The overlooked frontier of AI in construction: conversational, document-native automation for administrative workflows

Abstract

Research on artificial intelligence (AI) in construction over the past two decades has been dominated by sensor-driven applications such as safety analytics, progress monitoring, and sustainability modeling. These domains have benefited from abundant IoT, vision, and drone data, reinforcing an Industry 4.0 paradigm centered on physical automation and digital twins. However, this emphasis has left a major gap: the cognitive and administrative workloads that consume substantial professional time remain largely unaddressed. Documentation tasks such as reviewing specifications, assembling submittal logs, and preparing transmittals continue to rely on manual processes, despite their centrality to project delivery. This perspective paper examines this overlooked frontier through a targeted review of AI literature and a bibliometric scan of two decades of publications. The analysis shows that research on documentation automation accounts for only a small fraction of AI-in-construction scholarship compared with sensor-based domains. To illustrate the potential of document-native AI, the paper presents a case study of a submittals automation tool developed and tested with an industry partner. The system extracts submittal requirements from 1,000 to 1,500-page specifications, generates transmittal-ready Excel and Word files, and enables conversational querying. Field testing demonstrated substantial time savings and high user trust, while also revealing opportunities for improved responsiveness and workflow integration. The results show that lightweight, conversational, document-native AI can meaningfully reduce repetitive cognitive work while preserving human oversight. The paper concludes with a research agenda emphasizing reliability, multimodal document understanding, agentic workflow orchestration, and evaluation frameworks that quantify return on investment.