S-Curve. 

Framing the S-Curve for an evolutionary journey

If you have not yet discovered Simon Wardley and his mapping thinking, stop here and watch this.   Simon has a brilliant S-Curve with pioneers, settlers and town planners, really worth looking up. His model is about evolution (journey towards commodity) rather than diffusion (take up over time).  To quote Simon “The evolution of a single act from genesis to commodity may involve hundreds if not thousands of diffusion curves for each evolving instance of that act, each with their own chasm.”

The next S-Curve below, I am building on Simon’s axis of Ubiquity (how common something is) and Certainty (the likelihood of the outcome being as determined) which is an evolution S-Curve.  On these axes, we are plotting the systems and company development - time is not present, but as we will see, we have to remove time from the framing.  

Starting in the bottom left is the activity of Innovation where uniquity is low, as it is an idea, and it is not available to everyone. The certainty that any innovation will work is low.


The top right corner is the perceived north star.  Ubiquity is high as everyone has it, and there is high certainty that it works.  In this top right corner are commodities and utilities, technology at scale, by example turning on the water tap and drinking water flows.  Linking innovation and commodity is an evolution or journey S-Curve. Under this curve, we can talk about the transformation of the company, the companies practices, data, controls and what models it will most likely utilise.  The chart below highlights the most popular thinking in each stage and is certainly not exclusive.  Agile works well in all phases, AI can be used anywhere, except for choice, and data is not as definite as the buckets would suggest.  Control changes as we evolve from lean/ MVP in the first delivery, to using methodologies such as agile and scrum, then Prince 2 as a grown-up project management tool at scale and then towards quality management with 6 Sigma. 

Note: I have a passionate dislike of the term “best practice” as it only applies when in the linear phase but is literally applied everywhere.  At linear, you have evidence and data to support what “best” looks like.  At any stage before ubiquity and certainty, best practice is simply not possible other than by lucking out.  A desire for best practice ignores that you have to learn and prove what is it before you find it is best. And to all those who have the best - you can still do better, so how can it be best?

If one considers the idea of time and S-Curves you get to curve jumping or continual product development as set out earlier.  The purpose of an evolution or journey S-Curve presented in this way is that when time is not the axis, any significant business will have all these activities present at all times (continual adaptation/ evolution not diffusion). In nature, all different levels of species exist at the same time from a single cell, to complex organisms.  Innovation is not a thing; it is a journey, where you have to be all the camps, on the route, at the same time. 

Innovation is not a thing; it is a journey where you have to be all the camps, on the route, at the same time. 

Evolution S-Curve and governance

HBR argues that most capitalists markets are in a post-shareholder primacy model, meaning the purpose of an organisation is up for debate. Still, we are on the route to a more inclusive purpose and reason for a company to exist.  Law already exists in the UK as Section 172 of the Companies Act in the form of directors duties.  The global pandemic has highlighted a bunch of significant weakness that emerges from our focus on growth and shareholder focus, including, as examples only:

  1. Highly integrated long supply chains are incredibly efficient, but are very brittle and not resilient  - we lost effectiveness.

  2. A business needs to re-balance towards effectiveness.  A food company in a pandemic exists to get food to customers (effectiveness) not to drive efficiency at any cost.

  3. Ecosystem sustainability is more important than any single company's fortunes.

  4. ESG, risk, being better ancestors, costing the earth and climate change are extremely difficult on your own.

  5. Our existing risk models focus on resource allocation, safety and control. This framing means that new risk created in a digital-first world may be outside of the frame and therefore hidden in plain sight. 

Given this framing and context, it is worth overlaying governance on the S-curve of start-up development, which we will now unpack.  

Governance has centrally focussed on corporates and large companies who offer products and services to mass markets. By concentrating governance on companies who have scale, if they are well managed, and is there independence of oversight, we have framed governance as only of interest for companies where there is an interest to wider society on their behaviour. Indeed it becomes a burden rather than a value. 

Companies of scale tend to be found in the linear quadrant, top right, where growth is mainly incremental and linear.  Regulation and markets focus on “BEST practices” which have been derived over a long period. The data used is highly modelled, and the application of AI creates new opportunities and value.  Control is exercised through the utilisation of 6 Sigma for quality (repeatability) and other advanced program management techniques. KPI’s enable the delegation of actions and the monitoring, control thereof.  The business model is that of exercising good or “best” decision making, based on resource allocation and risk.   

Unpacking Corporate Governance is a broad and thorny topic, but foundations such as The Cadbury Report (1992) and the Sarbanes–Oxley Act (2002) have been instrumental in framing mandates.  However, governance, compliance and risk management became one topic in c.2007 and lost the clear separation of function.  Regulation has also formed an effective backstop to control behaviours and market abuse.  

The point is, when a company is at the scale, we have created “best governance practises and guidance”,  along with excellent risk frameworks and stewardship codes for investors.   Many of the tools and methods have stood the test of time and provide confidence to the market.  However, these tools and frameworks are designed for companies at scale.  On the journey from startup to scale, the adoption of such heavyweight practices in early development would be overly burdensome for emergent companies and are not a best or a good fit.  

