The Future of AI in Construction: Opportunities and Challenges

The construction industry is on the cusp of a major transformation driven by emerging technologies like artificial intelligence (AI) and automation. 

These innovations promise to boost productivity, improve safety, and help address skilled labour shortages. However, realising these benefits will require overcoming some significant challenges. This in-depth blog post examines the current state and future outlook for AI and automation in construction.

The Potential of AI and Automation in the construction industry

AI and automation have the potential to profoundly impact construction in multiple ways:

Using Ai for Improved Productivity in the construction industry 

The construction sector has lagged other industries in productivity growth. AI and automation can help improve productivity through:

  • Optimised planning and scheduling using AI algorithms
  • Automated hauling and material transport with autonomous vehicles
  • Automated fabrication and modular construction in factories
  • AI-powered project management software
  • Exoskeletons and assistive robotics to reduce worker fatigue

Enhanced Safety with Ai in the construction industry

Construction work is inherently hazardous. AI and automation can help improve safety by:

  • Automating dangerous tasks like working at heights or confined spaces
  • Wearables and sensors to monitor worker fatigue and prevent injuries
  • AI analysis of safety data to predict and avoid accidents
  • Automated quality inspection using computer vision
  • Exoskeletons that reduce strain and overexertion

Addressing Skilled Labour Shortages

Skilled tradespeople are ageing out of the workforce faster than they can be replaced. AI and automation can help by:

  • Automating tasks normally done manually to reduce labour requirements
  • Using AR/VR to train new workers faster and more effectively
  • Intelligent assistants that augment human capabilities and skills
  • Robots that handle heavy lifting and repetitive tasks

More Sustainable Construction

AI and data analytics can optimise construction plans to reduce waste and environmental impacts. Automation and robotics can also improve precision and consistency in construction.

The State of Adoption

While the potential impact of these technologies is tremendous, current adoption levels in construction remain relatively low.

  • In a recent survey, only 3% of UK construction employers reported using AI, and less than 10% used automation or related technologies like AR, VR, or drones.
  • Medium and large companies are leading adoption, while smaller firms have been slower to adopt.
  • Areas seeing the earliest adoption include using VR/AR for design and planning, laser scanning to map sites, sensors to monitor assets, and wearables to track workers.

So adoption is still in the early stages, but interest is growing quickly. More than half of construction employers believe AI, automation, and related technologies will become mainstream in the industry within 5-10 years.

Challenges Holding Back Adoption

Realising the possibilities of AI and automation in construction will require overcoming some significant barriers:

Initial Costs

  • Implementing AI, robots, and automation requires major upfront investment in equipment, software, training etc. This can be prohibitive, especially for smaller firms.

·        Equipment Costs: Acquiring the necessary AI-powered machinery, robotics, and automated systems entails substantial expenses. Construction companies must invest in state-of-the-art autonomous vehicles, collaborative robots, and cutting-edge construction equipment tailored to their specific needs.

·        Software Expenses: Implementing AI in construction processes requires robust software solutions, including AI algorithms, machine learning models, and data analytics platforms. These software packages are often sophisticated and come with licensing fees, further contributing to the overall expenditure.

·        Training Expenditure: To fully leverage the potential of AI and automation, the construction workforce needs to undergo specialised training programs. Workers need to be equipped with the skills to operate, maintain, and troubleshoot the new technologies. Training sessions, workshops, and educational resources come with costs that add to the initial investment.

·        Integration and Customization: Integrating AI and automation into existing construction workflows necessitates tailored solutions and customization. Companies may need to work closely with technology providers to ensure seamless integration with their current systems. This personalised approach adds to the overall implementation expenses.

·        Consultancy and Support: Seeking professional advice from AI and automation experts can be crucial in planning and executing the adoption process. Consultants and support services offer guidance on the best technologies to invest in, help design implementation strategies, and provide ongoing assistance. However, these services incur additional costs.

·        Infrastructure Upgrades: Some AI and automation technologies require infrastructure upgrades, such as establishing high-speed data networks and cloud computing capabilities. These infrastructure enhancements are essential for smooth operations but can impose substantial costs on construction firms.

Despite the initial costs, it is essential for construction companies to recognize the long-term benefits that AI and automation can bring. While the initial investment might seem substantial, the potential for significant returns in terms of increased productivity, enhanced safety, and streamlined operations can make it a worthwhile endeavour.

