MARCH - APRIL 2026CONSTRUCTIONTECHREVIEW.COM19THE KEYS TO AI TECHNOLOGY LEAPFROGGINGBy Adam Krob, Director of Information Technology, Field Audit and Process Improvement, Boh Bros. Construction Co., LLCThe construction industry continues to hold on to its reputation as a very late adopter of technology. Ber-nadette Salapare in Construc-tion Times outlines seven obstacles to construction technology adoption, from legacy software to a lack of standardization of data types. These obstacles have prevented the industry from realizing the efficiency gains enjoyed by other industries, such as manufacturing or retail sales. With the emergence of artificial intelligence (AI), large language models (LLMs) and intelligent agents, the construction industry has a rare opportunity to leap ahead to a more advanced position relative to other industries.The open question is: what will be different? How will LLMs, chatbots and AI agents succeed where other technolo-gies have not? The difference will have to be how construction companies tackle the human side of the technology productivity equation. There are three critical shifts construction should embrace for this leap: abandoning the insistence on uniqueness, building structured logistics for information and embracing 80 percent functional fit.Abandon the Insistence on Project UniquenessThe belief that every construction project is a unique undertak-ing has deeply influenced how we build systems, processes and data models. It has led us to underappreciate the similarity across projects and has prevented us from developing standardized ways of collecting and using data.While the environments we work in are variable and complex, our approach does not have to focus on the outliers, but on the similarities. Predictable, repeatable processes create the foundation for clean, structured data models that remain consistent across time, projects and teams. This data consistency unlocks meaningful long-term analysis, benchmarking and AI-powered decision support.Abandoning the focus on uniqueness does not require that we ignore complex-ity and nuance. Through techniques like mass customization using LLMs, we can tailor outputs without compromising the structure of data collection. Mass customization approaches will allow large language models to adapt to dif-ferent conditions or requirements while keeping the chat or speech interface the same. These models serve as adaptive layers between humans and systems, ab-sorbing differences in language, context and inputs while maintaining data integrity behind the scenes. The key is to break down project elements into micro-level repeatable tasks and understand what is different, introducing new agents or prompts to collect the correct information and run the appropriate processes. Each project becomes a recombination of pre-generated elements that can adapt to the specific needs of a project, reducing the demand for customized data collec-tion while increasing future predictability. Mass customization requires better and more tightly controlled information flows, which suggests a different approach to data collection.Build a Structured Logistic Approach for InformationIn the construction industry, job buyout, submittal review and delivery management are key components to effectively manage physical logistics. Contracts invest significant time and effort to get physical logistics right. Unfortunately, we do not invest in information logistics, a term I first heard from Oleg Kandrashou, the CEO of Cubby.Data is typically entered manually, such as daily production quantities, then passed through multiple systems before reaching decision-makers. Delays and inconsistencies are common in this data gathering process. The submittal review process illustrates Adam KrobAIINSIGHTSCXO
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