Making Design Thinking Work in Complex Ecosystems

In Roger Martin’s influential bookThe Design of Business, he describes how companies risk becoming trapped in an “exploitation mindset”. This can happen when they become so focused on honing their current business model or “algorithm”, that they cease to explore new problem spaces and opportunities for growth and renewal – or “mysteries” as he calls them.

One important reason for this, Martin suggests, is that such companies tend to overuse or depend on current or past metrics of business performance and innovation opportunity. They rely on market research or sales-customer interactions to learn only how to improve existing products or operations or to evaluate new concepts in the context of their current business model. As a result, these analytical, reliability-oriented companies are less able to envision the future by making bold “leaps of imagination” to escape their dominant logic. They fail to deliver transformative value, are slow to adapt their strategy and operating model and thus are at higher risk of disruption and ultimately, failure.

Martin is, of course, an advocate of design thinking – a mindset that entails making intuitive leaps over entrenched company heuristics in order to explore new markets, solutions and offerings that may have some validity in the future. Through a better synthesis of analytical, rational, reliability-focused activity with more interpretive, questioning and reflective behaviour, design thinkers help companies to move up and down what Martin terms “the knowledge funnel” – from mystery to heuristic to algorithm and then back again. In other words, design thinking gives companies an enhanced capability to explore new mysteries and discover novel opportunities, thereby avoiding the narrowing forces of exploitation and algorithm-improving efficiency at the thin end of the funnel.

In practice, the dominant mode of value  creation in design thinking is to co-evolve problem understanding with solution design. Through the medium of a prototype and via a process of ongoing, iterative engagement with customers or users, learning about user problems evolves alongside the capture of insights about solutions and which might work best. Users evaluate successive iterations of a solution through the lens of experience quality until they reach agreement with designers that they have arrived at a final acceptable product or service concept.

I agree with Martin that past data and an excess of analytical, efficiency-oriented thinking can handicap companies. Also, I concur that design thinking can be an effective means to overcome the exploitation / algorithm trap – when executed well. But I disagree that design thinking is the panacea for curing the ills of analytical over-reliability in all markets and in all contexts. Whilst the practice of “build-test-fail-learn-iterate” can lead to elegant innovation and superior user experiences in relatively simple, linear 1:1 company-customer markets such as consumer goods or tech, in markets characterised by many stakeholders with often competing interests, design thinking is less effective.

There are two reasons why. First, in many markets such as healthcare, it is simply not possible to test and iterate a prototype with stakeholders (such as patients, even payers in some markets!) due to safety or regulatory limitations (think of an invasive diagnostic test or of course, a drug delivery mechanism). Second, in complex systems consisting of multiple stakeholders and goals, where intervention in one part of the system may lead to negative outcomes in another, there is a greater need for problem learning before solution design for a variety of reasons. In the rest of this article, I shall focus on the second of these limitations.

The complexities of ecosystem innovation and intervention

In dynamic and complex ecosystems such as healthcare, energy, environment, cities, education, development and government, any innovator must be prepared to identify and satisfy multiple stakeholder goals simultaneously. They must understand how systems adapt and reorganize to meet those goals, often achieved through bottom-up incremental practice-based – not top-down – change. Also, they must recognise that value to a stakeholder is not only embodied in the functional purpose of a product or technology but rather, is also realised in the quality of interactions and experiences that exist between stakeholders as well as the knowledge that passes between them. In addition, necessary insight into complexity is missed if only one stakeholder group is asked about their problems and opportunities. If only the core problem owner is engaged – such as the patient – then the full extent of paradoxes or conflicts between all stakeholders may be neglected.

Hence, in these contexts, there is a much greater need to understand current practice, routines, context, variations, constraints, paradoxes and conflicts of priority and need amongst and within stakeholder groups. From a design thinking point of view, this is fundamental for identifying and defining opportunity, framing value-creation and then shaping valid ideas and solutions. Failing to do any of these well can lead to the creation of sub-optimal solutions that do not address priority outcomes, may imbalance system dynamics and can even increase stakeholder conflict. Rather like the parable of the learned blind men touching an elephant, seeing or focusing on only one part of such complex systems will only produce limited insight (especially at the tail end!).

So how can design thinking step-up and play a bigger role in making more effective systems innovation and intervention? For me, the answer lies in innovating the methods and units of analysis deployed for identifying and defining problems.

Stepping up Design Thinking in Ecosystems: It’s all in the inputs

If we are to capture problem inputs and evidence before solution design in complex ecosystem markets and contexts, then we need to overcome the limitations of capturing stakeholder inputs based on past or current perceptions and experiences of value and products. As Martin rightly states, many companies make the mistake of gathering needs and other problem inputs only in relation to their or their rivals’ existing or planned products and solutions.

Fortunately, there is an alternative. I argue that it is possible to capture highly useful and valid problem inputs from stakeholders without the need for problem and solution co-evolution through a prototype. Future-based insights to inform ideas and solutions can be captured from customers and stakeholders. It is simply a matter of using the right units of analysis and asking people the right kinds of questions. Let me explain further using examples from the diabetes care ecosystem.

Example: The diabetes care ecosystem

Patients (people) have health goals. For any health goal, a patient will hold or be ascribed a measure of achievement or a target outcome. Consider a Type 2 diabetic (T2D) person. They may have a health goal to reduce their A1c level – a measure of blood glucose. For such a goal, their target outcome may be to reduce their A1c level by 10% within 6 months, whether personally set or prescribed by a Health Care Practitioner (HCP). Their ability to achieve this outcome is a function of the knowledge, skill as well as the mental (especially motivational), material (products, drugs, devices) resources they possess, can access, are provided, can acquire and can use. It is also a function of the different contexts (social, environment, economic, cultural, space, time) that may enable or constrain their capability.

