Artificial intelligence (AI) is dramatically transforming the infrastructure and engineering sectors by automating design processes, optimizing maintenance schedules, and analyzing complex datasets at unprecedented speeds. The potential benefits—including enhanced operational efficiency, accuracy, and cost reductions—are substantial. However, as innovation accelerates, organizations must carefully evaluate how to harness this transformative technology effectively.
Amid the enthusiasm surrounding AI’s capabilities, critical questions arise: Where should investments be allocated for maximal success? How can business processes and models be adapted? And where should organizations begin? The answer lies not in displacing human expertise but in integrating it at the core of AI applications, thereby amplifying and accelerating outcomes. A recent study by MIT highlighted that only 5% of companies investing in AI currently realize profits from it. In the realm of Critical National Infrastructure (CNI) engineering, the real advantage of AI is its ability to enhance decision-making speed and quality beyond human capabilities.
The successful implementation of AI heavily relies on maintaining and sometimes expanding human oversight and decision-making, particularly regarding ethical, transparent, and secure applications within CNI. There is a notable shift in focus across the infrastructure engineering sector. Historically, success was measured by the capacity to deliver large capital projects, such as new roads, bridges, and rail lines. Today, the emphasis is increasingly on efficiently managing and sustaining existing assets to ensure resilient service provision.
Organizations in both the public and private sectors are grappling with tighter budgets and aging infrastructure, leading to a pressing need to maximize the utility of current assets. This challenge is further complicated by increasing risks to UK CNI from extreme weather events and state-backed cyber-attacks, as noted in the Strategic Defence Review. Balancing operational expenditure with performance, reliability, safety, and the imperative to reduce costs and carbon emissions is becoming critical.
Recent extreme weather events have underscored vulnerabilities in critical national infrastructure, revealing weaknesses in both physical assets and the management systems overseeing them. Since 1980, average temperatures have risen by approximately 0.8 to 1°C, and all ten of the warmest years on record have occurred since 1990. More frequent heatwaves strain infrastructure, resulting in buckled railway tracks, softened road surfaces, and sagging power lines, all of which pose safety and performance risks. Increased storm activity further challenges water and transportation networks.
AI presents a clear opportunity for transformative impact in this sector, and the urgency for its application is evident. However, the nature of CNI management means that operational and procedural changes can face significant resistance due to regulatory constraints, safety concerns, and institutional conservatism. While these attitudes are essential to prevent safety failures, there is a risk of overlooking opportunities for improvement or technological advancement, often due to overestimating the consequences of a single erroneous AI decision compared to numerous human decisions based on intuition.
The most significant potential for AI in infrastructure lies in fundamentally changing how operators interact with data and systems to support decision-making. This shift is as much about evolving organizational models and procedures as it is about the technology itself. Utilizing agentic AI to navigate complex, legacy systems can significantly enhance human capital, offering valuable insights for rapid operational decisions. Nevertheless, the infrastructure sector continues to lag behind other industries in scaling innovation, often hindered by complex systems, outdated processes, and risk-averse cultures.
As AI becomes more integrated into the infrastructure engineering sector, its medium-term success will depend on how effectively it complements human intelligence and expertise, rather than its level of autonomy. AI lacks the context, experience, and ethical considerations essential in safety-critical environments such as transport, energy, and utilities. A predictive AI model may suggest that a bridge requires maintenance, but only an experienced engineer can interpret that data, assess the broader implications, and determine the most effective response.
To maximize the benefits of AI, organizations must transform their decision-making processes to redefine the relationship between humans and AI. This necessitates a balanced, risk-based approach combining technical innovation with human oversight and governance. Organizations must adopt a more audacious stance in testing and implementing new technologies, starting with manageable pilot programs and incremental scaling to build confidence and trust.
A systems thinking approach will be crucial, enabling organizations to view infrastructure as an interconnected system rather than merely a collection of assets. This perspective allows for a better understanding of where AI can add the most value and where human oversight is less critical, paving the way for future scaling. AI applications have gained traction through targeted pilot programs that deliver clear value before addressing more complex issues. In these cases, human oversight remains integral, as engineers, data scientists, and operators validate insights and ensure alignment with broader strategic objectives, providing the ethical and contextual perspective that machines lack.
Ultimately, organizations poised to lead this new era of engineering intelligence will consider AI not as a substitute for human expertise but as an extension of it. This mindset necessitates a fundamental shift in operations and interactions with technology. When thoughtfully integrated, human and artificial intelligence can enhance operational performance, foster smarter decisions, and unlock new levels of safety, efficiency, and resilience across the critical infrastructure that society relies upon.
Joe Collis is business director, advisory at Amey.
See also
Global Semiconductor Equipment Sales to Reach $156 Billion by 2027, Driven by AI Demand
Google’s Crawler Integration Halts AI Licensing Revenue for Publishers Until 2026
Kaiser Therapists Rally Against AI Impact on Patient Safety Amid Industry Shift
LDS Church Reveals New AI Guidelines, Emphasizing Spiritual Integrity and Ethical Use
MetaX Stock Surges 700% on IPO Amid China’s Growing Demand for AI Chips
