Harnessing generative AI in construction: reducing overheads in the design and planning phase

16 May 2024

In an industry like construction, where low margins leave little room for error, accurately forecasting financial outcomes is imperative. The early design and planning stages of a project therefore become key, with the hours spent assessing the project’s viability, allocating resource and funding, mitigating risks, and managing schedules, quickly racking up. 

Given these challenges, and considering that our Real Estate 360° report showed a significant proportion of businesses had already invested in generative AI or planned to in 2024, it’s logical to explore how the industry could best implement generative AI at these vital design and planning stages – to reduce overheads and, ultimately, boost financial outcomes. 

The design and planning stage: applying generative AI technology

The design and planning phase of a construction project, typically accounts for a significant portion of the overall project cost. Though varying from project to project, you could expect to attribute 1.5% of the overall spend to design costs, 7-12% to architect fees, and 5-15% to planning. 

So, how could generative AI technology help?

Using generative AI to automate designs

Generative AI allows businesses to leverage algorithms that learn from historical data – meaning organisations can automate the creation of various design options for each phase of a project. Most importantly, this not only speeds up design creation, but optimises designs based on successful outcomes from previous projects – allowing the incorporation of historical insights and efficiency into the creative workflow. 

Instant changes can be made to the scope and technical outputs of a project, enabling design teams and contractors to respond more efficiently to customer enquiries. 

Through the use of 3D infra-red technology, drones, robots, and wearable devices, the industry can now gather data from a variety of locations, including spaces previously inaccessible to humans. This wealth of data sources allows businesses to gain a more comprehensive understanding of the environment. It also enables them to design the most efficient and sustainable outputs, tailored to their customers’ needs.

Access to this data enables collaboration within the supply chain, too. Generative AI can quickly identify design issues ahead of the build. Organisations can then share this insight and information with their supply chains, allowing them to respond to any necessary changes. 

Using generative AI to enhance project cost predictions

One of the industry’s biggest challenges, particularly in the last four years, has been managing rising costs and shortages of materials and labour. This has been further exacerbated by the industry’s data capture practices, which are often slow to incorporate changes and require human intervention to forecast costings and outcomes.

Through generative AI, however, data capture and storage now enable businesses to swiftly adjust contract costings, apply complex pricing models, and adapt flexibly to contract design changes and emerging issues. 

As generative AI continues to learn and evolve, the data it holds will allow businesses to easily predict and better understand the costs of a project, based on previous experiences and real-time data sources. 

How will generative AI drive unification, customisation and margin growth in new developments? 

In a world full of data and predictive outcomes, the sector is contemplating whether this creates an opportunity to unify projects, thereby reducing the number of complex builds, driving efficiency and stabilising margins. Alternatively, it might provide customers with the opportunity to further customise their asset design and build, thanks to a better understanding of outputs such as pricing, delivery, and ESG. 

For many industries, unifications and limitations of product lines enable predictive outcomes and management of outputs, such as material and service procurement, labour resourcing, and margin forecasting. 

Consider the automotive industry, where model types are tailored at the design stage and there’s a precise understanding of the cost to build and deliver that product. Could the construction industry create more unified products with an understanding of what is efficient and yields the highest margins? 

Increased customer interaction at the design stage, facilitated by technology and the presentation of digital visuals and predictable outputs, will likely encourage the customer to further customise their product. While this may introduce additional complexities, with real-time data on viability and costings, the industry may be in a position to significantly increase margins. This is a result of the customer understanding the challenges for the build and the true cost to deliver to their needs and expectations. 

What’s next? Five steps to building a data strategy

Across real estate and construction, many disparate systems are used. This presents challenges in accessing, governing and having confidence in data. Around 60% of industry leaders stated their existing data capture systems were not adequate for the use of AI. So, what steps can businesses take to build an effective data strategy?

  1. Establish strategic objectives – what are the business goals and how will you measure progress?
  2. Analyse your data usage – what data is currently available? How is it used and governed?
  3. Measure your talent – what resources and skills are available in your team? How can you upskill your talent to ensure the business is fit for a data-focused future?
  4. Evaluate your applications – what systems are available, what data do they contain? Is your data locked into a central resource or is it readily available to be used by your SMEs who know it best?
  5. Assess your technology – would the business benefit from a unified data platform offering a single source of truth? What reporting tools do you have and what do you need? 

How will generative AI impact the construction industry in the future? 

Currently, the industry heavily relies on architects, design specialists, project managers, and manual workers. In the coming decades, these roles could be performed by generative AI. Customers might use generative AI to design their own buildings and assets, while technology could oversee project management and performance reporting, providing real-time activity updates from the comfort of their homes and offices.

The question then arises: will designs and builds have a limited selection of unified products, or will we see a world of bespoke products and tailored services? Both scenarios offer the industry the opportunity to enhance margins and customer interaction through a deeper understanding of delivery costs and other outputs. Only time will tell which direction the industry will take. 

To discuss the business issues identified in this article or for help in building an effective data strategy, contact Kelly Boorman or Sarah Belsham. 

Real estate 360 2024

We present the perspectives of over 150 real estate business leaders, from economic challenges to tech and AI, and the increasingly essential role of ESG.

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