Industry experts predict that artificial intelligence (AI), like search engines and the cloud before it, will transition from an emerging, specialized tool to a mainstream technology. Even market sectors seeing early adoption, however, are hindered by limited expertise, data complexity, and more. AI’s use in capital construction projects risks being beset by similar roadblocks. A McKinsey and Company study, "Artificial Intelligence: Construction Technology’s Next Frontier," revealed that adoption of AI in engineering and construction trails that of other fields. Nevertheless, progress in AI adoption is being made as owners, designers, contractors, site selectors, and service providers lean into the tool’s potential. Organizations that understand the ways specific challenges in capital project development can be offset using AI are poised to reap the greatest advantages.
The introduction of AI into capital project workflows comes at an auspicious time. The manufacturing sector and its related construction market are stable, with Reuters confirming that U.S.-based manufacturing is growing and the Associated Builders and Contractors’ Confidence Index showing that the nonresidential construction market is also stable. Early 2024 did see slight spending falls in nonresidential construction, however. Combined with ongoing issues such as high raw material prices, the risks to capital project success mean teams should harness every available tool that has the ability to improve communication, collaboration, project knowledge, and efficiency.
Technology tools that predate AI established a strong track record of reducing risks and improving efficiency and productivity. Like these predecessors, AI in the architecture, engineering, and construction (AEC) industry has so far brought about improvements in safety and quality, as well as overall project performance in costs and schedule. BuiltWorlds, an organization with a network of companies in the built environment, has been bringing together organizations to better understand the opportunities where AI can positively impact construction, from more efficient planning, design, construction, and operations to identifying ways AI can improve the user experience with facilities and reduce carbon emissions.
Separate from, but complementary to, AI is machine learning (ML). While AI can perform some tasks that previously demanded human effort, ML enables software to predict outcomes with good accuracy. ML operates without the need for explicit programming, using pattern recognition to create models that can then develop iteratively as new data is fed into the system.
AI in Construction, Phase-by-Phase
During a project’s planning phase, AI can support data analysis and decision-making. Given the complexity of capital construction, there is an enormous amount of relevant data related to site conditions, regulatory requirements, and more. Assembling and interpreting such a large amount of data is impractical without a supporting technology. AI-driven algorithms can accomplish predictive analysis quickly. This helps project owners optimize their investments by choosing the right site and mitigating potential risks at the very beginning of the project’s timeline.
6 -- this is the number of building blocks identified by PWC for AI adoption in capital projects. When it comes to site selection, AI offers a multi-faceted approach that incorporates factors such as site conditions, construction costs, energy costs, labor availability, supply chain, transportation, and numerous other variables to optimize decisions. AI algorithms can quickly search large datasets and analyze geological data, terrain features, and environmental factors to assess a site's suitability and resilience to natural hazards. At the same time, AI can calculate construction cost data, taking into account materials, labor costs, and regulatory requirements, to estimate the financial feasibility of constructing a building in a specific location. Energy costs, a key driver of sustainability and operating costs, can also be assessed using AI models that predict future energy prices and consumption patterns based on historical data and market trends. Additionally, AI can analyze demographic trends, workforce skills, and local economic conditions to assess workforce availability and readiness. By incorporating these and other relevant factors into the analysis, AI provides decision-makers with comprehensive insights and helps select sites that offer the best mix of benefits for their specific needs and goals.
During the design phase, AI-enabled design tools work on top of BIM models to revolutionize clash detection. By analyzing operational characteristics as well as physical ones, conflicts can be identified between systems such as electrical conduits, plumbing, process equipment, clearances, and more. The iterative, pattern-recognizing algorithms of ML can generate various design alternatives and quickly propose alternate layouts, allowing project managers and their design and construction teams to accelerate the design process while ensuring compliance with industry standards and regulations.
Once a project moves into preconstruction, AI can analyze market data and support more accurate cost estimation, scheduling, risk assessment, and resource allocation. AI-powered project management platforms can also conduct constructability analyses. AI algorithms can analyze historical project data to create accurate cost estimates and timelines, taking into account factors such as materials, labor, and potential market fluctuations. AI-driven planning tools can optimize construction schedules by considering various constraints and dependencies, thereby minimizing delays and cost overruns. AI tools can also assist with materials sourcing and tracking carbon content of materials. Additionally, AI can facilitate risk management by identifying potential schedule risks and providing remediation strategies. AI-driven collaboration platforms, in conjunction with estimating and project management tools, enable communication and coordination between project teams, subcontractors, and suppliers, thereby increasing transparency and productivity throughout the preconstruction phase. By leveraging the power of AI, construction projects can achieve greater predictability, efficiency, and success from start to completion.
