Industrial AI Adoption: From Buzzword to Connected Intelligence
Industrial AI adoption is the process by which manufacturers and asset‑intensive enterprises connect operational technology, information technology, and artificial intelligence so data from machines, systems, and partners can support faster, more accurate, and safer decisions across design, production, supply chains, and service. The first Industrial Intelligence Report from AVEVA and IMD defines this broader shift as "industrial intelligence", an organisational capability that integrates OT, IT, and AI to enable connected, data‑driven decision‑making across entire industrial ecosystems. Based on more than 275 interviews with senior leaders across 12 sectors, the research focuses less on algorithms and more on how AI fits into digital ecosystems. These ecosystems link factories, ports, suppliers, and operators into shared data environments where insights move as quickly as materials. For many executives, AI is no longer an experiment but a strategic tool that must plug into this wider, connected landscape.
Leaders Prioritise Ecosystems, But Data Sharing Lags Behind
The Industrial Intelligence Report reveals a striking mismatch between strategic intent and day‑to‑day execution. While 74% of leaders see digital ecosystems as a top strategic priority, only 27% report sharing data substantially or extensively with ecosystem partners. This gap has direct implications for manufacturing AI challenges: without shared, high‑quality data, complex models cannot learn, scale, or deliver reliable decisions beyond a single site. Executives interviewed from ports and industrial hubs describe a vision where maintenance data, energy usage, logistics flows, and supply information feed a common industrial AI backbone. Yet integrating OT and IT systems across companies is slow, and many partners remain cautious about openness. For now, industrial AI adoption tends to focus on contained pilots, where a single organisation controls the data. The long‑promised value of cross‑company optimisation remains more aspiration than reality.
Where Industrial AI is Expected to Deliver ROI First
When senior leaders talk about enterprise AI implementation, they link success to clear, near‑term returns. The Industrial Intelligence Report points to industrial AI use cases that sit naturally within digital ecosystems: predictive maintenance on connected assets, real‑time optimisation of energy and materials, and end‑to‑end visibility for logistics and ports. These applications depend on continuous OT data streams and shared context, which industrial platforms can provide once connections are in place. Leaders also highlight AI‑assisted decision support for control room operators, where integrated IT and OT data can cut response times and reduce downtime. However, the report notes that ecosystems must move beyond isolated wins to deliver compound value across partners. That means aligning KPIs, deciding which insights are shared, and agreeing on standards for data quality. Without this foundation, AI tends to optimise silos rather than full industrial value chains.
Organisational and Technical Barriers Slow AI at Scale
The Industrial Intelligence Report makes clear that manufacturing AI challenges are as much organisational as technical. Case studies show how integration complexity, legacy systems, and weak governance block progress when companies try to scale pilots into enterprise AI implementation. Many plants still rely on heterogeneous control systems and fragmented data historians, which makes basic connectivity difficult. On the organisational side, unclear ownership for data, OT–IT turf battles, and limited AI literacy delay decisions. AVEVA’s CEO Caspar Herzberg stresses that "governance, integration and learning matter more right now than algorithms", underscoring that leadership, not model choice, often determines success. Michael Wade of IMD adds that data, AI, and connected platforms are turning traditional industrial collaboration into real‑time, intelligence‑driven systems. The leaders interviewed know this shift is underway, but closing the gap between vision and factory‑floor reality will demand disciplined governance and long‑term investment.