How we actually think

What sustainability actually means

Sustainability is one of the most used and least understood words in business. The confusion is not accidental. It reflects a genuine conceptual difficulty: the word describes a relationship between a human activity and the systems that sustain it, but it is most often applied as if it described a property of objects or organizations in isolation.

An organization is not sustainable. An activity is sustainable if, and only if, the conditions that make it possible are maintained or improved by the activity itself, over time. Sustainability describes the state of a system, not the qualities of any single component within it. This distinction sounds abstract until you try to set a meaningful goal. At that point, it becomes the difference between a target that is connected to reality and one that is not.

The SiD framework begins with this definition and builds outward from it. The first question it asks about any strategic challenge is: what are the conditions that make the organization’s activities possible, what is the current state of those conditions, and what is the trajectory? These are empirical questions. They have answers that can be measured, and those answers determine what a meaningful strategy looks like.

The twelve rules of complex systems

The SiD framework incorporates a set of structural rules that describe how complex systems behave. These rules are not theoretical constructs. They are empirical regularities observed across biological, ecological, social, and economic systems, confirmed across hundreds of projects and decades of applied research. Understanding them changes what strategies you design and what outcomes you expect.

The most consequential rules for organizational strategy are these. Complex systems are non-linear: small changes in one part can produce large changes elsewhere, and the relationship between cause and effect is not proportional. Complex systems contain time delays: causes and consequences are separated in time in ways that make intuition unreliable, because we tend to attribute consequences to the most recent visible cause rather than the actual one. Complex systems have emergent properties: the behavior of the whole cannot be predicted from the behavior of the parts in isolation. And complex systems adapt: they respond to interventions in ways that the intervenors did not design and often did not anticipate.

These rules have direct implications for how strategy should be designed. Non-linearity means that leverage exists in specific places, and finding those places matters more than applying uniform pressure everywhere. Time delays mean that early indicators of future conditions are more valuable than current performance metrics. Emergence means that portfolio thinking, designing multiple coordinated interventions rather than single initiatives, is more likely to produce the desired system-level outcome. And adaptivity means that building learning into the design, not as an afterthought but as a structural feature, is a prerequisite for durable results.

The toothbrush problem

Consider a toothbrush. By most measures of product sustainability, it performs poorly: it is made from mixed materials that are difficult to separate for recycling, it is replaced every three months by most users, and it ends up in landfill because the scale of collection required to make recycling economically viable does not exist. A conventional sustainability analysis of the toothbrush focuses on material composition and disposal.

A systemic analysis asks a different question: what is the network that makes toothbrush production and disposal possible, and what would need to change in that network for the product to be genuinely sustainable? The answer involves oral health systems, dental care distribution networks, material recycling infrastructure, and consumer behavior at scale. None of these are within the control of any single toothbrush manufacturer. But a manufacturer who understands the network can identify where intervention is possible, what coordination is required, and what the realistic path from the current state to a sustainable one looks like.

This is the network-as-power argument that underlies the SiD framework: the value of understanding a system lies not just in the analytical clarity it produces but in the strategic options it reveals, because many leverage points exist at the network level rather than the organizational level, and they are only visible to actors who have mapped the network.

From analysis to roadmap

Systemic analysis produces a picture of the current state, the dynamics that are producing it, and the leverage points where intervention would be most effective. The next task is to turn that picture into a roadmap: a sequence of decisions and investments, governed by defined criteria, that moves the organization from its current position toward its defined goals.

The backcasting method central to SiD begins with the desired future state and works backward to identify the decisions that make it achievable. This is different from forecasting, which extrapolates from current trends to a projected future. Backcasting asks: given where we need to be, what must be true at each earlier point, and what decisions now constrain or enable what is possible later? The method is particularly valuable for transitions because it reveals the path dependencies that forecasting typically obscures.

The practical output is a transition roadmap with explicit milestones, decision points, and learning loops. The milestones specify what must be true for the transition to be on track. The decision points specify who has authority to make which choices, and under what conditions. The learning loops specify what monitoring indicators matter, what they will tell you about the underlying system dynamics, and how that information feeds back into the roadmap as it evolves.

This is how the SiD framework becomes an operational tool rather than a conceptual one. The analysis tells you where to intervene. The roadmap tells you in what sequence, governed by what rules. And the governance architecture tells you who is responsible for ensuring that the learning loops actually close, and that the strategy adapts as the system does.