This article is about AI and predictive building maintenance. It also reads like an IBM corporate press release, but nonetheless it sparks some interesting thoughts. Recently I was at a conference where a friend of mine was on stage and as asked what technologies would be most important for the future of public infrastructure (water infrastructure, in the case of this particular conference.) AI and asset management came to my mind, and I willed my friend to also think of this. Alas, he did not. Now, if I had been up there would I have been able to articulate my thoughts clearly on the spot? Probably not, but with the benefit of a few minutes to think here is what I fantasize I might have said.

Basically, AI should be pretty good at asset management. Given good data on assets and their ages, they should be able to identifying assets (we’re talking physical assets here, like pipes or electrical equipment, or even green infrastructure like street trees) that are nearing the end of their service life and likely to fail in the engineering sense of no longer serving their intended purpose efficiently. Or, somewhat obviously, when things really have failed AI can help get that information to the attention of whoever can actually do something about it. Well, I still think humans have to do the up-front planning and have some vision for what they would like the infrastructure system to look like 20, 30, 50 years down the line. But then, AI should really be able to help with those repair-replace-upgrade-abandon decisions, so that as things wear out the system is slowly nudged in the direction of that long-term vision, all while minimizing life cycle cost and balancing whatever other objectives the owners or stakeholders might have. This all looks good on paper and is messy to do with a mish-mash of real-world governments and institutions and companies, but having the vision is a start.