Three Lessons For Building Trust In Construction AI

Francesco Iorio is co-founder and CEO of Augmenta, a company automating building design for the construction industry.

​Artificial intelligence is taking on increasingly complex work in engineering, manufacturing, transportation and other safety-critical industries. But as the technology advances, verification, certification and regulation are struggling to keep pace.​

Generative AI, in particular, needs to be treated as a system operating under uncertainty. Its power lies in its ability to rapidly explore countless ways to answer a question or solve a problem, considering far more possibilities than a human could on their own. But greater possibility also introduces uncertainty. To fully maximize the value of generative AI, we need to first focus on validating and verifying the processes that produce results, and then the outputs themselves.

​We’ve seen this tension between technological capability and trust play out before, and autonomous driving offers a useful roadmap for what construction and other industries where technology has real-world, physical consequences can expect.

​The Pattern We’ve Seen Before

​Every transformative technology has faced a “trust gap” before widespread adoption. Most people assume that as technology improves, trust will follow and oversight gradually lessens. Autonomous driving has shown that the reality is more complicated.

​As autonomous vehicles became more capable and moved from controlled testing environments onto public roads, they attracted greater scrutiny. The closer the technology came to operating independently, the more questions arose around validation, liability and regulation. In other words, new technology did not eliminate the need for oversight; instead, it raised the stakes for proving that the technology should be trusted.

​That starts with rethinking how we build the technology, the role we ask people to play and the standards the industry sets for itself.