The Ambition–Execution Gap in Industrial Data Ecosystems
A new Industrial Intelligence Report from AVEVA and IMD exposes a stark disconnect in how companies approach industrial data ecosystems. In interviews with more than 275 senior leaders across 12 sectors, 74% said digital ecosystems are a top strategic priority, yet only 27% actually share data substantially or extensively with ecosystem partners. This paradox sits at the heart of digital transformation barriers: leaders recognize that connected, data-driven collaboration is essential for resilience, innovation, and decarbonization, but cannot yet operationalize it at scale. Industrial intelligence—defined as the capability to integrate operational technology, information technology, and artificial intelligence—promises real-time, system-wide insight. However, the research shows that without robust data sharing strategies and ecosystem governance, this potential remains largely theoretical, leaving many organizations stuck in pilot mode rather than capturing enterprise-wide value.
Inside the Study: What 275+ Leaders Are Telling Us
The AVEVA–IMD study combines quantitative findings with deep qualitative interviews from ports, industrial hubs, and other complex operations. Leaders consistently point to digital ecosystems as the next frontier for industrial AI collaboration, especially for tackling higher-order problems like supply volatility and emissions reduction. Yet the adoption gap is clear: while executive vision is strong, execution lags in areas such as data interoperability, role clarity, and shared governance. Interviewees highlight that traditional, bilateral partnerships are ill-suited to the multi-party, platform-based ecosystems now required. Instead of isolated optimizations, organizations need connected networks that enable secure data flows across owners, operators, suppliers, and technology partners. The research frames ecosystem maturity not as a pure technology race, but as a multi-dimensional shift in strategy, operating model, and culture—explaining why progress remains uneven despite strong consensus on the direction of travel.
Why Data Stays Locked: Silos, Legacy Systems, and Weak Governance
If industrial data ecosystems are so strategically important, why is substantial data sharing still limited to just over a quarter of organizations? The report cites three interlocking constraints. First, integration complexity: heterogeneous OT environments and fragmented IT landscapes make it difficult to unify data in usable, trustworthy ways. Second, legacy systems: aging infrastructures often lack open interfaces, slowing efforts to build shared platforms for industrial AI collaboration. Third, weak governance: many firms have not yet defined clear rules on data ownership, access, quality standards, and accountability across partners. These digital transformation barriers reinforce one another, perpetuating data silos and discouraging ecosystem-scale initiatives. As a result, companies often resort to small, isolated projects rather than systemic collaboration, leaving significant efficiency, safety, and sustainability gains unrealized. Overcoming this requires deliberate investment in both technical foundations and cross-company governance models.
From Algorithms to Operating Models: Rethinking Ecosystem Strategy
The AVEVA–IMD findings argue that industrial leaders have over-focused on algorithms and under-invested in the conditions that make them valuable at ecosystem scale. Governance, integration, and organizational learning now matter more than the latest AI model. To unlock industrial intelligence, companies must design data sharing strategies that align incentives, clarify roles, and embed collaboration into core operating models. That includes defining which data is shared, under what rules, and how value is distributed among participants. Leadership also needs to shift thinking from one-off projects to enduring platforms that connect multiple parties in real time. This change demands a new skill set: ecosystem orchestration, not just internal optimization. As organizations make this pivot, they can move beyond incremental improvements toward strategic advantage built on jointly managed data assets and shared AI capabilities.
What Working Models Reveal About the Future of Connected Industries
Where industrial data ecosystems are more mature, the benefits are already visible. Case studies highlighted in the report show that when partners successfully integrate OT, IT, and AI on shared platforms, they gain end-to-end visibility, faster decision-making, and the ability to coordinate responses across entire value chains. Collaboration models, including partnerships between industrial software providers and automation specialists, demonstrate how standardized data layers, open architectures, and clear governance can overcome legacy constraints. These examples suggest a blueprint: start with concrete, cross-company use cases, align on governance and data contracts, and scale through interoperable platforms. As Michael Wade of IMD notes, long-standing operational collaborations are being transformed into real-time, intelligence-driven systems. The next wave of competitive differentiation will likely come not from stand-alone technologies, but from how effectively organizations participate in and orchestrate their industrial data ecosystems.