Strategy Outpaces Reality in Industrial Digital Ecosystems
Industrial companies are racing to build industrial digital ecosystems, but their strategic intent is outrunning execution. According to the inaugural Industrial Intelligence Report from AVEVA and IMD Business School, 74% of senior leaders across 12 sectors see digital ecosystems as a top strategic priority. Yet only 27% say they share data substantially or extensively with ecosystem partners, revealing a stark disconnect between vision and practice. The report, based on more than 275 executive interviews and case studies from complex hubs such as major ports and industrial precincts, argues that industrial intelligence—integrating OT, IT and AI—is the foundation for connected, data-driven decision-making. However, without meaningful enterprise data collaboration beyond company boundaries, even advanced industrial AI adoption risks being confined to isolated pilots, rather than transforming value chains and shared operations at scale.
Why Data Sharing Remains the Critical Bottleneck
The report identifies data sharing barriers as the single most persistent bottleneck limiting the effectiveness of industrial AI adoption. Executives acknowledge that ecosystems are essential to address higher-order challenges such as faster innovation, supply volatility and decarbonization. But when it comes to opening their data, organizations stall. Integration complexity makes it difficult to connect disparate systems across partners, while legacy technologies constrain how data can be standardized or exposed. Weak governance leaves unresolved questions over data ownership, liability and access rights, reinforcing a risk-averse mindset. As a result, many companies keep valuable operational data locked in silos, limiting AI models to narrow, internal datasets. This undermines the potential of industrial digital ecosystems, where the real advantage lies in spanning organizational boundaries to orchestrate shared intelligence, not just optimize isolated plants or assets.
Organizational and Technical Barriers to Enterprise Collaboration
Beyond technology, the Industrial Intelligence Report highlights deep organizational frictions that impede enterprise data collaboration. Corporate strategies often proclaim ecosystem ambitions, but operating models remain optimized for bilateral supplier relationships rather than multi-party platforms. Decision rights and responsibilities around shared data are rarely defined, making cross-company initiatives slow and politically sensitive. Technically, fragmented OT and IT landscapes, accumulated over decades, complicate integration and undermine trust in data quality. AI capabilities are frequently developed in pockets without clear pathways to production across partners. AVEVA and IMD’s research underscores that governance, integration and learning currently matter more than sophisticated algorithms. Until organizations invest in interoperable architectures, shared standards and joint governance forums, industrial digital ecosystems will continue to deliver only partial value, and collaborative AI use cases will struggle to progress beyond proofs of concept.
From Operational Collaboration to Real-Time Intelligence-Driven Ecosystems
Industrial sectors are not new to collaboration; they have long coordinated around safety, logistics and shared infrastructure out of operational necessity. The shift described in the report is from ad hoc cooperation to real-time, intelligence-driven systems powered by data, AI and connected platforms. Where ecosystems are working, companies are already capturing tangible operational value, such as smoother flows across ports or industrial zones. The next frontier, according to AVEVA and IMD, is converting that foundation into enduring strategic advantage. That means clearer ecosystem roles, codified data-sharing agreements, and leadership that treats data as a shared asset rather than a guarded commodity. As firms refine their industrial intelligence capabilities, those that systematically reduce data sharing barriers and embed collaboration into governance and technology choices are most likely to unlock the full promise of industrial AI adoption.