Opportunities and Challenges of Generative AI in Construction Industry: Focusing on Adoption of Text-Based Models

TLDR

Construction has lagged behind AI adoption despite rapid advances, yet the rise of large language models such as GPT, PaLM, and Llama has generated significant interest and potential for the industry. The study aims to fill the knowledge gap by investigating the opportunities and challenges of integrating generative AI into construction, guiding future adoption and research. This study analyzes literature perceptions, industry views via word‑cloud and frequency analysis, and incorporates authors’ opinions to address the guiding questions. The paper recommends a conceptual GenAI implementation framework, offers practical recommendations, outlines future research questions, and establishes foundational literature to support further research in construction and related fields.

Abstract

In the last decade, despite rapid advancements in artificial intelligence (AI) transforming many industry practices, construction largely lags in adoption. Recently, the emergence and rapid adoption of advanced large language models (LLMs) like OpenAI’s GPT, Google’s PaLM, and Meta’s Llama have shown great potential and sparked considerable global interest. However, the current surge lacks a study investigating the opportunities and challenges of implementing Generative AI (GenAI) in the construction sector, creating a critical knowledge gap for researchers and practitioners. This underlines the necessity to explore the prospects and complexities of GenAI integration. Bridging this gap is fundamental to optimizing GenAI’s early stage adoption within the construction sector. Given GenAI’s unprecedented capabilities to generate human-like content based on learning from existing content, we reflect on two guiding questions: What will the future bring for GenAI in the construction industry? What are the potential opportunities and challenges in implementing GenAI in the construction industry? This study delves into reflected perception in literature, analyzes the industry perception using programming-based word cloud and frequency analysis, and integrates authors’ opinions to answer these questions. This paper recommends a conceptual GenAI implementation framework, provides practical recommendations, summarizes future research questions, and builds foundational literature to foster subsequent research expansion in GenAI within the construction and its allied architecture and engineering domains.