Digital Transformation in the Australian AEC Industry: Prevailing Issues and Prospective Leadership Thinking
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The architecture, engineering, and construction (AEC) industry globally has a long history of prudently adopting novel technologies to improve products and services. Yet the rapid development of digital technology currently taking place is threatening to produce a more disruptive inflection, or substantial jolt. This paper explores the state of readiness of the AEC industry for such anticipated transformation. We illustrate our conceptual arguments with evidence from an explorative study across a sample of AEC organizations in Australia. At the core of this paper, we offer six provocations that highlight what we consider major challenges for the AEC industry—across multiple levels of analysis—related to the increasing role of digital technology. We then turn to lessons learned from other industries in order to propose a framework consisting of four leadership thinking schemas to enable digital transformation readiness: future thinking, strategic thinking, capability thinking, and experimental thinking. For these four schemas, we present practices and initiatives that may help AEC firms to better adapt—or to proactively create and shape a sustainable future.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it