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Two recent publications outline a new model of innovation for transforming entire industries, cities, and regions:
Together, these publications outline a trajectory of ecosystemic innovation applicable to manufacturing, construction, services, transport, energy, research and innovation ecosystems. The model focuses on the subsystems—or ecosystems—of cities and regions and outlines a transformation trajectory consisting of five stages:
1. Sector selection: Select a clearly defined sector, preferably at the 2- or 3-digit NACE level, bringing together organisations with common production processes, technologies, needs, and challenges. Identify the sector’s main activities, value chains, and opportunities for innovation, productivity improvement, and sustainability.
2. Ecosystem formation: Build a large-scale ecosystem through multi-sided digital platforms and collaboration agreements. The ecosystem may be area-based, activity-based, or network-based, bringing together companies, technology providers, research organisations, public institutions, knowledge and innovation actors, consumers, and end users. Establish the organisational and digital connections that enable knowledge exchange, resource sharing, and collaborative innovation.
3. Mapping, Modelling, and Simulation of Ecosystem Routines: Map interactions, relationships, and data flows that sustain the major routines of production, exchange, and product and service development. Identify the actors, resources, technologies, and processes involved, and define the key performance indicators (KPIs) that characterise the ecosystem’s current state, capturing individual member performance, ecosystem-level outcomes, and externalities. Develop models of ecosystem activities and use simulation to explore alternative configurations, identify potential innovations, and assess how changes in routines, interactions, and resource allocation could drive the transition towards a new ecosystem state characterised by improved innovation, productivity, and sustainability, positive systemic effects and externalities.
4. Creation of an intelligent environment: Create an intelligent environment to improve the capabilities of ecosystem members through directed interactions among human intelligence (HI), collective intelligence (CI), and machine intelligence (MI). Establish real-life connectors that enable the six directed interactions (HI–CI, HI–MI, CI–HI, CI–MI, MI–HI, and MI–CI) through digital platforms, data streams, e-services, AI agents, expert knowledge, and inter-firm collaboration. Enable the recombination of these forms of intelligence into connected intelligence that supports problem identification, knowledge discovery, decision-making, and the development of innovative solutions.
5. Transformation of selected company routines: Activate the intelligent environment to transform selected company routines by (i) identifying appropriate methods and solutions for routine transformation; (ii) mobilising the knowledge, technologies, partnerships, and human, collective, and machine intelligence capabilities and resources available within the intelligent environment; and (iii) implementing the selected changes and assessing their outcomes. Measure the resulting improvements in innovation, productivity, and sustainability through changes in the defined KPIs, and document the transition from the ecosystem’s initial state to a new state characterised by higher performance, systemic effects, and externalities.
The model shifts attention from supporting individual innovation projects to creating intelligent ecosystems that can transform the routines underlying members’ activities and, through these changes, improve the performance of entire ecosystems, industries and territories.
At the core of the model is a transformation engine composed of directed binary couplings between human, collective, and machine intelligence (see the accompanying figure). The model therefore places capabilities and capability building at the centre of innovation drivers: they provide the knowledge, resources, coordination, and intelligence required to identify, design, and implement changes in activity routines.
In this model, capabilities are the core resources, and capability building becomes the main innovation policy. They enable ecosystem members to address the full range of challenges associated with transformation: identifying suitable solutions, mobilising funding, accessing and deploying research and technological knowledge, acquiring production capabilities, organising implementation, and developing the market conditions needed for new products, services, and processes to succeed.