Ambition Outpaces Execution in Digital Ecosystem Adoption
Industrial executives overwhelmingly agree that digital ecosystems are central to the future of connected industries. According to the inaugural Industrial Intelligence Report from AVEVA and IMD Business School, 74% of leaders now view digital ecosystems as a top strategic priority, reflecting growing pressure to innovate faster, manage supply volatility and advance decarbonisation. The study, based on more than 275 senior leaders across 12 sectors, highlights a strong strategic consensus: competitive advantage will increasingly depend on how effectively organisations participate in broader, data‑driven networks rather than operating as isolated entities. Yet beneath this headline enthusiasm lies a more complicated reality. While industrial AI implementation and connected platforms are high on boardroom agendas, many companies still struggle to translate vision into concrete ecosystem practices. The result is a widening disconnect between digital transformation goals and the day‑to‑day operations of plants, ports and infrastructure assets.
Industrial Intelligence: The Technical Core of Connected Ecosystems
The report frames “industrial intelligence” as the capability that makes digital ecosystems workable in practice. It combines operational technology (OT), information technology (IT) and artificial intelligence (AI) to enable connected, data‑driven decision‑making across entire industrial value chains. In theory, this convergence should accelerate industrial AI implementation, improve predictive maintenance, and support real‑time optimisation from plant floor to supply chain partner. In practice, fragmented systems and legacy infrastructure limit how far data can actually flow. Many organisations still run critical assets on older platforms that were never designed for cross‑company connectivity, creating structural data silos. AVEVA and IMD argue that industrial intelligence is not simply a technology stack but an organisational capability: companies must integrate governance, architecture and skills at scale before they can reliably participate in digital ecosystems or derive strategic value from their data assets.
The Data Sharing Gap: Only 27% Move Beyond Minimal Collaboration
Despite the strategic emphasis on digital ecosystem adoption, only 27% of surveyed leaders say they share data substantially or extensively with ecosystem partners. This stark contrast with the 74% citing ecosystems as a priority exposes a serious execution gap in industrial data sharing. Many organisations still treat data as something to be guarded rather than exchanged, even where collaboration could unlock mutual benefits such as better demand forecasting, reduced downtime or shared sustainability insights. Data collaboration barriers appear at multiple layers: incompatible systems, uncertain ownership, and the absence of clear frameworks for how information is used and protected. The result is that ecosystem relationships remain transactional and project‑based instead of becoming integrated, intelligence‑driven systems. Without a shift towards more systematic sharing, digital ecosystems risk remaining PowerPoint concepts rather than operational realities.
Data Silos, Trust Deficits and Governance Gaps
Interviews highlighted three recurring obstacles to ecosystem‑level data collaboration: integration complexity, legacy systems and weak governance. Technically, many organisations wrestle with connecting OT assets, IT systems and cloud platforms, especially when partners use different standards or proprietary tools. This fragmentation reinforces internal and external data silos, making it difficult to build a unified view of operations across companies. Just as significant are trust and governance concerns. Without clear rules on data ownership, access rights and permissible uses, partners hesitate to expose sensitive operational information. Leaders cited worries about competitive disadvantage, security risks and regulatory exposure as reasons to limit data flows. AVEVA and IMD’s findings suggest that governance, integration and continuous learning currently matter more than advanced algorithms: industrial AI can only generate ecosystem‑level value when organisations are confident that data sharing is structured, fair and secure.
Closing the Gap Between Digital Strategy and Industrial Reality
The report argues that industrial sectors already know how to collaborate out of operational necessity; what is new is the shift to real‑time, data‑driven collaboration enabled by AI and connected platforms. To close the gap between ambition and execution, leaders must treat data sharing as a designed capability, not an afterthought. That means aligning corporate strategy with ecosystem roles, investing in interoperable architectures, and strengthening governance models that clarify responsibilities and risks. Clearer partner agreements, standardised interfaces and joint innovation programmes can all help overcome data collaboration barriers. Leadership behaviour is equally critical: executives need to champion transparency, reward cross‑company learning and measure progress not only by technology deployments but by the depth of information exchange. Organisations that convert today’s operational pilots into robust, ecosystem‑wide data practices are likely to gain a durable strategic edge.