One of the most challenging parts of the architecture process is accounting for myriad factors from the very start of projects. If a curveball comes after the initial design phase is over — say there’s a misunderstanding with the budget and now some of the building materials are no longer in scope — it can throw a project off the rails.
Because generative AI is trained on huge volumes of data that can encompass site constraints, budget, construction materials, code requirements and beyond, it can help architects and their teams account for potential problems ahead of time and get everyone back on track (and budget) faster. Plus, having access to this type of rich data can allow architects to spot inefficiencies and opportunities they might not have otherwise, which can help architects get the most out of the space and make the best use of the materials — a huge plus for sustainability.
It’s important to remember that, while generative AI gives architects “superpowers,” it’s no replacement for critical thought and human empathy. The tremendous datasets that generative AI applications are trained on still can’t account for everything — especially the cultural, personal and community impact of built environments.
Remember: generative AI is a collaborator, not a replacement for good thinking and human creativity.