The construction industry presents unique challenges for data collection and processing with AI. AI tools typically require large amounts of data for machine learning, which is sourced from various points across the construction landscape. This data often includes information from machinery and equipment, provided in various formats with different protocols and payloads.

Construction sites frequently operate in remote locations and have rapid mobilisation requirements. Coupled with the lack of connectivity typical in office environments, these factors create a challenging landscape for data collection.

There is a notable shift in thinking among customers and contractors, who are now collaborating to establish shared networks based on 4G/5G technologies. These networks offer significantly broader coverage compared to Wi-Fi and greater flexibility for data transmission.

Additionally, there is a trend towards decentralising compute power by deploying smarter devices and equipment to the edge. This shift will unlock further opportunities for AI integration.

Data Governance involves ensuring that companies avoid the “garbage in, garbage out” dilemma by managing data effectively to ensure its fitness for purpose before utilisation.

Data Security concerns whether AI operates in isolation and who determines the access levels granted to AI tools. In this fast-evolving landscape, initial versions of AI tools may offer limited control to data owners, raising privacy and security issues.

Lack of Visibility is a significant challenge with AI solutions, as the AI engine relies on past data to make decisions. Trust in AI decision-making is crucial, but questions arise regarding biases and the ability for humans to intervene to ensure fairness and ethical decision-making.

Incident Management strategies must be robust to address potential errors made by AI tools. Companies need to anticipate the impact of such errors on their reputation and compliance with regulations.

As AI becomes integral to operations, companies require personnel with AI skills, such as prompt engineering and AI response validation. Additionally, individuals capable of making responsible decisions and taking a hands-on approach to AI implementation are essential.

In summary, AI presents both opportunities and challenges in construction. Overreliance or overexposure to AI can have serious implications for businesses. Successful adoption requires testing, learning, and cautious implementation, along with appropriate safeguarding measures.