Digital Ecosystems Rise to the Top of the Industrial Agenda
Digital ecosystems have firmly entered the boardroom agenda, with 74% of industrial leaders ranking them as a top strategic priority. That is the headline finding from the inaugural Industrial Intelligence Report, launched by industrial software company AVEVA together with IMD Business School. Drawing on more than 275 interviews with senior leaders across 12 sectors, the research shows that companies see connected platforms as critical to tackling complex challenges such as faster innovation, supply volatility, and decarbonization. The report introduces the concept of industrial intelligence: an organizational capability that fuses operational technology, information technology, and artificial intelligence into a connected decision-making fabric across entire ecosystems. In practice, this means moving beyond isolated digital projects to shared, data-driven operations that span plants, partners, and value chains. Yet the same report also reveals that enthusiasm for digital ecosystems far outpaces their real-world implementation.
The Data-Sharing Gap Undermining Industrial AI Adoption
Despite the strategic emphasis on digital ecosystems, only 27% of leaders say they share data substantially or extensively with ecosystem partners. This stark contrast highlights a critical disconnect in industrial AI adoption: organizations recognize the value of collaboration, but hesitate when it comes to operationalizing data sharing. Without robust data flows across partners, advanced analytics and AI applications cannot achieve their full potential, limiting insights to siloed operations. The report’s case studies—from ports to industrial hubs—underline how limited interoperability and fragmented data architectures slow progress. Even where digital platforms exist, inconsistent standards and uneven partner maturity restrict ecosystem-wide intelligence. In effect, companies are building the outlines of digital ecosystem strategy but feeding these ecosystems with partial, delayed, or isolated data. This undermines the reliability, scalability, and trust required for shared AI-driven decision-making.
Integration, Legacy Systems and Governance: Core AI Implementation Barriers
The gap between ambition and execution is not only cultural; it is deeply structural. The Industrial Intelligence Report identifies three recurring AI implementation barriers: integration complexity, legacy systems, and weak governance. Many industrial organizations run decades-old operational technology alongside newer IT and cloud platforms, making end-to-end integration technically demanding and costly. Legacy systems often lack modern interfaces, obstructing the seamless data flows that AI models require. At the same time, governance frameworks frequently lag behind technology deployments. Questions around data ownership, access rights, and accountability remain unresolved, which discourages organizations from opening up their systems to partners. According to IMD’s Michael Wade, governance, integration, and learning currently matter more than algorithms themselves. Without clear rules, robust architectures, and continuous capability-building, even sophisticated AI tools cannot be fully embedded into ecosystem-wide operations.
From Operational Collaboration to Real-Time Intelligence-Driven Ecosystems
Industrial sectors are no strangers to collaboration; they have long coordinated logistics, safety, and supply chains out of operational necessity. What is changing, as the report stresses, is that data, AI, and connected platforms are transforming these relationships into real-time, intelligence-driven systems. Where digital ecosystems are working, organizations already see tangible value in more responsive operations and better cross-partner coordination. The next phase is to elevate this operational value into true strategic advantage. AVEVA CEO Caspar Herzberg argues that this will depend on new frameworks, competencies, and leadership practices that help companies transcend internal silos and orchestrate ecosystem-driven operating models. That means deliberate choices about data sharing, clearer role definitions among partners, and leadership that treats learning and governance as continuous disciplines. Closing the execution gap will define which industrial players can turn digital ecosystem strategy into durable competitive differentiation.