Integration of Ai with Existing Systems

  • Most technology used in construction today lacks interoperability. Integrating new innovations with legacy tools is difficult and expensive.

Integration with existing systems is a critical challenge that the construction industry faces when adopting new AI and automation technologies. One of the major roadblocks is the lack of interoperability between the cutting-edge AI solutions and the legacy tools that are commonly used in construction processes. This incompatibility creates difficulties and incurs significant expenses during the integration process.

Construction companies often rely on a mix of software applications, equipment, and hardware that have been developed by various vendors over time. These disparate systems were not originally designed to communicate or work seamlessly together. As a result, when introducing AI and automation, ensuring smooth integration with the existing infrastructure becomes a complex and time-consuming task.

  • Data Compatibility: AI and automation rely heavily on data to drive insights and decision-making. However, legacy systems may store data in different formats, making it challenging to extract, interpret, and share data across platforms. This lack of data compatibility hinders the effective functioning of AI algorithms and automation processes.
  • Customization Requirements: Integrating AI and automation technologies often requires customization to align with the specific needs of a construction company. Tailoring the new technologies to work seamlessly with the existing systems demands significant efforts in software development and adjustments, which can be costly.
  • Vendor Lock-In: Some legacy systems come with vendor-specific constraints, which can result in a form of vendor lock-in. This means that construction companies may feel compelled to stick with a particular technology provider due to dependencies on their existing systems, limiting the flexibility to explore and adopt new solutions.
  • Security and Privacy Concerns: When integrating new technologies with legacy systems, there are security and privacy risks to consider. Data breaches and unauthorised access to sensitive information become more likely if the integration is not adequately secured.
  • Training and Adaptation: Integrating new AI and automation technologies requires training the workforce to use the new tools effectively. Employees who are accustomed to the old systems may face a learning curve and may resist change, impacting the overall adoption process.

To overcome these integration challenges, construction companies need to take a strategic approach:

  1. Standardisation: Encouraging standardisation across different systems and adopting industry-wide data formats can simplify the integration process. Embracing open standards enables seamless data exchange and compatibility.
  2. Collaboration with Technology Providers: Engaging in collaborative efforts with AI and automation technology providers can lead to better integration solutions. Close partnerships with vendors can facilitate custom integrations tailored to the company’s specific requirements.
  3. Gradual Implementation: Implementing AI and automation in a phased manner can help address integration complexities. Starting with a few pilot projects or specific areas of operation allows the company to assess the impact of the new technologies and make necessary adjustments before full-scale deployment.
  4. APIs and Middleware: Application Programming Interfaces (APIs) and middleware solutions can bridge the gap between different systems, enabling them to communicate and share data effectively. Adopting such middleware can facilitate smoother integration and reduce compatibility issues.
  5. Data Governance and Security: Establishing robust data governance and security measures is crucial when integrating new technologies with legacy systems. Encrypting sensitive data, implementing access controls, and regularly updating security protocols safeguard against potential risks.

Risk Aversion

  • The industry tends to be slow to adopt new methods. Firms are hesitant to invest in unproven technologies.
  1. construction means that there may be fewer compelling success stories to serve as evidence of the benefits. Without a robust track record of positive outcomes, some companies may hesitate to take the risk.
  2. Short-Term Focus: In an industry often driven by tight project deadlines and immediate deliverables, the long-term benefits of AI and automation may not be immediately evident. Companies may prioritise short-term gains over longer-term transformative potential.

To address risk aversion and encourage adoption of AI and automation in construction, several strategies can be employed:

  1. Pilot Projects: Starting with small-scale pilot projects can allow construction firms to test and validate new technologies in a controlled environment. Demonstrating success at a smaller scale can build confidence and support broader implementation.
  2. Collaboration and Knowledge Sharing: Industry organisations, associations, and government bodies can play a role in facilitating collaboration and knowledge sharing among construction companies. Sharing best practices, success stories, and lessons learned can help ease concerns and foster a culture of innovation.
  3. Performance Guarantees: Technology providers can offer performance guarantees to construction firms, promising specific outcomes and benefits from the adoption of their solutions. These guarantees can help alleviate concerns about ROI and performance.
  4. Industry Advocacy and Education: Industry leaders and experts can advocate for the benefits of AI and automation in construction and provide educational resources to address misconceptions and fears. Workshops, conferences, and seminars can help increase awareness and understanding.
  5. Partnerships with Innovative Suppliers: Building strategic partnerships with suppliers and technology vendors that have a proven track record in AI and automation can mitigate perceived risks. Leveraging the expertise of these partners can lead to smoother implementation and positive outcomes.
  6. Government Support: Governments can provide incentives, subsidies, or grants to encourage construction companies to invest in AI and automation. Financial support can help offset initial costs and incentivize risk-taking.

