Industrial Intelligence Rises, but Data Sharing Lags
Industrial data ecosystems are moving to the top of executive agendas, yet collaboration remains constrained. That tension sits at the heart of the inaugural Industrial Intelligence Report on Digital Ecosystems and the Future of Connected Industries, launched by industrial software provider AVEVA and business school IMD at AVEVA World in Milan. Based on more than 275 interviews with senior leaders across 12 sectors, the study finds that 74% of respondents now regard digital ecosystems as a top strategic priority. However, only 27% say they share data substantially or extensively with partners in those ecosystems. The report defines industrial intelligence as the capability to integrate operational technology, information technology and artificial intelligence so that decisions are connected and data-driven across entire industrial networks. The headline numbers highlight a widening gap between board-level intentions and the reality on the ground inside plants, ports and complex value chains.
From Operational Collaboration to Real-Time, Data-Driven Systems
Industrial companies are not strangers to collaboration. Ports, energy hubs and manufacturing clusters have long coordinated operations out of necessity, as illustrated by case material from locations such as the Port of Rotterdam and Kwinana. What is changing, the report argues, is the role of data, AI and connected platforms in turning these long-standing relationships into real-time, intelligence-driven systems. Industrial data ecosystems are now expected to support faster innovation, resilience against supply disruptions and decarbonisation of complex operations. Yet the shift from exchanging paperwork or occasional data files to continuous cross-partner data sharing represents a profound operational change. It demands not only new technology but also shared processes, mutual trust and clear value propositions for every participant in the ecosystem, from asset owners and operators to service providers and software vendors.
The Hidden Digital Transformation Barriers
The AVEVA–IMD research highlights a cluster of digital transformation barriers that prevent industrial data ecosystems from reaching their potential. Leaders interviewed point to integration complexity as a first obstacle: heterogeneous equipment, proprietary protocols and fragmented data models make cross-partner data sharing technically arduous. Legacy systems further compound the challenge, limiting access to real-time data and constraining how information can be exposed beyond the enterprise perimeter. Weak or immature governance structures are another brake. Without clear rules on data ownership, usage rights, cybersecurity responsibilities and liability, organisations default to caution and keep their richest data sets siloed. These factors collectively explain why many companies invest in platforms, pilots and industrial AI adoption, yet struggle to scale into genuinely collaborative, multi-party digital ecosystems that deliver ecosystem-wide optimisation.
Why Governance and Leadership Matter More Than Algorithms
According to AVEVA and IMD, the issue is no longer whether algorithms are powerful enough, but whether organisations can orchestrate them across partners. IMD Professor Michael Wade argues that governance, integration and learning currently matter more than cutting-edge models. Many industrial firms are already generating operational value from data and analytics within their own boundaries. The next phase is converting that foundation into strategic advantage by improving cross-partner data sharing, clarifying roles in the ecosystem and strengthening coordination mechanisms. AVEVA CEO Caspar Herzberg frames the goal as building the frameworks, competencies and leadership practices required to transcend entrenched silos. That includes designing ecosystem operating models, setting shared performance metrics and cultivating leaders who can balance competitive sensitivities with the collective benefits of industrial data ecosystems.
Closing the Execution Gap in Industrial Data Ecosystems
The industrial intelligence report shows that the ambition–execution gap is wide but not insurmountable. Where ecosystems work, participants are already seeing tangible gains from joined-up data, industrial AI adoption and tighter operational synchronisation. To move more organisations into this category, the research implies three priorities. First, treat data governance as a joint strategic discipline, not an afterthought, with explicit agreements on access, security and value sharing. Second, tackle integration and legacy constraints systematically, using interoperable architectures and shared data standards to reduce friction. Third, invest in ecosystem learning: cross-partner pilots, shared playbooks and leadership development geared to collaborative industrial environments. Until these issues are addressed, the statistics are unlikely to shift significantly—and industrial data ecosystems will remain more of a strategic aspiration than an everyday operating reality.