Generative Artificial Intelligence in AEC Organizations: A Literature and SWOT Analysis
Bibliographic record
Abstract
Generative artificial intelligence (GenAI) is seen as an efficient way to enhance architecture, engineering, and construction (AEC) organizations through the generation of novel content and the automation of processes and tasks. Rapid advancement in GenAI is giving rise to new potential applications in AEC organizations, and this requires a detailed understanding and a strategic planning technique to evaluate the usefulness of GenAI to organizations. This study aims to address this gap by examining GenAI literature in the AEC industry to determine how GenAI contributes to the enhancement of AEC organizations. To achieve the aim, this article identifies how GenAI is used in AEC organizations, and proposes a strengths-weaknesses-opportunities-threats (SWOT) process and elements for the strategic evaluation of GenAI in AEC organizations. The research methodology consists of a systematic review process. The findings categorized 79 journal articles and conference papers, which revealed that GenAI was being explored to create architectural and structural designs of buildings, optimize construction processes, and enhance risk management and work safety. There are limitations in the capability of GenAI models to meet project management needs. These include low quality datasets used for training, cost, time, and computation resources required to implement GenAI effectively, and ethics and privacy issues when using GenAI in AEC organizations and projects. Additionally, a SWOT process is proposed, and twenty SWOT elements are developed and described: four elements for strengths, five elements for weaknesses, five elements for opportunities, and six elements for threats. The proposed elements aim at providing a foundation for AEC organizations to assess and achieve their strategic GenAI implementation goals. This article identifies the areas of research focus along with a SWOT process and elements that may be useful to researchers and industry practitioners focusing on GenAI and SWOT in the AEC industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.025 | 0.029 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".