The Role of AI in Construction: Benefits & Use Cases

Plenty of the activities construction firms depend on are prime candidates for AI-infused productivity boosts—think architectural design and engineering, financial project management, resource planning and allocation, scheduling, and document management, to start. That’s good news for both builders and the global economy because while the construction market contributed more than $13 trillion in 2023, these firms posted only 0.4% average annual productivity growth from 2020 to 2022 compared with 2% for the overall economy, according to McKinsey & Company.

AI has the potential to significantly improve those productivity numbers.

What Is AI?

Artificial intelligence refers to computer systems that can learn from data, recognize patterns, and solve certain problems far faster than humans. Tasks that could once be done only by people are now increasingly being done by AI systems.

Key Takeaways

• AI is expected to bolster construction company efficiency in the coming years by automating repetitive tasks, improving project scheduling, and optimizing resource allocation, among other benefits.
• AI-powered systems can reduce the time needed to produce cost estimates from weeks to hours.
• Monitoring systems with embedded AI capabilities can achieve major improvements in jobsite safety and risk management.
• AI-automated document management, contract analysis, and compliance tracking can save significant time while improving project transparency.
• Successful implementation demands quality data, proper training, and careful integration with existing systems.

Understanding the Role of AI in Construction

In construction, various AI technologies, such as machine learning , computer vision, generative AI, natural language processing, and predictive analytics, can work with human collaborators to enhance decision-making, automate processes, and optimize operations across the project lifecycle.

McKinsey says these AI technologies, along with robotics, augmented reality, virtual reality, and other related tech, are about to dramatically accelerate the construction industry’s productivity. This is happening in the nick of time: The industry must grow from the $13 trillion noted above to $22 trillion by 2040 to meet projected world demand—and it already suffers from a shortage of skilled workers.

The productivity boost is expected to come from better project management and scheduling, improved cost estimates, enhanced safety monitoring and progress tracking, and advanced training, among many other benefits. Integrating AI with other construction-specific technologies can amplify these advantages. For example, combining AI capabilities with building information modeling systems could power automatic detection of design conflicts and digital twin simulations of multiple scenarios. Integration with Internet of Things sensors facilitates real-time data collection and monitoring of machinery performance, and cloud-based platforms provide information access to all stakeholders, whether at a construction site or back in the office.

Benefits of AI in Construction

AI has the potential to deliver productivity benefits before, during, and even after the actual construction of a project. Advanced analytics and automation, for example, can apply to each phase of a project, helping construction companies improve efficiency and profitability.

• Preconstruction: AI can contribute to the planning phase by helping to optimize designs for one or more target goals, improve cost estimates, and simplify bidding processes. Generative AI software, for example, can iterate with architects and customers on initial designs to create versions that lower costs, increase functionality, or reduce material waste. It can also check for adherence to codes and identify potential conflicts before construction begins. AI-powered estimation tools can analyze historical project data and current market conditions to generate highly accurate cost predictions, which are crucial for forecasting cash flow. They can also reduce takeoffs—the process of listing all the materials, and their quantities, needed to complete a project—from weeks to hours. That means AI-enabled enhanced precision can make a company’s bid more competitive without sacrificing profit margins. In addition, AI can automate document processing for contracts and specifications, extracting critical information and flagging potential risks, so that little or nothing falls through the cracks during project planning.
• Construction: During active construction, AI can automate many aspects of project management and help companies maximize their use of resources. Dynamic scheduling systems can analyze resource availability, task dependencies, and other factors to create optimal timelines that adapt when conditions change. Back-office functions can benefit from automated financial controls that track costs versus estimates, flag anomalies, and provide real-time visibility into project performance. Furthermore, AI can improve resource allocation by analyzing productivity data and staff availability across multiple projects, with the goal of reducing workers’ idle time. To improve safety, computer vision systems currently monitor construction sites continuously for PPE use and soon will help identify unsafe behaviors and then alert supervisors in real time for immediate intervention.
• Post-construction: AI can continue delivering value after projects are complete by helping companies uncover possible business improvements, as well as through smart building management for construction companies that offer maintenance services. For example, predictive maintenance systems can analyze sensor data from building systems to identify potential issues before a failure occurs. AI-powered quality-control systems use image recognition and analysis to identify defects and confirm compliance with specifications. AI can also improve the ability of financial analysis tools to offer detailed insights into the performance of completed projects, helping companies refine their cost-estimation, scheduling, and resource-allocation methods.

