Digital transformation in asset-heavy industries
Why digital transformation looks different in asset-heavy industries
I recently took part in a roundtable about artificial intelligence, attended by executives from digital-native and consumer-facing companies across the Netherlands. As we compared notes about the progress and pace of digitization, one observation came up more than once: digital transformation in asset-heavy industries can look slow compared to some sectors.
It wasn’t meant as a criticism, but it stuck with me because it assumes the wrong benchmark. Industrial innovation runs at two speeds. ‘Workflow-touching’ innovation, where digital tools reshape planning, monitoring or decision-making, can move quickly. ‘Machine-touching’ innovation, by contrast, where software interacts directly with physical assets, carries significant operational and safety risk. What might look slow to outsiders is in fact responsible innovation.
In asset-heavy environments, speed alone is not the answer. A failed industrial control or monitoring algorithm can trigger shutdowns, safety incidents and cascading operational losses. That’s why digital transformation in these industries must follow a different path, one that is more integrated, more cautious and designed for the long term.
Once you start looking through that lens, the pace makes more sense.
How industrial innovation works
Investment in industrial digitalization is projected to grow from around $116 billion in 2024 to nearly $674 billion by 2032. Asset-heavy companies are clearly aware of the opportunity. So, I think it’s clear that this isn’t about a lack of ambition.
The difference is in how that opportunity is pursued. In a software-driven world, digital systems may be replaced every few years, while industrial assets are expected to run for decades.
Therefore, digital investment must focus on solutions that are secure, interoperable and maintainable over long lifecycles, becoming part of the asset itself rather than something bolted on. That’s why industrial digital transformation often looks evolutionary rather than disruptive.
Risk plays a vital role in shaping that evolution. It isn’t a barrier to innovation; it is the architecture. High-risk, machine-level changes move slowly and are validated rigorously. Lower-risk workflow innovations can move much faster.
For instance, changing how a control system governs a turbine demands extensive testing and sign-off. Improving how maintenance teams prioritize inspections or respond to alerts can be trialed and refined more iteratively. Where software environments might reward speed and novelty, ROI here is measured in reduced uncertainty. In other words, being ‘right’ matters more than being ‘fast’.
What this looks like in practice
This risk-tiered approach becomes particularly clear in environments such as offshore oil and gas production, which takes place in some of the world’s most complex and hostile conditions. Digital transformation here requires deep expertise to improve reliability and decision-making in places where risk tolerance is understandably low.
The same principles apply in our own work. For example, Baker Hughes worked with QAFCO to support their Operational Excellence Framework and enable real-time process optimization and predictive maintenance across QAFCO's ammonia plants, resulting in a 0.8% increase in site-wide daily ammonia production as well as ~566 hours of production saved.
Digital insight is tightly coupled with service execution, ensuring recommendations translate into real operational change. That’s the distinction that I think is often missed.
Built to last
This is what meaningful digital progress looks like in asset-heavy industries. It is cumulative rather than explosive: fewer emergency call-outs, better forecasting, smoother workflows, higher uptime and lower emissions. Scaling what works across fleets is the industrial version of disruption — slower at first, but transformational.
In the long run, these industries may be better positioned than they appear. They bring decades of operational data, deep engineering knowledge, global service networks and high-impact use cases that digital-native players cannot easily replicate. As AI matures, those structural advantages compound.
This isn’t digital transformation done slowly. It’s digital transformation designed to last.