From Hype to Industrial Intelligence: How Leaders Really View AI
Industrial AI adoption is shifting from isolated pilots to a broader quest for what AVEVA and IMD call “industrial intelligence.” Based on more than 275 interviews with senior leaders across 12 sectors, their new Industrial Intelligence Report defines this capability as the integration of operational technology (OT), information technology (IT), and artificial intelligence (AI) to enable connected, data‑driven decisions across an entire industrial ecosystem. Rather than treating AI as a bolt‑on tool, respondents increasingly see it as the connective tissue linking factories, assets, partners, and supply chains. The goal: create continuous manufacturing intelligence that optimizes throughput, reduces downtime, and supports decarbonization. Yet the research also underscores that intelligence is not just about algorithms. It involves new operating models, shared data platforms, and ecosystem collaboration that extends well beyond individual plants or production lines.
Digital Ecosystems Are a Top Priority—But Data Sharing Lags
The report’s most striking finding is the gap between strategic intent and operational reality. While 74% of leaders rank digital ecosystems as a top strategic priority, only 27% say they share data substantially or extensively with ecosystem partners. This mismatch explains why many enterprise AI deployment efforts stall after successful proofs of concept. Manufacturers and operators want connected value chains, but struggle to link multiple sites, suppliers, ports, or infrastructure operators into coherent industrial workflows. Case studies such as the Port of Rotterdam and Kwinana highlight how complex integration, heterogeneous legacy systems, and fragmented governance can dilute the benefits of advanced analytics. Leaders are discovering that industrial AI adoption is constrained less by model performance and more by the legal, technical, and organizational frameworks required to share data responsibly and reliably at scale.
Governance, Integration and Learning Now Matter More Than Algorithms
AVEVA CEO Caspar Herzberg and IMD Professor Michael Wade argue that the next wave of manufacturing intelligence will be won through governance and integration, not just better code. The research shows that many organizations already derive operational value from AI—optimizing production schedules, improving energy efficiency, or enhancing maintenance. Yet to convert these wins into strategic advantage, they must tackle the unglamorous work of aligning roles, responsibilities, and data policies across their ecosystems. Wade notes that industrial sectors have long histories of collaboration born from operational necessity. Today, data, AI, and connected platforms are transforming those relationships into real‑time, intelligence‑driven systems. This requires investment in cross‑functional learning, shared standards, and robust data stewardship so that AI insights can flow seamlessly from shop floor to boardroom and across partners without compromising trust or safety.
From Pilots to Mission‑Critical AI in Real‑World Operations
The report indicates that industrial AI is steadily moving from experimental pilots toward mission‑critical deployment in core operations. Leaders are focusing on use cases where AI is tightly embedded into industrial workflows—supporting asset performance management, autonomous process control, and dynamic supply‑demand balancing. Collaborations between software providers and automation specialists, such as those emerging around AVEVA’s ecosystem, show how combining domain expertise with scalable data platforms can accelerate this shift. Practical integration approaches emphasize open architectures that can ingest OT and IT data, then expose AI‑generated insights back into control systems and operator interfaces. As firms refine governance and integration, AI becomes less of a parallel analytics layer and more of an operational backbone. The direction of travel is clear: industrial intelligence is evolving from isolated dashboards into a shared, ecosystem‑wide capability that underpins everyday decision‑making.