Over the past decade, while artificial intelligence (AI) has rapidly transformed numerous industries, the construction sector has been slow to adopt these advancements. However, the rise of sophisticated large language models (LLMs) such as OpenAI’s GPT, Google’s PaLM, and Meta’s Llama has demonstrated significant potential, sparking widespread global interest. Despite this surge, there remains a lack of research specifically examining the opportunities and challenges of integrating Generative AI (GenAI) within the construction industry, resulting in a crucial knowledge gap for both researchers and practitioners. Addressing this gap is essential to effectively leveraging GenAI during its early adoption phase in construction. Given GenAI’s remarkable ability to generate human-like content by learning from existing data, this study explores two key questions: What does the future hold for GenAI in the construction industry? What are the potential opportunities and challenges associated with its implementation? To answer these questions, the study conducts a literature review, assesses industry perspectives using programming-based word cloud and frequency analysis, and incorporates the authors’ insights. Additionally, the paper proposes a conceptual framework for GenAI implementation, offers practical recommendations, highlights future research directions, and establishes a foundational knowledge base to support further exploration of GenAI in construction, architecture, and engineering disciplines.

generative AI; implementation framework; construction; AEC; GPT; LLM; PaLM; Llama; fine-tuning.

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