Industrial AI incorporates systems, data, architecture, integration, governance, and a clear understanding of how technology is applied to support industrial operations.
This section brings together articles on IT in manufacturing, industrial platforms, legacy systems, automation strategy, digital transformation, data architecture, and the practical realities of connecting operational technology with business systems.
Many of these articles predate the current generative AI wave, but the themes remain highly relevant. AI in heavy industry will only create value where the digital foundations are strong enough to support it.
My current focus is shifting toward AI in Heavy Industry — especially generative AI, industrial knowledge systems, AI agents, and decision support.
But industrial AI does not exist in isolation.
Before an organisation can use AI effectively, it needs to understand its existing technology landscape:
These remain essential topics that determine whether AI becomes useful infrastructure or another disconnected experiment.
For many years, industrial digital transformation focused on connecting systems, improving visibility, standardising data, modernising platforms, and aligning IT with operations.
Those challenges have not disappeared.
Generative AI has added a new layer of possibility, but it has also made the old problems more visible. Poor data quality, fragmented systems, unclear ownership, weak governance, and legacy software constraints all limit what AI can achieve.
Industrial organisations that want to benefit from AI need to move beyond experimenting with tools and implement a holistic a digital strategy that connects:
Industrial organisations generate large volumes of data, but value depends on context, structure, quality, accessibility, and governance. Topics include: unified namespace concepts, contextualised data, industrial platforms, data ownership, reporting, analytics, and the foundations needed for AI-ready data.
Heavy industry depends on long-lived assets and long-lived software. Modernisation is rarely simple. Topics include: legacy application strategy, software replacement, technical debt, risk management, platform selection, and pragmatic modernisation.
How operational technology and enterprise IT come together — and why the boundary between plant systems and business systems is central to industrial digital strategy. Topics include: plant data, historians, automation systems, enterprise integration, reporting, and the organisational divide between IT and operations.
Industrial digital initiatives need to connect technology decisions to business outcomes. Topics include: digital industrial platforms, IT audits, business value, technology roadmaps, operational excellence, and aligning digital investments with organisational priorities.
This section is mainly for people responsible for the practical digital foundations of industrial organisations.
It is relevant for:
While the AI in Heavy Industry section looks toward what is emerging, this section looks at the foundational systems and decisions that make that future possible.
Many of the articles in this section were originally written for industrial audiences, including readers of SA Instrumentation and Control. They reflect a long-standing interest in how IT can create value in manufacturing and heavy industry.
Some references may belong to an earlier phase of industrial digitalisation, but the core issues remain relevant:
These questions are even more important now that AI is becoming part of the industrial technology landscape.
If you are new to this section, these articles provide useful entry points into the digital foundations of industrial AI.
My recent focus builds on these digital strategy foundations and increasingly on AI in heavy industry — especially generative AI, industrial knowledge systems, AI agents, and decision support.
If you are interested in how AI will affect industrial operations, engineering, IT/OT, project development, and business decision-making, start here:
I work with selected industrial clients, software vendors, and project teams on questions involving industrial digital strategy, software products, AI adoption, and the practical use of technology in heavy industry.
Typical questions include:
