How AI Is Transforming Energy Construction—and What’s Holding It Back
By M. ABUSALAH, Consolidated Contractor Company (CCC)
(P&GJ) — The digital transformation (DT) is redefining how industries operate, and the energy construction sector is no exception. DT is not solely an upgrade in technology; it is a holistic business strategy that requires cultural, operational, and procedural shifts across an organization. In parallel, artificial intelligence (AI) is emerging as a pivotal enabler, enhancing DT by providing robust analytical and automation capabilities. This article explores the barriers and opportunities for digital transformation and AI adoption, drawing on case studies, change management models, and industry insights to offer a roadmap for energy construction firms navigating these changes.
DEFINING DT AND AI IN CONSTRUCTION
Digital transformation encompasses the integration of cutting-edge digital technologies to improve processes, increase productivity and generate value. In the energy construction sector, DT involves incorporating tools such as Internet of Things (IoT) devices, drones, building information modeling (BIM) and advanced analytics to modernize workflows.
AI extends these capabilities further. AI-powered systems facilitate geospatial route planning, predictive maintenance, prompt engineering and autonomous operations. The synergy of DT and AI creates opportunities not only to streamline operations, but also to redefine business models and competitive strategies.
BARRIERS TO TRANSFORMATION AND AI ADOPTION
Sociotechnical and cultural challenges. One major barrier is the sociotechnical gap: transforming an organization requires changing not only its technology but also its workforce’s skills, mindset and culture.1 Resistance to change is common, especially among employees who fear job displacement or lack the training to manage new systems. Achieving transformation demands patience, effective training programs and strong leadership to foster a culture of innovation (FIG. 1).
Managing digital disruption and complexity. Traditional management and communication methods, such as reliance on email, are inadequate in a digital-first world. Modern lean management practices, which include agile frameworks (e.g., Kanban, SCRUM) and integrated enterprise resource planning (ERP) systems, are essential to reduce waste and enhance collaboration. However, the transition to these new systems introduces complexities in risk management, particularly with regards to data privacy and performance measurement. The use of sensors, tracking software and IoT devices increases the volume of data but also raises concerns over employee privacy and regulatory compliance.
Regulatory and ecosystem challenges. The evolving regulatory landscape presents its own set of challenges. As governments and technology giants push for cloud adoption and new data protocols, construction companies must navigate a maze of compliance requirements. Moreover, the construction ecosystem, comprising joint ventures, subcontractors and supply chains, adds another layer of complexity in ensuring that all stakeholders adhere to updated digital standards.
High initial investment and integration issues. Implementing advanced digital technologies and AI often involves significant upfront costs. In industries where margins are tight, the financial risk of investing in new technologies without guaranteed short-term returns can be a major deterrent. Integration challenges also persist, as legacy systems must be updated or replaced to support the new digital infrastructure.
OPPORTUNITIES OFFERED BY DT AND AI
Enhanced operational efficiency. Digital transformation can markedly improve operational efficiency. By reducing human error and streamlining processes, construction companies can achieve the 3%–4% annual productivity gains necessary to stay competitive.2 AI enhanced technologies such as 3D Lidar scanning enable rapid verification of the pipeline trench profile and right-of-way clearance, ensuring compliance with design specifications and cutting down project delays by reducing rework.
Data-driven decision-making. The digitization of construction processes generates vast amounts of data. Advanced analytics and AI-powered tools can transform this data into actionable insights, leading to more accurate risk assessments and performance predictions. For instance, integrating drones, IoT sensors and big data analytics has been linked to revenue increases of up to 12% for companies that fully leverage these tools.
Innovative business models. Digital transformation paves the way for novel business models. Rather than investing heavily in hardware, such as installing sensors on every excavator, companies can explore alternative approaches, like equipment rental based on usage hours.3 Such models optimize resource utilization, reduce capital expenditure and align costs more directly with operational needs.
AI-enhanced capabilities. AI brings further opportunities by automating complex tasks. In the context of oil and gas pipeline construction, AI-driven geospatial analysis optimizes route planning by considering terrain and environmental factors. Near real-time monitoring using AI predictive models minimizes downtime by foreseeing equipment failures. Moreover, digital twin technology creates virtual replicas of physical assets, enabling continuous lifecycle management and predictive maintenance.
CHANGE MANAGEMENT: STRATEGIES FOR A SUCCESSFUL TRANSFORMATION
Effective digital transformation is not achieved overnight. A robust change management plan is crucial for mitigating risks and ensuring a smooth transition. Drawing on established models such as Kotter’s 8-Step Process and Lewin’s Change Model, the following framework is recommended.4,5
Unfreezing the status quo. Creating a sense of urgency is the first step. Senior leadership must clearly communicate the necessity of change and build a guiding coalition that includes digital transformation and AI leaders and early adopters. This phase involves breaking down existing silos and preparing the workforce for upcoming changes.
Developing a strategic vision. A comprehensive digital transformation and AI strategy should balance both external pressures, like evolving regulations and market dynamics, and internal needs, such as process optimization and cultural shifts. The strategy must incorporate incremental changes, with clearly defined objectives and early wins (e.g., a successful 3D Lidar scanning project) to build momentum.
Modular and gradual implementation. Rather than attempting a wholesale transformation, a phased approach is advised. Modular implementation allows companies to test and refine digital processes on smaller projects before scaling them across the organization. This method reduces risk and minimizes disruption, while providing tangible proof-of-concept outcomes.
Embedding and sustaining change. Once new processes are adopted, it is essential to institutionalize them. This involves ongoing training, continuous feedback loops, and robust governance to ensure that digital tools and AI systems remain aligned with business objectives. Over time, these practices become embedded in the corporate culture, ensuring long-term success.
CASE STUDIES AND REAL-WORLD APPLICATIONS
3D lidar scanning for structural inspections. A notable early win in digital transformation was the deployment of 3D Lidar scanning on a critical project. In one case, a 3D Lidar scanner was used to inspect the foundation of a diaphragm wall, and the process was completed in just two days. This rapid assessment not only identified deviations from the BIM model, but it also demonstrated the efficiency gains of digital methods over traditional inspection techniques. LiDAR scanners generates unstructured spatial data, while AI algorithms, specifically Deep learning helps classify this data, assigning meaning to each point, such as identifying whether it represents a tree, wall, power line, ground etc.6
AI in pipeline construction. In the oil and gas pipeline sector, AI has been used to optimize route planning through geospatial analysis. By evaluating terrain, seismic risks and environmental impacts, AI models help engineers select the safest and most cost-effective routes. Additionally, the use of predictive maintenance powered by AI minimizes downtime, ensuring continuous operation and reducing unforeseen expenses. A noteworthy example of innovation in pipeline monitoring involves the use of a specialized small ball designed to navigate through the pipeline's flow. This ball is equipped with cameras that capture continuous 365° video footage of the internal surface. The recorded videos are then analyzed using advanced deep learning algorithms to identify anomalies such as corrosion, leakage and sediment buildup, which could impede fluid flow. Also, this model detects leaks as low as 0.04 liters per second. This proactive approach enhances maintenance and safety by enabling timely interventions based on detected issues. To ensure optimal performance, the model was trained on a comprehensive dataset that included various surfaces, such as clean pipelines, corroded segments, surfacing with leakage and areas with sediment buildup. This rigorous training enables the algorithms to effectively distinguish between normal and problematic conditions within the pipeline system.
Redefined business models. Digital transformation has led to innovative business models across the industry. For example, rather than installing permanent sensors in construction equipment, companies are increasingly adopting usage-based rental models.6 This approach not only streamlines operations, but it also reduces capital outlays, allowing firms to allocate resources more efficiently.
Prompt engineering. By applying prompt engineering to advanced large language models (LLMs), companies can deploy AI agents capable of answering onsite technical questions instantly. This significantly boosts efficiency and output quality, particularly when models are trained on domain-specific content. However, successful implementation requires strict data governance; the training source must be authoritative to avoid the "garbage in, garbage out" phenomenon. Furthermore, employees must be trained to craft effective prompts. For example, rather than simply asking an AI to "write a safety plan," a refined prompt would be: "Play the role of a construction safety manager and OSHA expert. Create a site-specific safety plan for an airport terminal building project in a rural environment. Major scopes include steel erection, glass wall installation, plumbing, electrical, and mechanical systems. Include fall protection requirements, crane safety protocols, and public protection measures. Format this as a detailed outline with section references to OSHA standards." When combined with a robust internal knowledge base, this level of prompt engineering can boost operational efficiency (FIG. 2).
Takeaway. The convergence of digital transformation and AI offers the energy construction sector unprecedented opportunities for operational excellence and competitive advantage. Although barriers such as sociotechnical challenges, regulatory complexities and significant investment costs exist, the potential rewards in terms of enhanced efficiency, improved decision-making, and innovative business practices are compelling. A thoughtful, phased approach to change management, combined with a strategic vision for digital adoption, is essential for navigating these challenges and fully harnessing the benefits of these transformative technologies.
As the industry continues to evolve, organizations that proactively integrate digital and AI-driven solutions will not only improve their project outcomes, but they will also set new benchmarks for innovation in the construction domain.
DR. MUSTAFA ABUSALAH is a Transformational AI & Digital Leader with more than 18 yrs of experience in Oil & Gas, Energy, and Construction, and he specializes in driving enterprise-wide digital transformation, AI adoption and data-driven innovation. He holds a Sc.D. in Artificial Intelligence and has led global teams to deploy scalable AI, IoT, data science and BI solutions that boost operational efficiency, reduce risk and unlock profitability. His leadership has spanned strategic roadmap development, ERP integration, change management and building future-ready talent, with a focus on turning data into a strategic asset.
LITERATURE CITED
1 Abusalah, M. “Practical Approach to Managing Project Challenges,” Pipeline and Gas Journal, November 2024, online: https://pgjonline.com/magazine/2024/november-2024-vol-251-no-11/features/practical-approach-to-managing-project-challenges
2 Agarwal, R., Chandrasekaran, S., & Sridhar, M. “Imagining construction’s digital future,” McKinsey & Company, June 2016, online: https://www.mckinsey.com/capabilities/operations/our-insights/imagining-constructions-digital-future
3 Construction Equipment Rental Market - Global Forecast 2025-2030, Report, Research and Markets, November 2025: https://www.researchandmarkets.com/reports/5306610/construction-equipment-rental-market-global
4 Kotter, J. P. “Leading Change: Why Transformation Efforts Fail,” Harvard Business Review, May 1995, online: https://hbr.org/1995/05/leading-change-why-transformation-efforts-fail-2
5 Lewin, K. (as referenced by Cummings, S., Bridgman, T., & Brown, K. G.) “Unfreezing change as three steps: Rethinking Kurt Lewin’s legacy for change management,” The Tavistock Institute of Human Relations, September 2015, online: https://doi.org/10.1177/0018726715577707
6 Gharineiat, Z., Tarsha Kurdi, F., Campbell, G. Review of Automatic Processing of Topography and Surface Feature Identification LiDAR Data Using Machine Learning Techniques. Remote Sens. 2022, 14, 4685. https://doi.org/10.3390/rs14194685