Table 2

Linking empirical findings to AI-BIM framework pillars

Empirical finding (Ethiopia)Supporting literatureStrategic framework pillarContextual interpretation
RIM practiceLimited interdepartmental coordinationNaji et al. (2024) ProcessesReflects institutional fragmentation; emphasises the importance of integrated workflows, collaborative platforms, and cross-agency coordination mechanisms
Stakeholder collaboration challengesAsif et al. (2024) People/ProcessesIndicates weak stakeholder engagement; suggests strengthening communication frameworks and collaborative decision-making structures
Outdated technologies and manual systemsKesto and Tsega (2022), Melaku Belay et al. (2021) TechnologyHighlights low digital maturity; calls for investment in digital infrastructure, interoperable systems, and adoption of emerging technologies such as AI, BIM and AI-BIM integration
AI-BIM adoption challengesLack of skilled personnelOlugboyega and Windapo (2021), Saka and Chan (2019a, b)PeopleHighlights Ethiopia's human resource limitations; underscores the critical need for capacity-building initiatives, professional training programs, and institutional skill development to support AI-BIM adoption
High initial investment costHamma-Adama et al. (2020), Ismail et al. (2022) BudgetReflects financial constraints and limited public-sector funding; emphasises the importance of investment planning, cost-benefit analysis and exploring public-private partnerships
Resistance to changeRane et al. (2024), Tan et al. (2019) People/ProcessesIndicates organisational culture challenges and low technology acceptance; suggests the need for change management strategies, awareness programs, and incremental implementation approaches
Lack of clear policies and regulationsSrivastava et al. (2022), Semunigus (2020) PolicyDemonstrates regulatory and institutional gaps; highlights the necessity of establishing supportive legal frameworks, standards, and national digitalisation strategies
Poor data quality and accessibilityNaji et al. (2024) DataReveals weak data governance systems; underscores the need for structured data management, data standardisation, and integration mechanisms
AI-BIM adoption benefitsBIM enhances decision-makingOzturk and Tunca (2020) Technology/DataDemonstrates readiness for digital transformation and supports the use of AI-BIM for predictive analytics and evidence-based decision-making
AI-BIM improves resilience and sustainabilitySalleh et al. (2019), Bilge and Yaman (2021) Technology/ProcessesReflects awareness of inefficiencies in current RIM; aligns with sustainability goals and long-term asset management
AI-BIM enhances stakeholder collaboration and project deliveryHetemi et al. (2020), Ma et al. (2020) People/ProcessesIndicates potential for improved communication and coordination across departments; reinforces the need for collaboration-focused strategies
AI-BIM Lifecycle cost optimisation and efficiencyPishdad and Onungwa (2024) Processes/BudgetEmphasises long-term economic benefits and supports the integration of lifecycle thinking into infrastructure planning and management

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