Net zero, not zero sum
When we hear or read about Artificial Intelligence, it provokes various reactions based on a varied understanding of what it means and the context in which it is used. Some may see it as an opportunity, others a threat – or maybe a lack of understanding means we do not know what to make of it. At a basic level, Artificial Intelligence, or AI, refers to the capability of machines to imitate human intelligence. This may be learning from data, understanding spoken and written language, recognizing patterns, problem solving and making decisions. Wherever AI is used, the aim is to perform tasks that typically require human intelligence.
The pace at which AI is developing is impressive, but in practice would be more aligned to those who think of it as an opportunity rather than a threat. Automation, speed and efficiency of business processes mean AI does not seek to replace human engagement but merely enhance it. When we explore AI’s current and possible future utilization within the property and construction industry, we can see how it can help achieve Net Zero targets without engaging in a zero-sum game.
Machine learning
A specific characteristic of AI is its ability to learn from data and provide an outcome without being specifically programmed to do so. Commonly known as Machine Learning (ML), this concept has been around for decades, and machines now can interpret large quantities of multi-layered, complex data. Once a basic functionality, ML is now at a stage where it has advanced sufficiently to create the perception that machines are thinking for themselves. It is also the most applied aspect of AI that businesses and organizations look to take advantage of.
Examples of ML are all around us. If you are reading this article on a laptop, you may see adverts recommending products generated based on your recent browsing history, demographic or location. Your email inbox uses ML to filter unwanted messages and spam. You may use facial recognition to unlock your phone. All these outcomes derive from ML’s capability to interpret data, identify patterns and increase optimization. Within the property and construction industry, we can identify several opportunities to utilize ML to improve efficiency and reduce the sector’s carbon intensity.
Optimizing the asset life cycle
Unlike in the management of financial assets, in the built environment, past performance can be an accurate indicator of future results. Using data to analyze the performance of current buildings and equipment, ML would recognize patterns that consider numerous factors to provide highly detailed, objective-led recommendations quickly and according to a particular project brief during the design stage. Guiding the design team to make decisions based on a greater understanding of future performance not only helps to maximize the life span of a built asset, which helps delay the need for new buildings, but can also help to optimize energy efficiency according to available budgets or help to select materials that meet the design brief but are less carbon-intensive to procure. Targeting the reduction of carbon in the design stage and helping to maximize the life spans of assets is one of the most effective ways of reducing the carbon footprint of the property and construction sector, as up to 40 percent of emissions are incurred ‘upfront’ before the handover of assets to their end users.
Low-carbon, high-performance buildings are not a new concept, as AECOM’s High Performing Building Pathway has demonstrated. With a design mindset and an innovative approach to the design process, High Performing Building Pathway considers whole-life carbon, cost, construction, handover and occupation in every stage of the building’s development to increase efficiency and improve performance. The potential for integration of AI into this process is exciting, as opportunities to design for lower carbon footprint buildings will surely benefit from the vast amount of project data already available, which no project team could fully comprehend. For example, predictive modeling may be used to develop the design and produce digital twins that can eventually be handed over to the end-user or operations team, promoting more efficient maintenance practices.
Integrating AI into the project manager’s function during the planning and construction stages may also provide learning from previous, similar projects and identify risks early enough to allow mitigation or even suggest effective mitigation practices based on what has worked before. The obvious benefit is a reduction in program overruns and their associated cost. It also helps to reduce wastage and resource redundancy that would otherwise contribute to the carbon impact of a project. When on site and during the construction stage, further opportunities present themselves with the implementation of real-time project monitoring that alerts workers before accidents occur and reduces the need for site visits, including travel associated with obtaining construction updates.
In the operational phase of an asset’s life cycle, AI can help reduce carbon emissions by optimizing maintenance activities and equipment use. Smart buildings are already a reality, where system performance can be monitored and action taken to reduce energy use, for example. AI can use the data that smart buildings generate to learn and then apply solutions in real-time, even identifying issues and taking action before they become major problems by accessing building management systems. In this way, smart buildings will become smarter.
