Innovation ecosystems thrive when they can identify opportunities that align with their existing capabilities and build on their strengths. To discover these opportunities, it’s crucial to look beyond individual capabilities and consider how they relate to one another. By using relatedness and complexity metrics, ecosystems can pinpoint where the greatest potential lies for innovation and growth. This approach allows stakeholders to strategically invest in areas that offer not only economic rewards but also long-term resilience in an ever-evolving global landscape.

What is an innovation opportunity?

Well-functioning innovation ecosystems tend to diversify incrementally, moving into activities that have skills similar to the ones they currently possess. This structural transformation is a path-dependent process. However, there is room for agency. Due to related diversification, economies with similar capabilities can specialize in different areas. In this context, innovation opportunities are all the fields of innovation (scientific, technological or productive) that are highly related to the current strengths of an innovation ecosystem.

To better guide innovation policymaking, economists increasingly recommend using the complexity and relatedness framework as a valuable tool, especially for smart specialization policies. By combining these metrics, policymakers can assess which opportunities are the most attainable (based on relatedness), and how valuable each of them is in terms of complexity (or other desired indicators).  In essence, high relatedness points towards fields where the current know-how of an ecosystem is compatible to a certain field, hinting that the risk of pursuing that capability is relatively lower than lowly related fields.

How to prioritize between innovation opportunities

The mapping of innovation opportunities allows policymakers to grasp the levels of risk and rewards that each capability represents, and follow the paths that better suit the needs of the ecosystems they represent. Within the spectrum of possible strategies, there are two that are worth noting.

Maximum relatedness strategies

Some ecosystems choose to focus on related skills, building on their existing knowledge and resources to reduce the risks of entering new domains. While this approach leverages established expertise, it can pose challenges, especially for less developed ecosystems. Many of their opportunities are concentrated at the lower end of the complexity spectrum, and following this path too rigorously may trap them in lower-value capabilities. This limits their potential for growth and could worsen income inequalities globally.

Minimum time strategies

Others may prefer to take leaps into less related, complex techonologies to skip certain stages of traditional economic development and move directly to more advanced or modern stages. This move can open new opportunities if successful, but it is risky: targeting complex capabilities too early may lead to failure and wasted resources.

finding the right balance

Between these two extremes, there are numerous strategies that ecosystems can adopt, balancing the trade-offs between risk, complexity, and time. Ecosystems can pursue incremental diversification by exploring moderately related industries or technologies, ensuring a manageable level of risk while gradually expanding their capabilities. They may also implement hybrid approaches, combining short-term moves into related domains with long-term investments in more ambitious, complex opportunities. The key lies in tailoring the strategy to the ecosystem’s current strengths, development goals, and available resources, ensuring a sustainable and resilient growth trajectory.

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