Article
The industry recognizes the scale of the shift. In Russell Reynolds Associates’ H1 2026 Global Leadership Monitor, 97% of engineering and construction executives agreed that generative AI will be a required skill for future C-suite executives. Yet only 42% were confident they had the right skills to help implement AI, while just 20% said their teams had fully implemented generative AI in day-to-day workflows.
Our interviews with 18 CEOs, non-executive directors, and senior executives in the industry point to the same challenge: firms have identified numerous use cases, but the harder task is translating, scaling, and embedding AI into how they operate. As one executive put it, “you can barely escape a discussion from your investors that does not include AI.”
The stakes extend beyond implementation. AI could also reshape the economics of the engineering model. One executive compared the shift to the early wave of high-value engineering center offshoring, when clients ultimately captured much of the savings once lower-cost delivery became transparent. AI could follow the same pattern: once clients see the productivity benefit, traditional fee structures become harder to defend.
This raises two important questions for these firms:
As the industry moves away from execution-heavy pyramids and towards expert-led, AI-enabled models, requiring new leadership capabilities and mindsets, the firms that succeed will be those that integrate engineering expertise, data, and AI into a coherent system that creates real competitive advantage.
AI is already changing how core engineering and project delivery work gets done. Activities that previously required days of manual effort can now be completed in hours. One executive described engineering roles as moving “from execution to validation,” with AI producing outputs that humans then review, challenge, and sign off. As a result, clients increasingly expect contractors to demonstrate improved efficiency, especially as they invest in their own AI capabilities.
For engineering firms, this creates dual pressure: keep pace with client expectations, while redefining their role in a value chain where data ownership often sits elsewhere.
The biggest challenge is to move from isolated tools to integrated systems. Leading firms are beginning to move beyond standalone pilots toward interconnected AI ecosystems, including “digital workers” that coordinate multiple agents across workflows, evaluating multiple design and planning scenarios, and leveraging expertise and project experience from across the organization.
Yet much of the industry remains in experimentation mode. The risk, as one non-executive director put it, is “lots and lots of pilots” without a clear strategy for how to scale those that carry the most value creation potential.
Historically, engineering leadership was grounded in deep technical expertise, with career progression following a path from engineering excellence to senior management. But as AI takes on a greater share of execution, leaders must increasingly integrate the physical and digital worlds.
This also changes what firms should look for in senior leadership. AI competence will matter, but so will learning agility, resilience, intellectual curiosity, and comfort with ambiguity. Several interviewees emphasized the importance of leaders who enjoy change and can bring others with them. Those who are anxious about change, overly attached to established methods, or unable to learn from younger and more digitally fluent colleagues, may quickly hit their ceiling.
RRA’s H1 2026 Global Leadership Monitor found that 88% of engineering executives are confident in their ability to identify the strategic initiatives for higher-value opportunities, and 98% believe they can translate strategic priorities into clear actions for their team. As AI becomes more integrated and embedded within these operations, value creation will hinge on leaders who can connect current and future engineering, technological, and commercial priorities.
As the pool of available engineering services leaders with the right experience becomes more limited, engineering organizations need more deliberate and proactive succession planning for the next generation of leaders. The most successful future leaders may not be the best traditional engineers, but must be the best “integrators:” leaders who understand enough about engineering, technology, commercial models, and client problems to futureproof the business.
Against the backdrop of wider succession challenges in the sector, interviewees reiterated that retaining and structuring institutional knowledge will be critical. As experienced technical talent approaches retirement, firms need to capture their expertise while building the next generation of leaders who can combine engineering judgment, digital fluency, and commercial discipline.
If leadership capability defines the opportunity, culture determines whether it is realized. Across interviews, culture consistently emerged as a significant barrier to effective AI adoption.
The industry has traditionally been characterized by a low tolerance for error, reflecting the safety-critical nature of its work. This creates an inherent tension, described by one executive as “the actual cultural collision: safety-critical precision versus iterative experimentation.”
A second tension lies in the industry’s economic model and its delivery incentives. Time spent experimenting with AI tools can reduce short-term billability, creating a disincentive for adoption even when the long-term benefits are clear.
AI won’t remove the need for engineering firms, but it will redefine how they create value. As more engineering work is automated, clients will expect faster, more intelligent delivery. As a result, specialist expertise and sound judgment—augmented, not replaced, by AI—may command a premium.
The leadership challenge? Integration. AI-fluent leaders without delivery context risk building tools that teams do not adopt; delivery leaders without sufficient AI literacy may underestimate the scale of change. The CEO and senior P&L leaders need to own strategic choices: where AI creates value, where it introduces risk, and what capabilities the organization must build to translate business needs into technical solutions.
This requires leaders who can bring together engineering expertise, data, technology, risk judgment, and commercial insight. In some cases, firms may also need new models of collaboration with asset owners and operators, including shared environments for developing data, infrastructure, and use cases.
The future industry leader is no longer the top engineer. It’s the leader who can anticipate market shifts; reposition the firm to remain competitive; judge the speed of change; and ensure the business delivers with the discipline, reliability, and trust on which engineering performance depends.
Based on 18 interviews with CEOs, non-executives and executives in engineering, procurement, and construction firms in 2026.
Abigail Skerrett co-leads Russell Reynolds Associates’ Industrial Services & Infrastructure practice. She is based in London.
Natasha Treschow is a member of Russell Reynolds Associates’ Industrials and Board & CEO Advisory practices. She is based in London.
Pierre Lefebvre leads Russell Reynolds Associates’ Global Engineering & Construction practice. He is based in Montreal.
Hans Roth is a senior member of Russell Reynolds Associates’ Industrials and Board & CEO Advisory practices. He is based in Houston.
ja-JP
ログイン
