Digital Ecosystems: A Strategic Priority with Limited Follow-Through
Digital ecosystems have moved to the center of industrial strategy. According to the inaugural Industrial Intelligence Report from AVEVA and IMD Business School, 74% of senior industrial leaders now view digital ecosystems as a top strategic priority. These ecosystems are envisioned as connected networks of partners, assets and platforms that enable organizations to respond faster to market volatility, accelerate innovation and support decarbonization efforts across complex operations. Yet, despite this strategic emphasis, actual enterprise data collaboration remains tentative. Only 27% of surveyed leaders report sharing data substantially or extensively with ecosystem partners, highlighting a stark execution gap. The findings, based on more than 275 interviews across 12 industry sectors, suggest that most organizations are still in the early stages of turning ecosystem vision into operational reality, with digital ambition outpacing the practical mechanisms needed for trusted data exchange.
Inside Industrial Intelligence: Connecting OT, IT and AI
At the heart of industrial digital ecosystems lies what AVEVA and IMD describe as “industrial intelligence”: an organizational capability that integrates operational technology (OT), information technology (IT) and artificial intelligence (AI). The goal is to enable connected, data-driven decision-making not just within a single plant or site, but across entire industrial ecosystems that span suppliers, operators, ports and industrial clusters. When this capability is in place, organizations can turn real-time operational data into shared insight, powering industrial AI integration for predictive maintenance, process optimization and more resilient supply chains. Case examples cited in the report, including the Port of Rotterdam and the Kwinana industrial area, show how cross-enterprise visibility can unlock tangible value. However, these successes remain the exception rather than the norm. For many companies, industrial intelligence is still constrained by fragmented data landscapes and limited interoperability between legacy systems and modern platforms.
Why Data Sharing Stalls: From Legacy Systems to Governance Gaps
The research highlights several persistent data sharing barriers that keep industrial ecosystems from reaching their potential. Integration complexity is a primary obstacle: many organizations struggle to connect heterogeneous OT systems, aging infrastructure and newer cloud-based applications into a coherent data fabric. Legacy systems often lack standard interfaces, making secure, real-time data exchange with partners technically demanding and costly. Equally significant are governance weaknesses. Clear rules about who owns which data, how it can be used and how risks are shared are often missing or underdeveloped. This uncertainty reinforces siloed behaviors, as leaders fear exposing sensitive operational information or losing competitive advantage. Even when the technology exists, organizations may hesitate to move beyond pilot projects. As IMD’s Michael Wade notes, governance, integration and organizational learning currently matter more than sophisticated algorithms in determining whether ecosystem initiatives deliver sustained strategic value.
Balancing Collaboration, Security and Competitive Advantage
The AVEVA–IMD report argues that industrial sectors must develop robust frameworks to balance data sharing with security and competitive concerns. Companies need clear models for defining roles, responsibilities and value-sharing arrangements across their digital ecosystems. This includes establishing standardized data contracts, access controls and audit mechanisms so partners can collaborate without compromising intellectual property or operational security. Leadership practices also need to evolve. Ecosystem orchestrators must foster trust, encourage transparency around risks and benefits, and prioritize coordination over unilateral control. AVEVA’s Caspar Herzberg emphasizes that the next phase of ecosystem maturity is about converting existing operational value into strategic advantage through better data sharing and more deliberate leadership. As industrial AI integration and connected platforms advance, organizations that successfully transcend silos and embed strong governance will be best positioned to turn real-time data flows into shared intelligence and long-term competitiveness.