While System Thinking is gaining popularity in the innovation world, not all innovation challenges need it or benefit from it. It is important to get skilled in understanding which challenges benefit from System Design, and which do not, so to avoid departing from established innovation practices and introducing the complexities of System Thinking methodologies without a real need for them.
For example, a car manufacturer that wants to design and launch a new car can do so with classic innovation and New Product Development frameworks. Although highly complicated and expensive in terms of time and resources, a new car would require pre-existing technologies, would be built in existing plants, and would be sold via traditional selling channels. While complicated, such a challenge is not "complex" per se, and System Design would be of limited use here.
Innovating an urban transportation infrastructure is, instead, another story. Traditional approaches may focus on alleviating traffic congestion through isolated interventions like widening roads or upgrading public transport, dramatically overlooking interconnected effects. However, a systems thinker would recognise the multifaceted interactions — population growth, urban development, socioeconomic factors, and environmental impact — and would design more effective solutions.
The following is a list of criteria that help to distinguish systemic challenges from the non-systemic ones, in order to better understand when to employ System Design and when more established innovation frameworks like Design Thinking or Lean methodologies.
Interconnected components
Challenges where various components are interrelated and affect each other's performance or behaviour.
Examples: supply chain management, healthcare systems, and environmental sustainability.Dynamic and nonlinear relationships
Challenges with dynamic and nonlinear relationships, where changes in one part of the system can have unpredictable effects on other parts.
Examples: economic systems, climate change, and social networks.High likelihood of unintended consequences
Challenges where interventions or changes may lead to unintended consequences that affect the overall system.
Examples: policy implementation, technology adoption, and organizational restructuring.Strong feedback loops
Challenges characterised by feedback loops, where outputs of a system influence its own behavior.
Examples: market dynamics, educational systems, and innovation ecosystems.
Embracing such intricacies necessitates a departure from linear problem-solving methodologies and an embrace of systems thinking — a paradigmatic lens that views challenges as dynamic, interconnected systems rather than isolated components.
Emergent properties
Challenges where the system exhibits emergent properties that are not immediately evident from the behavior of individual components.
Examples: the emergence of new technologies, cultural shifts, and complex biological systems.Long-term effects
Challenges with long-term implications and impacts that may not be apparent in the short term.
Examples: sustainable development, infrastructure planning, and climate change mitigation.Multiple stakeholders
Challenges involving multiple stakeholders with diverse interests and perspectives.
Examples: public policy issues, collaborative research projects, and community development initiatives.Public-Private nature
Innovation challenges that require both public and private intervention to work effectively — with the public sector involved in policy making and infrastructure building, and the private sector in monetisation and delivery.Uncertainty and ambiguity
Challenges where there is a high degree of uncertainty and ambiguity in understanding the problem or predicting outcomes.
Examples: early-stage technology development, market disruptions, and social innovation.Cross-disciplinary nature
Challenges that require integration of knowledge from multiple disciplines to fully understand and address the complexity of the problem.
Examples: healthcare innovation, smart cities development, and sustainable agriculture.
High indirect, intangible, or unknown costs
Challenges that show a high ratio of indirect, intangible, and/or stochastic costs over the known costs add a large degree of uncertainty and unfairness, as indirect costs often fall on actors external to the innovation process.
Examples: smart-work policies, and innovations with high cultural impact.High indirect, intangible, or unknown benefits
Some innovations may carry great benefits, but they are largely hard to capture.
Examples: urban green, art, and Universal Basic Income.Many-to-many value exchanges
While most of the market economy is transactional, with 1:1 value exchanges, a much larger amount of systemic challenges may need to leverage many-to-many complex value exchanges.
Examples: Digital Product Passports, Georgist policies, and environmental policies.
If the innovation challenge you are tackling checks one or more of these criteria, you may want to apply system thinking methodologies, such as system dynamics, causal loop diagrams, and scenario planning, to gain a deeper understanding of the system in which you are operating, identify leverage points, and design more effective and sustainable solutions.
