Generative AI, industrial data, and decision support for complex operations.
Heavy industry is entering a new phase of digital change. Traditional automation, control systems, historians, dashboards, and enterprise software are being joined by a new generation of AI capabilities — including generative AI, knowledge assistants, AI agents, and intelligent decision-support systems.
This page explores how AI can be applied in mining, oil and gas, chemicals, energy, manufacturing, and other industrial environments where safety, reliability, operational context, and engineering judgement matter.
The focus is practical: how AI can help industrial organisations make better use of their knowledge, data, people, and systems — without losing sight of the realities of running complex operations.
AI in heavy industry is not just about adding chatbots or applying generic machine learning models.
Industrial organisations already have layers of technology: control systems, PLCs, SCADA, DCS, historians, laboratory systems, maintenance systems, ERP platforms, engineering documents, spreadsheets, reports, operating procedures, and years of hard-won operational knowledge.
The opportunity is to use AI to connect, interpret, retrieve, summarise, explain, and support decisions across this complex landscape.
That includes generative AI and large language models, but also related technologies such as retrieval-augmented generation, knowledge graphs, AI agents, analytics, predictive maintenance, anomaly detection, and decision support.
The question we should be asking now is:
“Where can AI safely and usefully improve how industrial people work, decide, learn, and respond?”
AI adoption in heavy industry has different constraints from AI adoption in office work or consumer applications.
Industrial environments involve:
This means AI must be introduced with care.
It is important to not try and replace engineering judgement or operational discipline. The goal is to augment and further empower people who already carry responsibility for complex systems.
For decades, industrial digitalisation has been shaped by a divide between operational technology and business technology.
On one side are automation and control systems: PLCs, DCS, SCADA, instrumentation, historians, alarms, and process control.
On the other side are business and enterprise systems: ERP, maintenance management, reporting, planning, finance, procurement, projects, and management dashboards.
AI creates a new layer between these worlds.
Used well, AI can help industrial organisations:
But this requires more than technology. It requires good problem framing, governance, domain expertise, trustworthy data, and a clear understanding of where AI should and should not be used.
Industrial organisations contain enormous amounts of knowledge hidden in documents, procedures, manuals, standards, reports, emails, spreadsheets, and individual experience. Generative AI can help people retrieve, summarise, compare, and apply this knowledge — if it is implemented with proper context, validation, and governance. Topics include: industrial knowledge assistants, retrieval-augmented generation, document intelligence, engineering and operations copilots, procedure and standards retrieval, and reducing the loss of institutional knowledge.
AI agents are beginning to move beyond simple prompting into structured workflows. In industrial settings, this could support engineering analysis, maintenance planning, reporting, procurement support, project documentation, and operational decision support. But industrial agents need constraints, oversight, auditability, and integration with real systems. Topics include: industrial AI agents, workflow automation, human-in-the-loop systems, agent reliability, task orchestration, and safe integration with operations and business processes.
The most valuable AI applications may emerge at the intersection of operational technology, enterprise IT, and engineering knowledge. This includes connecting process data, maintenance data, engineering documents, operating procedures, and business context into more useful decision-support systems. Topics include: OT/IT convergence, industrial data architecture, historians and contextual data, knowledge graphs, data governance, and practical integration between plant systems and business systems.
Industrial AI should be linked to business value, operational risk, and implementation capability. Executives and project teams need to know where to start, what to avoid, how to prioritise use cases, and how to build internal capability without being distracted by hype. Topics include: industrial AI strategy, use case selection, business case development, governance and risk, vendor evaluation, AI readiness, and implementation roadmaps.
This page is written for people working at the intersection of industry, technology, engineering, and business.
It is especially relevant for:
We face a common challenge:
How do we use AI in a way that is useful, trustworthy, and practical in real industrial environments?
I share my learnings about AI in heavy industry from the perspective of an engineer, software business founder, former CIO, and industrial technology consultant.
My background includes process engineering, industrial software, IT leadership, product strategy, and business development. I have worked with organisations serving demanding industrial sectors including mining, oil and gas, and heavy industry.
That experience has shaped my view of AI.
Industrial AI cannot be treated as a generic software trend. It has to be understood in the context of real operations, existing systems, engineering judgement, business priorities, safety, reliability, and organisational capability.
My interest is in practical AI adoption: where AI can help, where it is immature, where the risks are, and how industrial organisations can move forward responsibly.
If you are new to this topic, start with these articles:
I work with selected industrial clients, technology vendors, and project teams on practical questions around AI, industrial software, digital strategy, and the use of technology in heavy industry.
Typical questions include where generative AI can create value, how AI fits between OT and IT systems, which use cases are realistic, how to evaluate vendors, and how to build a practical roadmap for adoption.