Training Requirements for Ai implementation in the Construction industry

  • Using these emerging technologies requires new skillsets construction workers often lack today. Extensive retraining will be necessary.

As the construction industry embraces the transformative potential of AI and automation, one of the most critical challenges it faces is the need for upskilling and retraining the existing workforce to adapt to these emerging technologies. The successful integration and utilisation of AI and automation tools require construction workers to acquire new skill sets and knowledge that go beyond their traditional expertise. Here are some key aspects to consider regarding the training requirements:

  1. Digital Literacy: AI and automation technologies often rely on digital interfaces and software applications. Therefore, construction workers must develop a higher level of digital literacy to operate and interact with these tools effectively. Training programs should focus on teaching workers how to navigate and utilise various software, data analytics platforms, and other digital resources.
  2. Technology Operation and Maintenance: Depending on the specific AI and automation solutions adopted, workers may need to learn how to operate and maintain complex equipment, robotics, and autonomous systems. Training should cover equipment calibration, troubleshooting, and regular maintenance tasks to ensure optimal performance and longevity.
  3. Data Analysis and Interpretation: AI relies heavily on data to derive insights and make informed decisions. Construction workers will need to understand how to gather, analyse, and interpret data generated by AI and automation systems. This data-driven approach will enable them to identify trends, optimise processes, and implement data-backed solutions.
  4. Human-Robot Collaboration: With the increasing use of collaborative robotics (cobots) and AI-powered tools, construction workers will need to learn how to work alongside these technologies. Training should focus on establishing effective human-robot collaboration to maximise productivity and safety on construction sites.
  5. Cybersecurity Awareness: As the construction industry becomes more digitally connected, it also becomes more vulnerable to cybersecurity threats. Workers must be educated on cybersecurity best practices to protect sensitive data and prevent potential cyber-attacks on AI and automation systems.
  6. Adaptability and Continuous Learning: AI and automation technologies are rapidly evolving, and new advancements are introduced frequently. Construction workers need to develop an adaptable mindset and embrace a culture of continuous learning to stay updated with the latest developments and innovations in their field.

To address the training requirements effectively, construction companies can adopt the following strategies:

  1. Comprehensive Training Programs: Develop comprehensive training programs that cover various aspects of AI and automation implementation. These programs should be tailored to different roles within the construction industry, ensuring that each worker receives training relevant to their responsibilities.
  2. Hands-on Experience: Offer hands-on training opportunities where construction workers can interact with AI-powered tools and automation equipment in real-world scenarios. Practical experience will enhance their confidence and proficiency in using these technologies.
  3. Online Learning Resources: Provide access to online learning platforms and resources that offer courses on AI, automation, and related technologies. Online resources can supplement in-person training and enable workers to learn at their own pace.
  4. Collaboration with Educational Institutions: Collaborate with educational institutions, technical schools, and vocational training centres to develop specialised AI and automation training programs. Such partnerships can bridge the skills gap and attract new talent to the construction industry.
  5. Upskilling Initiatives: Encourage workers to participate in upskilling initiatives voluntarily. Recognize and reward employees who actively seek opportunities to enhance their knowledge and expertise in AI and automation.
  6. Mentorship Programs: Implement mentorship programs where experienced workers can guide and support their colleagues in learning about AI and automation. Peer-to-peer knowledge sharing can accelerate the adoption of new skills.

Cultural Inertia

  • Parts of the industry have traditionally low technology adoption. Changing mindsets and processes will be challenging.

