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January 2026 #18318 Page #188
TheLast Word (When) Will AI Overtake the Truss Industry? Joe Kannapell, P.E.
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rtificial intelligence (AI) has been 70 years in the making since eminent AI pioneer, John McCarthy, coined that term in 1955 to describe “the capability of a machine to imitate intelligent human behavior.” Truss software has been 50 years in the making, with scant penetration of AI technology into truss software over that time. So why, now, does its entrance seem eminent? The primary reason is that we hear a steady drumbeat of AI innovations in movies, music, and so many other facets of life. And we naturally assume that innovation in the truss business will follow this prevailing trend. While we must continue to study AI technology and assess where it may be used to improve our industry, we also need to understand how its development is radically different than prior innovations. AI is typically constructed using vast quantities of industry data – which is something that truss plants, unlike many other businesses, generate daily, adding many millions of computer bytes to the billions of bytes of data that has already accumulated on servers. So, at least our industry has built a basis for the development of AI technology. But is that data easily accessible and does it contain adequate information upon which to base AI efforts? Although the quantity of available truss data may be adequate, the data files themselves must be well organized and properly documented. Typically, organizations that have mastered the filing of repetitive work, like that of national single family and apartment builders, have well organized and identified data files. But there are also companies that undertake highly customized work that have their own excellent computer-based filing systems. The quality of data files is of overriding importance because that is what will be used to “train” an AI system. But truss files normally contain only the estimated labor cost and not the actual labor hours expended, so they will be of limited use only in determining optimal truss configurations, even after examining millions of different designs. Furthermore, such a system needs to include the type of equipment used, so the value of past labor data will be diminished after new automation is added to a plant. Even so, now is the time for plants to increase the usefulness of truss data files by adding actual labor hours expended, which will allow a meaningful reconciliation of actual-to-expected costs for present and future uses. Individual truss data files, when matched with labor data, may aid in automating selection of optimal configurations, but they usually lack information regarding adjacent trusses, unless they are girders. Because trusses are built in batches, a better approach would be for an AI system to be based on the entirety of a job, which would involve using roof and ceiling shapes as a basis. Countless manual efforts have gone into developing such a job costing system, but with limited results. Parameters such as the length of ridgelines and hip-lines, pitches, heel heights, and other geometric features all play into the difficulty of a job. However, the location Continued next page
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