Beyond AEC, other industries are leveraging automation and machine-learned intelligence to support decision-making, reduce repetitive tasks, increase quality, and boost overall efficiency.
The use of machine learning or the wider field of AI has seen limited use in the AEC industry, or the tools we use. This is partly down to the relatively slow digital transformation of the industry into a data-driven sector, but is also down to the limited integration of such technologies into AEC software.
Despite this, the AEC industry is rife with repetitive processes and reinvention of the wheel from project to project. Across the portfolio of an AEC firm, experience and knowledge is often reapplied on each project from first principles, without any thought given to digitisation and subsequent automation.
Design tools with the capability for automation where appropriate and the ability to leverage AI at scale and responsibly as and when possible.
Design tools should have the ability to leverage design data for automation, machine learning and, eventually, generative AI. In the short team, design teams should have the ability to easily automate repetitive tasks that they face without relying on developers and computational experts.
Beyond automation, design tools should have the ability to harness intelligence – whether knowledge from within an organisation, external sources, or data harvested from other projects or through software use – and feed this back to project teams. For the majority of AEC firms the potential for AI is less about automated design generation, but rather through automated design intelligence.
To safeguard our teams, any implementation of artificial intelligence needs to be transparent and responsible, allowing teams to leverage AI securely and with no loss of liability.