One of the significant challenges hindering the widespread adoption of AI and automation in the construction industry is the cultural inertia that exists within many construction firms. This refers to the resistance to change and the reluctance to adopt new methods, technologies, or processes. The construction sector has traditionally relied on tried-and-tested methods that have been used for decades, making it resistant to embracing disruptive innovations like AI and automation. Several factors contribute to this cultural inertia:

  1. Risk Aversion and Fear of the Unknown: Many construction companies are risk-averse, preferring to stick to familiar practices rather than venturing into the unknown. AI and automation represent uncharted territory for some firms, and the fear of potential failure or negative outcomes can deter them from exploring these technologies.
  2. Lack of Awareness and Understanding: Some construction professionals may not fully comprehend the capabilities and benefits of AI and automation. They may perceive these technologies as complex or irrelevant to their operations, leading to a lack of motivation to adopt them.
  3. Organisational Structure and Hierarchical Barriers: Larger construction companies with hierarchical structures might face challenges in implementing AI and automation due to the need for approvals from multiple layers of management. This bureaucratic process can slow down decision-making and implementation.
  4. Dependency on Traditional Expertise: In some cases, construction firms heavily rely on the expertise and experience of their seasoned workforce. The integration of AI and automation might be perceived as a threat to these workers’ roles, leading to resistance.
  5. Time and Resource Constraints: The construction industry is often characterised by tight schedules and budget constraints. The initial time and financial investment required to implement AI and automation can be seen as a burden, especially for smaller companies.

Overcoming Cultural Inertia:

To overcome cultural inertia and foster a culture of innovation and adaptability, construction companies can take several proactive steps:

  1. Leadership Buy-In and Vision: Leadership plays a crucial role in driving cultural change. Executives and management should demonstrate their commitment to embracing technology and innovation. They should articulate a clear vision for integrating AI and automation and communicate its benefits to all levels of the organisation.
  2. Pilot Projects and Proof of Concepts: Implementing small-scale pilot projects can provide tangible evidence of the benefits of AI and automation. Successful pilot projects can serve as examples to motivate employees and build confidence in these technologies.
  3. Employee Education and Engagement: Educate employees about AI and automation, dispel myths and misconceptions, and address their concerns. Encourage open discussions about the potential impact of these technologies and invite feedback from the workforce.
  4. Gradual Implementation and Training: Rather than attempting a radical overhaul, introduce AI and automation gradually into existing processes. Provide comprehensive training and upskilling programs to equip employees with the necessary skills to work alongside these technologies.
  5. Championing Innovation: Identify and nurture internal champions of innovation. Empower employees who show enthusiasm for adopting new technologies and encourage them to take the lead in driving change within the organisation.
  6. Collaboration and Knowledge Sharing: Foster a collaborative environment where employees can share their experiences and ideas related to AI and automation. Learning from each other’s successes and challenges can promote a culture of continuous improvement.
  7. Recognizing and Rewarding Innovation: Acknowledge and reward employees who actively contribute to the successful implementation of AI and automation. Recognition can incentivize others to participate and contribute to the adoption process.

Liability and Regulation

  • Automation requires clear assignment of legal liability. Insurance and regulations will need to catch up.

Liability and Regulation:

·        Another critical challenge that the construction industry faces in adopting AI and automation is the issue of liability and regulation. As these technologies become more prevalent on construction sites, questions arise regarding who is responsible in case of accidents or errors involving AI-powered systems. Additionally, the existing legal and regulatory frameworks may not adequately address the complexities and unique considerations associated with AI and automation in construction.

·        1. Clarifying Legal Responsibility: The implementation of AI and automation introduces new legal complexities, particularly when accidents or errors occur. In traditional construction scenarios, liability for accidents might be clear-cut, with human operators often held accountable. However, with the introduction of autonomous systems and AI algorithms, the lines of liability become blurred. Determining responsibility can be challenging when the cause of an accident involves the interaction of multiple parties, including humans and machines.

·        2. Insurance Challenges: The insurance industry is also grappling with the implications of AI and automation in construction. Insurers need to assess the risks associated with these technologies accurately and determine appropriate coverage and premiums. Traditional insurance policies may not adequately address the specific risks posed by AI and automation, requiring the development of new insurance products tailored to the construction industry’s evolving landscape.

·        3. Regulatory Gaps: The current regulatory environment may not fully account for the unique challenges posed by AI and automation in construction. Regulations and standards that were designed for conventional construction methods may not be sufficient for ensuring the safe and responsible deployment of AI-powered systems. Policymakers and regulatory bodies need to update existing regulations or develop new ones that address the potential risks and ensure the ethical use of AI in construction.

·        4. Ethical Considerations: AI and automation in construction raise ethical questions concerning issues like data privacy, algorithmic bias, and the impact on the workforce. Ensuring that AI systems treat all workers and stakeholders fairly and without discrimination is crucial. Transparency in AI decision-making processes and the ability to explain how certain conclusions are reached is essential for building trust in these technologies.