Challenges of AI Adoption in Construction

Integrating AI into traditional construction workflows comes with obstacles. Midsize construction firms in particular face hurdles due to resource constraints—yet they may need AI capabilities to remain competitive. Construction leaders will likely face the following key challenges as they navigate their AI journeys:

• Initial costs: While AI adoption can be done in phases, there is an investment in integrating multiple capabilities across various business functions—from adding AI capabilities to estimating software or activating functionality available from the current vendor to enhancing project management tools with predictive analytics. Although the cumulative investment across multiple systems may be substantial, a phased approach allows companies to prioritize AI capabilities that address their most pressing pain points before expanding to other areas. This incremental strategy helps distribute costs over time while generating early wins that help fund subsequent investments.
• Data availability: Many construction companies struggle with data that’s stored across multiple systems, incomplete historical records, and inconsistent data formats. Poor data quality can lead to flawed results from AI systems. Organizations must invest in data collection, cleaning, and integration processes to get the most out of their AI technologies. Here, McKinsey suggests that the mistake many companies make is to “let the perfect be the enemy of the good.” Rather, it advises companies “to make initial changes using the data they have, and then use the outcomes to figure out what improvements are required.”
• Resistance to change: The industry has a reputation for being slow to adopt new technologies. Indeed, cultural resistance can impede AI adoption, as workers struggle to adapt to new workflows. Successful AI deployment requires comprehensive change management so that workers master new tools before additional ones are brought to bear.
• Skills gap: The effective use of AI technologies requires a workforce with digital literacy and an understanding of data-driven decision-making. Many construction professionals lack these skills, creating a significant barrier to adoption. Companies that invest in training programs to help employees develop the necessary competencies while also recruiting talent with relevant technical expertise often do better than peers that don’t.
• Integration issues: Construction companies often use multiple systems for different functions, from project management to construction accounting. Integrating AI capabilities with these legacy systems—which may lack the data structures needed for integration—can be complex, costly, and require significant technical work to achieve interoperability. In this context, the construction firms best positioned to adopt AI have already invested in ERP systems that integrate all of these business functions in a single platform and often include embedded AI capabilities.
• Cybersecurity risks: As construction companies digitize more of their operations, they face increased cybersecurity risks. Protecting sensitive project data and financial information requires robust security measures and ongoing vigilance.
• Regulatory uncertainty: AI’s rapid advancement has outpaced regulatory frameworks in many, if not most, jurisdictions. Construction companies, as in other industries, may face uncertainty about compliance requirements and standards for AI use. This regulatory ambiguity can make companies hesitant to invest in AI technologies that might face future restrictions or requirements.
• Scalability concerns: Construction projects vary significantly in scope, complexity, and requirements, making it difficult to develop one-size-fits-all AI solutions. Companies must carefully consider how AI systems will adapt to different projects and grow at the pace of the business.
• ROI uncertainty: Measuring the return on investment for AI implementations can be challenging, particularly in the early stages. Benefits such as improved safety, better decision-making, and enhanced quality may be difficult to quantify.

Use Cases of AI in Construction

AI’s potential value to construction companies may be seen best through specific, practical applications that address the industry’s persistent challenges. The following applications represent many of the most mature AI uses in construction today.

• Project scheduling: AI promises to move project scheduling beyond static Gantt charts to dynamic, intelligent scheduling systems. AI-powered scheduling systems can analyze historical project data, resource availability, available materials inventory, weather forecasts, and many other variables to generate optimal schedules. Should disruptions occur, the software can automatically recalculate timelines and suggest mitigation strategies. For instance, if a critical material delivery is delayed, the AI system can instantly reorganize tasks to minimize idle time and maintain project momentum.
• Resource allocation: Intelligent resource management systems use AI to refine the deployment of labor, equipment, and materials across multiple projects. By analyzing productivity data, skill sets, and availability, these systems can help companies use their resources at maximum efficiency. For example, AI can anticipate when specialized equipment will be idle on one project and automatically schedule its transfer to another site where it’s needed, reducing rental costs and improving utilization.
• Risk management: AI can turn risk management from reactive to proactive. Systems can analyze project data to identify patterns that precede common risks, such as delays, cost overruns, or safety incidents. This predictive capability allows project managers to implement preventive measures before problems materialize. AI systems can also help organizations quantify risk probabilities and their potential effects, paving the way for more informed decisions about risk mitigation.
• Cost estimation: Advanced AI estimation tools can analyze up to thousands of historical projects to generate more accurate job cost predictions. These systems consider variables that human estimators might overlook, such as seasonal labor availability, regional material price trends, and the impact of concurrent projects on resource costs.
• Progress monitoring: Computer vision and drone technology combined with AI analytics fuel automated progress tracking that surpasses traditional manual methods. AI systems can compare daily site imagery with BIM models and project schedules to calculate completion percentages, identify deviations, and predict potential delays. This real-time visibility helps project managers make timely interventions that keep projects on schedule. Plus, these progress monitoring improvements have ripple effects that can enhance construction revenue recognition and payments.
• Quality control: AI-powered quality assurance systems can use image recognition to detect defects, verify compliance with specifications, and help companies maintain the consistency of their outputs. For example, these systems can identify improper rebar spacing, surface defects, installation errors, and other issues that human inspectors might miss. By catching problems early, AI can reduce rework costs and improve finished quality.
• Safety monitoring: AI-capable safety systems can combine multiple technologies to create safer construction sites. Computer vision can monitor worker behavior and compliance with PPE rules. Meanwhile, predictive analytics can anticipate high-risk periods and activities. NLP technology can analyze safety reports and near-miss incidents to identify patterns and prevent future accidents. McKinsey finds that AI-powered safety systems in logistics, supply chain, and construction operations can reduce workplace accidents by between 20% and 80%, depending on the nature of the firm.
• Design optimization: Generative design powered by AI can explore thousands of design alternatives to find solutions that can meet multiple project requirements. These systems can balance objectives such as cost minimization, structural efficiency, and environmental impact. For complex projects, AI can identify innovative design solutions that human designers might not consider.
• Document management: AI can transform document management from an administrative burden to a strategic advantage. NLP systems, for example, can automatically extract key information from contracts, specifications, and correspondence. AI can categorize and tag documents for easy retrieval, and AI-driven search functions can help team members find needed information. Automated contract analysis can identify many potential risks, compliance issues, and extract important deadlines.
• Predictive maintenance: AI systems can monitor building equipment and systems to predict maintenance needs before failures occur. By analyzing sensor data, maintenance histories, and performance metrics, these systems identify patterns that indicate impending problems. This predictive approach helps reduce emergency repairs, extends equipment life, and minimizes disruption to building occupants. For construction companies offering maintenance services, this capability provides a competitive advantage. Furthermore, construction companies can apply the same predictive maintenance capabilities to their own equipment.

