Aiolova, Maria (2026) Building AI fluency in the built environment: Developing organizational learning cultures to drive AI adoption and innovation in the architecture, engineering, and construction industry. EdD thesis, Northeastern University, USA.
Abstract
The purpose of this action research study was to investigate and improve the development of organizational learning cultures that drive artificial intelligence (AI) adoption and innovation within the Architecture, Engineering, and Construction (AEC) industry. This research addresses a critical gap: while AI promises transformative productivity gains, most AEC firms lack the organizational learning frameworks to realize this potential. Using a two-cycle action research design, Cycle 1 examined how an established AEC firm cultivates positive learning cultures. Four themes emerged: deliberate cultivation of learning environments, structured knowledge-sharing systems, institutional investment through dedicated initiatives, and cross-disciplinary collaboration as an innovation catalyst. These findings informed two strategic interventions at a second AEC firm: an AI Proposal Assistant automating bid development workflows, and an AI Fluency Learning Path for Microsoft 365 Copilot developed in partnership with Microsoft. Five themes emerged from Cycle 2: (1) AI-augmented workflows achieved productivity gains of 50–83% time reduction, exceeding industry benchmarks; (2) knowledge base quality proved the primary determinant of AI effectiveness; (3) organizational readiness gaps require structured fluency development before full benefits can be realized; (4) human oversight is critical for building trust in professional contexts; and (5) participant enthusiasm serves as a leading indicator of adoption potential. This study demonstrates that AI fluency develops through structured experimentation with authentic business problems supported by intentional learning pathways. Findings directly informed an organizational Digital and AI Transformation Roadmap and offer the broader AEC industry actionable frameworks for developing AI fluency programs and building learning cultures that drive digital transformation.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Dean, Michael |
| Index terms: | transformation, partnership, productivity, effectiveness, built environment, artificial intelligence, determinant, construction industry, institutional investment, action research, organizational learning, knowledge base, proposal, experiment, collaboration, program, time reduction, workflow |
| Subjects: | project planning, economic analysis, risk assessment, data collection methods, business, artificial intelligence, professional development, management, performance management, software systems, infrastructure and transport systems, industry analysis, information systems, partnership management |
| Topics: | Risk Management, Procurement, Project Management, Quality Management, Stakeholder Management, Business Strategy, Information Management, Research Practice, Organizational Design, Digital Applications, Urban Studies |
| Descriptive scope: | 4 PCTE |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here