At no point in a capital project is data analysis more important than during the construction phase itself. Many construction technology platforms have incorporated “co-pilot” AI functionality to improve the efficiency of their systems. AI can compare construction progress to plan, assess project delivery alternatives, and run “what-if” scenarios for project scheduling. Evaluating real-time information about supply timelines can be invaluable in an environment where supply chain concerns are among the biggest challenges for a construction project. Technologies that have become familiar on the job site, such as drones, robotics, and IoT sensors, are now incorporating AI that can, for example, perform detailed aerial surveys, identify safety hazards, and monitor equipment performance. Progress that was already being made using robotics to automate tasks is being further improved by leveraging AI to assist with more complicated tasks. This is especially critical since labor availability is another risk factor in the current construction environment.
Organizations that understand the ways specific challenges in capital project development can be offset using AI are poised to reap the greatest advantages. AI can significantly contribute to carbon reduction in the construction and operation of buildings by optimizing processes and enhancing energy efficiency. During construction, AI-driven tools can analyze vast amounts of data to identify the most sustainable materials and methods, minimizing waste and reducing the carbon footprint. AI can also improve project planning and logistics, ensuring that resources are used efficiently and transportation emissions are minimized. In building operations, AI-powered systems can monitor and control energy consumption in real-time, adjusting heating, cooling, and lighting to optimal levels based on occupancy and usage patterns. Predictive maintenance powered by AI can also extend the lifespan of building systems, reducing the need for replacements and further cutting down on emissions. By integrating AI into both construction and operational phases, the building industry can make significant strides toward achieving carbon reduction goals and promoting environmental sustainability. Considering the data already available from building management systems and their sensors, AI is a natural fit for optimizing building performance and energy efficiency. There is also a role for AI in predictive maintenance. These benefits can reduce downtime, improve plant reliability and output, and improve occupant comfort.
The Human and Facility Interface
Gamification and AI are transforming the design and construction of facilities, as well as enhancing the interface between people and these spaces. Gamification applies game-design elements to non-game contexts, making processes more engaging and motivating for stakeholders. In construction, this can lead to better project management, increased collaboration, and improved training for workers through interactive simulations and virtual reality (VR) environments. These tools enable teams to visualize complex designs, identify potential issues, and experiment with solutions in a risk-free setting, ultimately improving efficiency and outcomes.
The interface between people and facilities is also evolving thanks to these technologies. Gamified interfaces can make facility management more intuitive and engaging, encouraging occupants to participate in energy-saving initiatives or report maintenance issues. AI enhances this interaction by personalizing the environment to meet individual needs, such as adjusting lighting and temperature based on user preferences or predicting when maintenance is required to ensure uninterrupted comfort and functionality. Together, gamification and AI are making the design, construction, and operation of facilities more efficient, sustainable, and user-centric.
Adopting AI in the Manufacturing Sector
PWC has launched a global series on AI in manufacturing, and its first white paper, "Introducing AI in Manufacturing," identifies six building blocks that apply to the construction of capital projects.
- Identifying business applications. Capital project teams should develop an overall vision and identify specific pilot projects or use cases.
- Data management. Because data is the foundation of AI, capital project teams should develop a map of sensors and equipment data sources “to understand the data volumes, velocities, and varieties they will be dealing with. Furthermore, they need to define data quality metrics and systematically monitor these to create awareness of their importance, which is often a major challenge for implementing AI,” according to PWC.
- Technology adoption. Not only will a facility have multiple data sources to be integrated and managed, but project teams face multiple choices for technologies to gather, store, process, and analyze that data. Again, developing an architecture or map during project development will help.
- Talent and organization. PWC recommends a team comprised not only of data scientists but data engineers, data stewards, solution architects, and analytics translators.
- Process. As with other construction-related and operational systems, AI implementation should be approached using a clear process. This includes clear ownership and access, security measures, and performance metrics.
- Culture. Manufacturers and capital project teams must educate members of the organization to build trust. This involves communicating not only the capabilities of AI but also its limitations and risks.
As happened with many recent technologies during their early-stage rollout, AI is currently seen as a disruptor. Some organizations are struggling to effectively adopt AI, while others are avoiding it altogether. However, experience is already showing that AI can improve productivity and efficiency in capital project planning and delivery, safeguard workers, improve quality, and enhance the human interface with facilities. An intentional approach to AI adoption will offer those involved in planning, designing, constructing, and managing facilities a competitive advantage.