Even at the end of an asset’s life, when disposal occurs, the adoption of AI to identify opportunities for recycling and reuse of building material and equipment can present detailed recommendations for an asset owner to extract any final residual value and offset carbon emissions anywhere material or equipment can be repurposed.
Challenges
As with any innovative solution, while the multiple potential applications of AI in the construction industry present opportunities to save cost and increase productivity, they must be weighed against the investment needed in the technology and associated training to implement them effectively. The property and construction industry is one of the least digitalized industries in the world. Therefore, implementation costs are relatively high and a business case needs to be made. The effectiveness of AI and its reliance on data is also problematic when the industry lacks significant quantities of digital data and construction projects become more complex.
Where data can be accessed, data sovereignty issues and questions on confidentiality arise. How does AI differentiate between public and private information, and once used, how can any generative outcome from AI be communicated or utilized without breaking data protection rules? This is currently one of the grey areas in the adoption of AI and the biggest challenge for any organization to overcome.
Other challenges that apply to all organizations looking to benefit from adopting AI include a lack of technical expertise, poor data quality and integration with existing systems. Without significant digitalization, the property and construction industry will find it hard to adopt AI, not just because of lack of data – or access to data – but also because of possible shortages in the necessary skills needed to implement and take advantage of potential AI applications. A lack of understanding makes it hard to make the case for adoption and businesses are generally finding it hard to realize the full potential of this technology.
Low-carbon, high-performance buildings are not a new concept, as AECOM’s High Performing Building Pathway has demonstrated.
Middle East application
In October 2017, recognizing the importance of AI, the UAE launched its Strategy for Artificial Intelligence, which later became the National Strategy for Artificial Intelligence 2031. Since then, examples of its actual use have been limited as organizations and companies appear to still be within the exploration stage. More recently in Dubai, Emaar’s new project, Dubai Square, is reported to be using AI in the planning, design and construction process, while Dubai Municipality has launched the ‘Using Artificial Intelligence to Detect Building Violations’ project to help it monitor construction sites as part of its wider ‘Artificial Intelligence Roadmap’ (2024). In April 2024, it was reported that Abu Dhabi’s Department of Municipalities and Transport (Abu Dhabi DMT) launched two AI solutions to make the building permit process more efficient in the Emirate, including AI-enabled BIM and an automated plan review system with a virtual assistant chatbot.
Further afield, Saudi Arabia has recognized the challenges surrounding data sovereignty and has developed several pieces of legislation to lay a foundation for AI implementation, including a National Framework for AI in Digital Learning. In September 2024, Saudi Arabia set a target for AI to contribute 12 percent to the country’s GDP by 2030. It is hard to imagine this will be achieved without significant adoption in its property and construction sector as the numerous giga projects in the Kingdom would appear to offer plenty of opportunity to do so.
At present, the examples of actual implementation appear to be oriented toward saving time and automating business processes, which will have a limited impact on decarbonization. They are nevertheless positive indicators of a willingness to engage with the technology and can only help to drive larger efficiencies that will have greater environmental consequence.
Future developments
There will be a balance between how people see AI, from identifying quick wins that can be implemented relatively easily, cost-effectively and at lower risk to the longer-term adoption of practices requiring higher investment levels. The construction industry is one of the least digitalized industries in the world, and the companies that are more heavily digitalized stand to benefit more from the adoption of AI. This presents a challenge to the industry but may also be another incentive to modernize. The opportunity cost of not adopting AI throughout the asset lifecycle would be measured not only in financial terms but also in terms of the environmental impact. Accounting for around 40 percent of total global emissions, the property and construction sector has a responsibility to identify ways and take measurable action to reduce its carbon footprint. In a world increasingly reliant on the built environment to support growing populations, delivering assets and infrastructure in a quicker, safer, cost-effective and less carbon- intensive way will catalyze societal development.
In a world increasingly reliant on the built environment to support growing populations, delivering assets and infrastructure in a quicker, safer, cost-effective and less carbon-intensive way will catalyze societal development.