Industrial AI Adoption Outpaces Real Ecosystem Collaboration
A new Industrial Intelligence Report from AVEVA and IMD Business School highlights a stark reality behind industrial AI adoption: strategy is racing ahead of execution. Based on more than 275 interviews with senior leaders across 12 sectors, the study finds that 74% of executives now rank digital ecosystems as a top strategic priority. Yet only 27% say they share data substantially or extensively with ecosystem partners. The research defines industrial intelligence as the capability to integrate operational technology, information technology and artificial intelligence to enable connected, data-driven decisions across entire industrial ecosystems. While many organizations are investing heavily in platforms and analytics, their digital ecosystem strategy remains largely inward-looking. Limited enterprise AI collaboration beyond organizational boundaries means that most companies are not yet capturing the full network effects or multi-party efficiencies that industrial AI promises.
The Execution Gap: Why Data Sharing Still Lags
The report exposes a deep execution gap between digital ecosystem strategy and day-to-day practice. Leaders frequently describe digital ecosystems as crucial to tackling higher-order challenges such as faster innovation, supply volatility and decarbonization. However, the same organizations struggle to operationalize that vision. Interviews and case studies point to integration complexity, entrenched legacy systems and weak governance as recurring data sharing barriers. Industrial firms have decades of experience coordinating operations with suppliers, customers and infrastructure partners, but most of this collaboration remains analog or siloed. Without robust frameworks for cross-company data access, standards, security and accountability, ecosystem projects stall at pilot stage. The findings suggest that simply deploying AI tools or cloud platforms is not enough. What is missing is the connective tissue—policy, architecture and incentives—that turns isolated digital projects into functioning, multi-party digital ecosystems.
From Algorithms to Architecture: Rethinking Digital Ecosystem Strategy
One of the clearest messages from the study is that digital ecosystem strategy must evolve beyond a narrow focus on algorithms. According to IMD’s Michael Wade, governance, integration and learning matter more at this stage than model sophistication. Industrial sectors are already seeing operational gains from industrial AI adoption, but turning these into enduring strategic advantage requires deliberate ecosystem design. That means clearly defined roles for partners, shared data standards and transparent rules for ownership, access and value sharing. AVEVA’s Caspar Herzberg argues that organizations need new leadership competencies and operating models that transcend internal silos. Rather than treating AI as a series of point solutions, leading firms are beginning to architect end-to-end data flows across plants, suppliers, logistics hubs and infrastructure operators—laying the groundwork for real-time, intelligence-driven networks.
Closing the Industrial AI Adoption–Execution Gap
The limited extent of data sharing revealed in the report underscores a crucial shift industrial leaders now face: from experimentation to ecosystem-scale execution. To close the adoption–execution gap, organizations must build practical frameworks for cross-organizational data collaboration. This includes multi-party governance models, interoperable architectures that can bridge legacy systems, and incentive structures that reward sharing insights rather than hoarding them. Successful examples in ports and industrial hubs show that when partners align around common outcomes—such as throughput, safety or emissions reduction—data sharing becomes a shared necessity, not a competitive risk. As digital ecosystems mature, companies that move fastest to orchestrate trusted, collaborative data environments are likely to capture outsized benefits from industrial AI, while laggards remain confined to isolated, subscale pilots that never deliver their promised impact.