·        5. Interoperability and Standards: Standardisation and interoperability of AI systems are essential for ensuring seamless integration and communication between different technologies on construction sites. The lack of common standards can hinder the widespread adoption of AI and automation, as companies might be reluctant to invest in technologies that are not compatible with their existing systems.

Addressing Liability and Regulation Challenges:

·        Addressing the challenges related to liability and regulation requires collaboration among various stakeholders, including construction companies, technology providers, insurance companies, policymakers, and industry associations. Here are some steps that can help navigate these challenges:

·        1. Collaboration and Information Sharing: Construction firms, technology providers, and regulatory bodies should collaborate to share information about best practices, safety protocols, and risk mitigation strategies related to AI and automation. Knowledge sharing can lead to the development of comprehensive guidelines and standards for the responsible use of these technologies.

·        2. Industry-specific Regulations: Policymakers need to work closely with industry experts to develop regulations that are tailored to the unique needs and challenges of AI and automation in construction. These regulations should strike a balance between encouraging innovation and ensuring safety and ethical use.

·        3. Insurance Solutions: The insurance industry should actively engage with construction companies and technology providers to design insurance products that adequately cover the risks associated with AI and automation. Companies should seek specialised insurance coverage that accounts for potential liabilities arising from the use of these technologies.

·        4. Transparent AI Systems: Developers of AI algorithms and systems should prioritise transparency in their technologies. AI systems should be designed to provide explanations for their decisions and actions, ensuring that stakeholders understand how and why certain outcomes are reached.

·        5. Education and Training: Construction professionals should receive training on the proper use and interaction with AI and automation. This includes understanding the limitations of AI systems, how to interpret their outputs, and how to intervene in case of unexpected situations.

·        6. Test and Evaluate: Before widespread deployment, construction companies should thoroughly test and evaluate AI and automation systems in controlled environments. Conducting pilot projects can help identify potential issues and areas for improvement, reducing the risks associated with large-scale implementation.

Key Technologies Driving Change

Several key technologies are poised to drive AI and automation adoption in construction over the next 5-10 years:

1. AI and Machine Learning

AI algorithms will be integrated into design software, project planning tools, equipment, and more to optimise workflows. Machine learning applied to massive datasets will uncover new efficiencies.

AI and machine learning technologies are set to revolutionise the construction industry by optimising various processes and workflows. Integrated into design software and project planning tools, AI algorithms can analyse vast amounts of data to generate precise and efficient construction plans. By learning from historical project data, AI can suggest innovative design solutions and identify potential risks, ultimately leading to more cost-effective and sustainable construction projects. Furthermore, machine learning’s ability to process large datasets can help construction companies make data-driven decisions, improve project timelines, and enhance resource allocation.

2. Computer Vision

Cameras with computer vision algorithms will monitor job sites and equipment to prevent accidents, detect defects, track progress, and analyse productivity.

Computer vision technology is set to transform construction site monitoring and management. Equipped with cameras and advanced algorithms, computer vision systems can detect potential safety hazards, track construction progress, and identify defects in real-time. This technology enhances safety by automatically identifying workers who may not be wearing proper safety gear or entering restricted zones. Additionally, computer vision can improve quality control by comparing completed work against design plans, ensuring adherence to specifications. By providing real-time insights into construction site activities, computer vision improves project oversight, minimises delays, and enhances overall productivity.

3. Digital Twins

High-fidelity digital twins of buildings and infrastructure will enable better design, construction sequencing, progress monitoring, maintenance, and more.

Digital twins are virtual replicas of physical buildings or infrastructure, enabling real-time monitoring and analysis throughout the construction lifecycle. By creating high-fidelity digital twins, construction professionals gain a comprehensive understanding of the project’s progress, performance, and potential issues. This technology allows for more accurate design decisions, simulation of construction sequences, and early detection of potential flaws before they manifest in the physical world. Digital twins also enable predictive maintenance, ensuring that buildings and infrastructure are well-maintained and optimised for long-term use.

4. Collaborative Robotics

Robots and humans will increasingly work together performing tasks like material handling, welding, bricklaying, assembly, and more.