What Is the Future of AI in Construction?

The future of AI in construction promises transformative changes as AI technologies mature and adoption accelerates. Indeed, spending on AI technologies by construction companies is expected to triple in the next five years, from $4.96 billion in 2025 to $14.72 billion in 2030—an average annual growth rate of 24.31%.

The construction sites will evolve to feature increasingly autonomous machinery, such as AI-powered robots and drones performing everything from bricklaying and welding to site surveying and material transport. This shift could fundamentally change construction workforces and project execution. Complementing this physical change, advanced predictive analytics are poised to provide unprecedented foresight into project risks, market trends, material availability, and workforce dynamics. The integration of AI with digital twin technology will create virtual replicas of construction projects that update in real time, powering sophisticated simulations and subsequent refinements throughout a project.

Future AI systems will augment human capabilities through intuitive interfaces and intelligent assistance. NLP will allow workers to interact with AI systems conversationally, providing access to advanced analytics regardless of technical expertise. Such human-AI partnerships could prove particularly valuable in addressing sustainability challenges, as AI tools can help construction companies minimize material usage, reduce waste, lower carbon footprints, and help buildings operate at peak efficiency. As these technologies mature, the distinction between technology-forward and traditional construction companies is likely to sharpen, with early adopters gaining significant competitive advantages in efficiency, quality, and innovation.

Gain Visibility and Profitability with AI and NetSuite

NetSuite’s construction ERP solution provides construction companies with a powerful foundation for AI adoption. As a unified business system, NetSuite centralizes data—from financial transactions and project costs to resource allocation and customer relationships—to create the comprehensive, high-quality data set that AI systems need to function effectively.

NetSuite’s AI capabilities are embedded throughout the platform and offer both “assist” features to automate repetitive tasks and “advise” features that provide predictive insights and recommendations. AI-powered anomaly detection identifies unusual transactions or cost patterns that might indicate errors or fraud. At its core, NetSuite offers intelligent financial management; the system can automatically categorize expenses, allocating costs to appropriate projects and identifying potential overruns before they drag down profitability, and analyze resource utilization patterns to improve scheduling and allocation decisions.

The integration of AI into construction operations represents a fundamental shift in how projects are planned, executed, and managed. For most construction companies, AI adoption represents an imperative, rather than an option. AI’s ability to enhance cost-estimation accuracy, refine resource allocation, improve safety outcomes, and streamline document management addresses some of the industry’s most persistent challenges. Success lies in taking a phased approach that aligns technology with business objectives, starting with high-impact use cases while investing in data quality and workforce training.

AI in Construction FAQs

Can AI do construction estimates?

Yes, AI can improve both the speed and accuracy of construction cost estimates. AI-powered estimation tools can analyze historical project data, current market conditions, and project-specific requirements to generate highly accurate cost estimates in a fraction of the time required by traditional methods. Moreover, AI estimation tools can automatically update estimates as project parameters change, identify potential cost-saving opportunities, and flag risks that might impact the budget.

How is AI used in the construction industry?

AI is rapidly changing many aspects of the construction industry through multiple applications across the project lifecycle. In the design phase, AI-powered generative design tools can help create building layouts and identify potential issues before construction begins. During construction, AI enables real-time progress monitoring through computer vision and drone technology. It also helps optimize schedules and resource allocation. AI-powered safety systems use cameras and sensors to identify hazards, PPE compliance, and prevent accidents.