Patients also have functioning goals. These are non-health goals that they wish to be or do as a result of achieving a health goal. For example, a T2D person may wish to start a new job, find a partner, take part in sports, have children, leave home, etc. Functioning goals can provide essential motivation for an individual patient to achieve a health goal.

It is important to note that patients with the same goal will have different measures of target outcome they wish to achieve. This reflects their perceived and actual capability to achieve that outcome and the functioning goals they wish to realise by doing so. Also, patients have widely varying resources – particularly bodily, cognitive and social resources. These greatly influence their capability and also willingness to perform (or avoid performing) activities to achieve their target outcome.

HCPs also have health goals. Typically, these are to help patients achieve their target outcome through education, diagnosis, treatment, monitoring, support and counselling. They also have functioning goals such as to enjoy work, feel valued, have influence over colleagues, secure a promotion, etc. Just as for patients, an HCP’s ability to achieve a target outcome is a function of the knowledge, skill and material (products, drugs, devices, guidelines) resources they possess, can access and deploy to do so. It is also influenced heavily by their practice and health system contexts that may enable or constrain that ability. HCPs have varying types and levels of resources at their disposal in different settings and across multiple contexts. The availability, amount, type and cost of resources greatly shape their capability to achieve target outcomes, whether set personally or set by managers or public health bodies at the system or care setting level.

Just as for patients, different HCPs with the same goal will have different measures of what target outcomes can be achieved, sometimes even for the same patient. This variation reflects both their and their patients’ perceived and actual capability to achieve target outcomes.

Consider the example of the T2D patient looking to reduce their A1c level. A diabetes nurse and an endocrinologist will likely have the same goal of supporting T2D patients to reduce their A1c level. The target outcome they set for (or with) a patient is a function of their own and the patient’s capability to achieve that outcome for the goal. The endocrinologist’s target outcome may be high if they have more experience, knowledge and access to a wider set of resources, such as the latest drugs. On the contrary, a diabetes nurse may expect a much lower A1c reduction for their patients as traditional nutrition, exercise and counselling support and advice may be harder to adopt and less effective. Even for the same type of HCP, target outcomes also vary, e.g., from one endocrinologist to another.

Health payers, policymakers, and decision-makers also have health goals. Their goals are to allocate and optimise resources to achieve desirable health outcomes. They also have capabilities that are a function of the material and financial resources available to them.

From the above analysis of the diabetes care ecosystem, you can see that there are several units of analysis that can be used to identify and also quantify stakeholder problems and systems gaps. These inputs – variously goals, activities, resources, capabilities, motivations, emotions, desired experiences, and outcomes – can be mapped, linked, prioritised, ranked and compared across stakeholders. What’s more, they can be done so without any reference to current products and solutions – it is not necessary for people to have to interact with an artifact to discover their needs and experiences. Put simply, people do know what they want to be able to do and can achieve, and they can tell us what is preventing them from doing so.

Transform value … by redesigning problem inputs

When it comes to complex ecosystems, making design thinking like “leaps of imagination” to a valid solution is much harder to achieve just once, let alone repeatedly, for all the reasons I have described above. To increase the chances of success, however, I argue for greater separation between the activities of problem inputs, value definition and solution design in the early stages of innovation or the “mystery” part of the funnel as Martin puts it. Also, I argue for more rigour and discipline in problem evidence generation and interpretation. To improve validity, I identify four pre-solution “D” activities that design thinkers can pursue in ecosystem contexts. These are:

1. Define mysteries according to how stakeholders define their goals for improving their part or role in the system, not the company’s or the innovator’s definition.
2. Decouple solutions from problems and unmet needs when capturing insight into these stakeholder goals, and use structured methods for identifying their priorities, resources, capabilities and desired outcomes.
3. Determine how to reveal and link variations in problem inputs between and within stakeholders. This allows for the discovery of hitherto hidden opportunities within segments of stakeholders.
4. Design Value Frames that describe and define the opportunities for innovation and improvement that emerge from activities 2 and 3 (see below section on Value Frames).

By undertaking the above four activities before solution design, companies can add greater system, focus, and evidence to early-stage innovation whilst also extending the scope, creativity, value and fitness (to stakeholders) of new value propositions, market strategy, technologies, products, and services.

For design thinkers, doing the above will add further validity and credibility to their important role within organisations and will ensure their practices, perspectives and solutions are more widely regarded and supported by top management.

After all, which CEO doesn’t want more certainty, confidence, clarity, and evidence – leading to less innovation risk – and more value, better outcomes and increased quality for their customers and other stakeholders?

What is a Value Frame?

A Value Frame is a type of artifact we use at UMIO to explore opportunity (rather than solutions), to compare and evaluate different growth or revenue paths or options, to frame the generation of ideas and then to craft more detailed value propositions and ultimately strategy. They are loosely defined, thematic definitions of opportunity consisting of problem inputs / evidence, stakeholder need, systems gaps, constraints to overcome, contexts, paradoxes as well as measures of value potential that could be realised both commercially, for stakeholders and for the ecosystem overall.

Value Frames allow for further dialogue, collaboration and engagement around opportunities and value with both internal and external stakeholders. They may or may not contain specific solutions or ideas.

For further information on UMIO and our Value Design thinking in healthcare, please get in touch. We’d be delighted to hear from you.

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Chris Lawer is the CEO of UMIO. He helps companies to identify and define high-validity innovation and growth opportunities and then address them through the design of differentiated value-propositions and strategy. Chris has led dozens of such programmes in multiple industry and government sectors over the past 20 years, but particularly in healthcare and B2B. More from Chris on his blog and @chrislawer

Chris Lawer




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