Published: 12. January 2025,
For OEMs, AI in construction is not a distant concept — it’s the foundation for the next phase of growth. As Mikkel Dalgas, CTO at Trackunit, explains, early adopters are already using AI to make their manuals, diagnostics, and technical knowledge more accessible.
“We’re starting to see OEMs leverage AI to make expertise instantly available to customers — finding the right spare part, identifying an error, or troubleshooting before a breakdown ever happens.”
“AI allows OEMs to strengthen relationships and boost loyalty. Imagine a future where a virtual agent ships the right part before a human even picks up the phone.” — Mikkel Dalgas
The Deloitte 2025 Smart Manufacturing and Operations Survey found that 92% of manufacturers view smart manufacturing as the main driver of competitiveness over the next three years, and nearly a third already deploy AI or machine-learning at facility level. These findings underline how quickly data-driven operations are becoming central to OEM strategy.
This shift marks a fundamental change: manufacturers are moving from selling machines to delivering intelligence as a service.
The aftermarket journey — from maintenance to repair — is one of the richest areas for AI innovation. By connecting equipment data with customer support, OEMs can predict and resolve issues faster, reducing costs and elevating the ownership experience.
Dalgas notes that this isn’t just about technology. It’s about trust and accessibility. “When AI understands your machine and your language, it can provide support that feels truly personal – moving from static manuals to meaningful dialogue.”
“AI gives OEMs the ability to deliver expertise at scale — turning every service interaction into a moment of value, not frustration.” — Mikkel Dalgas
This customer-centric model transforms service from reactive troubleshooting into proactive value creation — a new way for OEMs to differentiate in a competitive landscape.
While cloud-based analytics have defined the last decade, the next leap for AI in construction lies in edge intelligence — embedding AI models directly inside telematics units and machine controllers.
Dalgas explains, “We’ll soon see small, purpose-built AI models running inside the machine itself. They’ll monitor performance, interpret data, and guide operators in real time.”
A McKinsey analysis on generative AI in maintenance found that applying intelligent systems to asset health and service operations can improve uptime by as much as 20–30 percent through faster issue detection and resolution. For OEMs, this kind of edge capability brings intelligence directly to the machine, minimizing latency and dependency on cloud connectivity.
“Edge AI will redefine how machines understand themselves — diagnosing faults, analyzing context, and helping operators take the right action before downtime happens.” — Mikkel Dalgas
This evolution mirrors what’s already happening in consumer tech, where companies like Apple are integrating compact AI models into mobile devices. The same principle is now reaching heavy equipment — bringing machine learning literally closer to the metal.
The concept of the digital twin, a virtual model of a machine, is familiar territory for OEMs. But Dalgas sees the next step emerging quickly: the cognitive digital twin.
“A digital twin shows you what’s happening. A cognitive twin tells you why and what to do next,” he says. These AI-driven twins will interpret sensor data, recognize anomalies, and even express an opinion about the machine’s health.
According to the World Economic Forum, digital-twin ecosystems are evolving into intelligent networks that connect OEMs, suppliers, and operators in real time. For equipment manufacturers, this will enable smarter design loops, faster innovation, and machines that continuously learn from field performance.
This evolution marks a powerful new phase in machine intelligence that blends data science with decades of engineering knowledge.
At the heart of every OEM innovation is a shared goal: eliminate downtime. For years, the industry has been data rich but insight poor. AI changes that equation by transforming raw data into actionable intelligence.
“We’ve collected data for years but struggled to monetize it,” Dalgas reflects. “AI gives OEMs a way to turn that data into insight, accelerating uptime, reducing cost, and building better machines.”
AI in construction is not a futuristic concept. It is the practical toolkit for OEMs ready to lead the industry’s digital transformation.
AI is changing the game for the construction industry, unlocking new possibilities in efficiency, safety, and collaboration. In this series, industry leaders and experts dive into how AI is being integrated across workflows, overcoming key challenges and paving the way for more intelligent, innovative construction practices. Whether it’s predictive maintenance, workforce transformation, or data-driven decision-making, we’ll uncover how AI is driving progress and shaping what’s next for construction.
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