Developing a Model for the Adoption of Agentic AI in Facilities Management: Enabling Autonomous Decision-Making for Sustainable Built Environments

About the Project

Facilities Management (FM) is a critical, multi-trillion-dollar global industry responsible for ensuring the functionality, safety, comfort, and sustainability of the built environment. Facilities management encompasses a wide range of activities, including maintenance, space management, security, and sustainability practices. Traditionally reliant on reactive maintenance and human-intensive monitoring, the sector faces escalating pressures from rising energy costs, stringent sustainability targets (e.g., Net Zero), and increasing complexity of building systems. The Fourth Industrial Revolution (4IR) presents a paradigm shift with the emergence of Artificial Intelligence (AI), particularly Agentic AI. Agentic AI refers to systems that can perceive their environment, make decisions, and take actions autonomously to achieve specific goals without continuous human intervention. In an FM context, this could range from a system that autonomously adjusts HVAC and lighting based on occupancy and weather forecasts, to one that dispatches a robotic unit to inspect a fault and place a work order with a contractor.

However, the adoption of such transformative technology in the conservative FM industry is slow and fraught with challenges. Facility managers, often constrained by limited budgets, skills gaps, and operational silos, lack a clear framework to guide the transition from traditional practices to AI-driven, autonomous operations. The problem, therefore, is not a technological one but a socio-technical one: There is a critical absence of a robust, evidence-based model that explains and facilitates the adoption of Agentic AI within the complex organisational context of FM. This PhD research aims to fill this gap by developing and validating a comprehensive adoption model that will empower FM professionals to integrate Agentic AI effectively, thereby unlocking new levels of efficiency, sustainability, and resilience in building operations. The prospective candidate is expected to generate a holistic model aimed at bridging the knowledge gap regarding the adoption of agentic AI for facilities management activities. The student will adopt a pragmatist paradigm employing a mixed-methods design.

Academic qualifications

Have, or expect to achieve by the time of start of the studentship a first-class honours degree, or a distinction at master level, ideally in Real Estate and Investment, Facility management, Construction Project Management, Civil Engineering, Computer Science or equivalent with a good fundamental knowledge of Quantity Surveying, Urban and Regional Planning

English language requirement

IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.

Essential attributes:

• Only a first-class honours degree, or a distinction at master level in a subject relevant to the PhD project will be considered, or equivalent achievements.
• Competent in the use of Statistical Software
• Publication record in reputable journals
• High standards of verbal and written English communication with strong interpersonal skills
• Excellent time management skills

Desirable attributes:

• Practical experience in research or industry will be considered an advantage.

APPLICATION CHECKLIST

• Completed application form
• CV
• 2 academic references, using the Postgraduate Educational Reference Form (download)
• Research project outline of 2 pages (list of references excluded). The outline may provide details about
  1. Background and motivation of the project. The motivation, explaining the importance of the project, should be supported also by relevant literature. You can also discuss the applications you expect for the project results.
  2. Research questions or objectives.
  3. Methodology: types of data to be used, approach to data collection, and data analysis methods.
  4. List of references.
• The outline must be created solely by the applicant. Supervisors can only offer general discussions about the project idea without providing any additional support.
• Statement no longer than 1 page describing your motivations and fit with the project.
• Evidence of proficiency in English (if appropriate)

To be considered, the application must use the advertised title as project title

For informal enquiries about this PhD project, please contact t.osunsanmi@napier.ac.uk

Application link: https://evision.napier.ac.uk/si/sits.urd/run/siw_sso.go?mP9MDnTs1Rwm8ftb3WVhDhXtraMQwXSUMdHC9wIc34es5bJqXf