Remembering that any company of scale has all these phases present at the same time, but there are five possible camps or phases where we need governance; three are in orange and two in yellow.   The yellow blocks represent phases where there is a degree of stability insomuch that there can be growth, but there is not a wholesale change in everything.  The orange block represents phases where everything is changing.   Yellow blocks indicate complicated oversight,  where orange suggests complex.  

To be clear, it is not that companies or markets in a linear phase are not complex; it is that the management at linear has more certainty in terms of practices and forecasting coupled with having to deal with less change. When there is a product of service at linear it delivers significant noisy signals and priorities that often overshadow any data or insights from other phases.  Management at scale requires a focus on understanding the delta between the plan and the executed outcome and making minor adjustments to optimise.  

The management during the yellow stable growth camps/ phases is complicated as patterns and data will not always be that useful.  Data will not be able to point to a definitive decision directly. Governance provides assurance and insights as management continually searches for the correct data to make decisions on, which may not be there.  Management during an orange highly volatile camps/ phases is more complicated as you cannot rely on existing data during a transition between what you had and the new. Simply put if you did, you will only get what you have and not be able to move to the new.  The idea of transition is that the old is left behind. Experienced leadership will show skills in seeking small signals from the noisy existing data and the noise. When considering governance through this dynamic lens, it is apparent that it becomes much more challenging and that we cannot rely on the wisdom and best practices of linear. 

Plans at scale are more comfortable and more predictable; they are designed to enable the measurement of a delta. Plans during innovation are precisely the opposite, not easy and highly unpredictable.  Using the same word “plan” in both cases means we lose definition and distinction.  

  • A plan at scale is built on years of data and modelled to be highly reliable; it is accurate and has a level of detail that can create KPIs for reporting.  The plan and the model is a fantastic prediction tool.  

  • A plan at start-up and growth is about direction and intention. Failure to have one would be catastrophic,  but with the first few hours of the words being committed to a shared document, the plan is out of date.  To be useful, it has to lack precision, detail and measurement but will set out stages, actions and outcomes.  It must have a purpose, direction and how to frame complex decisions. 

Similarly, governance at scale is more comfortable and more predictable; governance is about understanding where and how delta might arise and be ready for it. Governance during innovation is precisely the opposite, not easy and highly unpredictable.  Using the same word “Governance” in both cases means we lose definition and distinction.

Using the same word “Governance” at scale and in startup cases means we lose definition and distinction.    

Complexity: Organisational mash-ups

Many businesses are mash-ups of previous transformations, plus current evolution. This observation has two ramifications: One, the structure, processes and skills are neither fully aligned to the original model or various constructions of a new model.  Two, data shows that as you categorise focus and alignment in the more senior positions, and who have been in post longer, most have a compass or alignment coupled with a mash-up of a previous model. Bluntly they stopped on the evolution path creating a dead end.  Senior management who tend to have a closed mindset, rather than an open and continually learning one, tend to fall back on the experience of previous best practices, models and pre-transformational ideals, adding a significant burden to governance for any stage.  The concept that there is a direct coupling between innovation and KPI measurement, which makes it harder for corporates to innovate and evolve is explored in this article.   

All companies have an increasing dependence on ecosystems for their growth and survival.  Ecosystem health is critical for companies at scale for supply chains and customers.  Companies who operate at scale and in the linear phase, therefore, are dependent on companies who are in different stages on a planned route to scale. Thus, not only is a scale company dealing with its internal governance and innovation requirements as already noted, but the directors have to understand data from an ecosystem, who is also trying to understand what their data is telling them about their evolution path.  

Directors have to understand data from an ecosystem, who is also trying to understand what their data is telling them about their evolution path.  

Governance is not about best practices and processes at any stage; it is about a mindset of an entire organisation and now ecosystem.  When you reflect on it, Directors with governance responsibilities have to cope with data for decisions from chaotic and linear requirements at the same time — equally relying on individuals and teams who have different perceptions both inside and outside of the organisation. Never has data-sharing been more important as a concept, both as a tool or weapon (inaccurate data) in competitive markets.   How can a director know that the data they get from their ecosystem can support their decision making and complex judgement?

Take Away

The S-curve has helped us on several journeys thus far. It supported our understanding of adoption and growth; it can now be critical in helping us understand the development and evolution of governance towards a sustainable future.  An evolutionary S-curve is more applicable than ever as we enter a new phase of emergence.  Our actions and behaviours emerge when we grasp that all parts of our ecosystem interact as a more comprehensive whole. 

A governance S-curve can help us unpack new risks in this dependent ecosystem so that we can make better judgements that lead to better outcomes. What is evident is that we need far more than proof, lineage and provenance of data from a wide ecosystem if we are going to create better judgement environments, we need a new platform. Such a new platform is my focus and why I am working on Digital20.