Collaborative robotics, also known as cobots, are designed to work alongside human workers in a shared workspace. In the construction industry, cobots assist with labour-intensive tasks, such as material handling, welding, bricklaying, and assembly. By augmenting human capabilities, collaborative robots enhance productivity and alleviate physical strain on workers. These robots can be easily programmed and reprogrammed to adapt to different construction tasks, making them highly versatile tools on the job site. The integration of collaborative robotics in construction enables a more efficient allocation of labour, leading to faster project completion and reduced labour costs.

5. Autonomous Vehicles & Equipment

Self-driving haul trucks, excavators, cranes, and other heavy equipment will automate mundane and dangerous construction tasks.

Autonomous vehicles and equipment are set to revolutionise construction by automating tasks that were traditionally performed by human operators. Self-driving haul trucks, excavators, cranes, and other heavy equipment can efficiently transport materials and perform repetitive tasks, significantly increasing construction productivity. Autonomous vehicles and equipment also contribute to safer job sites by reducing the risk of accidents caused by human error. As these technologies continue to advance, construction companies can expect to see substantial improvements in efficiency, cost-effectiveness, and overall project timelines.

6. Wearables & Sensors

Smart hardhats, vests, boots, gloves and other gear with embedded sensors will monitor worker safety and productivity while on the jobsite.

Wearables and sensors are poised to enhance worker safety and productivity in the construction industry. Smart hardhats, vests, boots, gloves, and other gear embedded with sensors can monitor vital signs, detect fatigue, and assess environmental conditions on the job site. These devices provide real-time data to construction managers, allowing them to ensure the well-being of their workforce and proactively address potential safety risks. Additionally, wearable technology can track worker movements and analyse productivity levels, offering valuable insights into workflow optimization and resource allocation.

7. AR/VR

Augmented and virtual reality will be used for immersive training, remote collaboration, hands-free work instructions, design visualisation, and pre-construction planning.

Augmented and virtual reality technologies are transforming the way construction projects are planned, designed, and executed. AR/VR allows construction professionals to visualise 3D models of buildings and infrastructure, facilitating better design collaboration and decision-making. With AR/VR, stakeholders can experience immersive virtual walkthroughs of construction sites before the physical construction begins, allowing them to identify design flaws and make necessary adjustments. These technologies also support remote collaboration, enabling project teams to communicate and coordinate effectively, regardless of their geographical locations. By improving communication, design visualisation, and pre-construction planning, AR/VR streamlines construction processes and reduces the likelihood of costly errors and rework.

Skills Needed for the Future

To leverage AI and automation, the construction workforce will need new skill sets:

  • IT literacy and comfort learning new software/tech
  • Working alongside robots and autonomous equipment
  • Data analysis
  • Technical troubleshooting and maintenance
  • Cybersecurity
  • Digital collaboration tools

Construction workers will also need “human” skills that computers lack:

  • Problem-solving
  • Creativity
  • Communication
  • Leadership
  • Initiative
  • Adaptability

A mix of new training programs, on-the-job learning, and culture change will be needed to build these skills across the industry.

The Outlook for Jobs in the construction with Ai

The question of how AI and automation will impact construction jobs remains controversial. Studies show a range of possibilities:

  • Most optimistic: Near-total automation could boost productivity so much it largely offsets displaced jobs.
  • Most pessimistic: Robots could eliminate ~2 million construction jobs worldwide by 2057, according to one projection.
  • Middle scenario: New jobs in technology, maintenance, and human-robot collaboration may balance out lost manual labour jobs.

In any case, the industry will need to proactively train workers for new roles that emerge. There are also calls for governments to provide more support through transition periods.

Key Takeaways

In summary, while AI and automation adoption is still early-stage, rapid transformation of the construction industry seems inevitable. As the technologies improve and costs come down, implementation will accelerate.

Firms that want to remain competitive will need to become early adopters or risk being left behind. However, for the industry as a whole to enjoy the many benefits promised by AI and automation, barriers like costs, training, and cultural resistance will need to be deliberately dismantled through cross-industry collaboration.


[1] McKinsey, “Where machines could replace humans—and where they can’t (yet),” July 2016.

[2] BCG, “Automation in Construction,” July 2019.

[3] World Economic Forum, “An Action Plan to accelerate Building Information Modeling (BIM) adoption,” January 2019.

[4] Construction Leadership Council, “Roadmap to Recovery: The Industry Recovery Plan for the UK Construction Sector,” June 2020.

[5] UK Green Building Council, “Climate change,”

[6] Construction Innovation Hub, “Robotics and Automation: Why the industry needs to change,” July 2018.