Industrial Intelligence Rises, But Ecosystems Lag Behind
Industrial leaders agree that the future of competitiveness lies in connected, data-driven operations, yet most are still struggling to operationalise that vision. A new Industrial Intelligence Report from industrial software provider AVEVA and IMD Business School is based on more than 275 interviews with senior leaders across 12 sectors, combining quantitative findings with in‑depth case studies such as the Port of Rotterdam and Kwinana. The research frames industrial intelligence as the capability to integrate operational technology, information technology and artificial intelligence so that decisions can be made across entire digital ecosystems rather than within isolated plants or business units. Executives increasingly see these digital ecosystems as essential to tackle complex challenges, from faster innovation cycles to supply volatility and decarbonisation. However, the report concludes that the step from pilots and isolated platforms to fully orchestrated industrial AI adoption at ecosystem scale remains incomplete for most organisations.
The 74–27 Gap: Intentions Outpace Data Sharing Reality
The headline finding from the AVEVA–IMD study is stark: while 74% of surveyed leaders rank digital ecosystems as a top strategic priority, only 27% say they share data substantially or extensively with ecosystem partners. This disconnect exposes a major bottleneck for industrial AI adoption, which depends on large, diverse, and timely data flows across organisational boundaries. Many businesses have articulated ecosystem strategies and invested in platforms, yet partners still access only narrow slices of operational information. The report’s case studies highlight recurring data sharing barriers, including concerns over competitive exposure, unclear value‑sharing models, and a lack of shared standards. As a result, AI and analytics tend to optimise individual assets or sites, rather than unlocking network‑wide industrial intelligence that spans suppliers, logistics providers, operators, and customers.
Data Silos, Legacy Systems and Fragmented Ecosystems
Beneath the numbers lies a familiar set of structural obstacles. Data silos remain entrenched because critical information is locked in legacy systems designed long before connected digital ecosystems were envisioned. Integration projects are complex, slow, and often under‑resourced, limiting the ability to expose reliable data feeds to partners. The report underlines governance weaknesses as another major friction point: many organisations lack clear policies on who owns which data, how it can be used, and how risks are allocated. This governance gap fuels mistrust and discourages extensive sharing, particularly where safety or regulatory exposure is high. Meanwhile, ecosystem fragmentation persists as multiple platforms, formats, and interfaces coexist, each with partial coverage. Together, these issues prevent companies from turning operational collaborations into real‑time, AI‑ready networks capable of delivering scalable value from industrial intelligence.
From AI Experiments to Industrial Intelligence at Scale
The AVEVA–IMD report suggests that many organisations have moved past algorithm experimentation and are now wrestling with the harder work of ecosystem design. As IMD’s Michael Wade notes, governance, integration and learning matter more right now than algorithms themselves. To progress from isolated AI use cases to true industrial intelligence, companies must tackle trust and interoperability head‑on. That means defining shared governance frameworks, establishing transparent rules for data use, and agreeing on roles and responsibilities across the ecosystem. It also requires technology choices that favour open standards and modular integration over closed, proprietary stacks. Illustrative collaborations in the market, such as alliances between software and automation specialists, show how combining domain expertise with robust data platforms can help bridge AI integration and data capability gaps – but only when partners commit to systematic, governed data sharing.
Unlocking Collaborative AI: The Leadership Agenda
The path forward is less about new algorithms and more about leadership choices. AVEVA CEO Caspar Herzberg emphasises the need for frameworks, competencies and leadership practices that enable organisations to transcend silos and shift to ecosystem‑driven operating models. Senior teams must explicitly prioritise cross‑company data collaboration as a strategic capability, not a side project. That involves incentivising joint value creation, embedding data sharing requirements into contracts, and investing in shared integration layers that partners can access with confidence. At the same time, leaders must cultivate a culture of learning across organisational boundaries, using ecosystem pilots to refine governance and build trust. As digital ecosystems mature, industrial AI adoption will increasingly differentiate those who turn operational collaborations into live, intelligence‑driven systems from those still treating data as a guarded, internal asset.