Generative AI: taking building technology to new heights – ADC

It’s no exaggeration to say that generative AI will have a transformative effect on global industry. With the ability to generate new outputs based on algorithms and trained data, it represents the next step in artificial intelligence and a new level of sophistication for machine learning. Predictions are already appearing around its economic value. According to the FT, it’s set to provide a boost of seven per cent to global GDP, affecting over 300 million jobs.

While the true impact of this technology is yet to be seen, for architects, it marks a turning point in the sector’s ‘digital journey’. Increased efficiencies, improved 3D modelling, and enhanced user experiences are just some of the ways in which it will benefit. Its influence shouldn’t be understated, this could be its very own ‘typewriter’ or rather ‘smartphone’ moment.

Yet in order to understand where specifier-focused technology is heading, it’s important to see how far it’s come. In the space of just 50 years, digital adoption has gained considerable momentum. As a digital leader, NBS, has been at the forefront of evolving technology, influencing the way specifications are managed. Even at inception, the importance of linked information was paramount – binding together drawings, specifications, product, and cost information. Look to the modern day, and these elements are now gathered through information and specification platforms, streamlining the entire design process.

So, what’s next? Well for one, we should recognise that ‘natural language’ processing tools (NLP), such as ChatGPT, aren’t putting architectural jobs at risk. Instead, I see these tools being used to support, rather than replace. Research by OpenAI, the creator of GPT-4, suggests that 80 percent of the US workforce could see ten percent of their tasks performed by generative AI.

From a communications standpoint, I see AI language tools playing a key role in processes, improving helpdesk support and customer service response times. Yet the technology’s potential extends much further. Through improved deep-machine learning, it could be used to draw upon existing data sets and product information for a quicker, more efficient specification process.

For example, it can help when sourcing the right products and materials for any project – a process that is extremely time-consuming for specifiers. However, fine-tuned NLPs can slash research time, suggesting the best product matches and even answering questions relating to product performance.

It can also reduce risk. AI can recognise when certain products or materials are incompatible with specific systems. Identifying this in the early design stages can then reduce the need for costly rework further down the line. Search terms are also vastly improved through the ability to understand natural language. This helps specifiers find information at a faster pace and with more accuracy.

Ultimately, tools such as ChatGPT improve the overall decision-making process. Systems can alert specifiers to a product’s feature or availability, ensuring that they are up to date with the latest product information. At a time of difficulty around supply chains, this can help ensure that products are available when architects need them.

Finally, it could also provide solutions to the sector’s almost limitless thirst for data. Today’s construction sites are hotbeds for tech, and AI that analyses data faster and with more insight is in high demand. Deployed correctly, it could deliver improvements to energy usage, carbon calculations, and on-site safety and security.

However, we must remember that each of these elements relies on one key ingredient: human review. No matter how advanced, construction technology can only take us part of the way. It still takes highly-skilled minds to fact-check, analyse and understand the nuances required for each stage of construction. After all, businesses are only as good as their people, not their data.

We must also be clear about the data feeding the machine. In the case of ChatGPT, it was trained on information available during 2021. If we’re serious about using generative AI in a commercial sense then we must ensure that the data in question is up to date and in line with the latest laws and regulations. Again, it highlights the importance of human input. While potentially transformative, generative AI has no understanding of the outputs they produce. In the end, it will be the human touch that makes all the difference.

Contact Details

For more information please call 0345 456 9594, email or visit the NBS website.

Russell Haworth is CEO of construction technology company NBS, and UK CEO of Byggfakta Group. He is also author of the recently published book ‘Thoughts from the Big Chair: A Leader’s Guide to Digital Transformation’ which is available to purchase on Amazon.