Artificial intelligence enhances digital asset management

Architecture, Engineering, Construction, and Operation (AECO) is a crucial sector in many nations. However, in the last decades, the AECO industry has struggled to evolve, and as a result, its productivity has suffered. In a world beset by resource scarcity, climate change, and significant population growth in developing and established countries, it is more important than ever to consider how the Built Environment (BE) can provide an inexpensive, sustainable, healthy, and peaceful existence for the global population. In this context, a new set of dynamics emerges as a result of the digital revolution fostered in other sectors. Servitization is one of them, significantly impacting the asset's use phase. This dynamic occurs during the asset's transaction and usage phases when it is no longer viewed solely as a physical thing used to achieve an organization's fundamental objectives but as a component of the set of services that can be delivered to the end-user through its use. Since roughly 80% of an asset life cycle cost is spent during O&M activities, the focus is widely shifting towards this stage to ensure minimal financial and environmental impacts of a building project. As a result, Asset Management (AM), once regarded as a non-core function supporting the organization's primary business, is becoming a crucial function that balances costs, opportunities, and risks to optimize the asset's performance and realize value. Despite being historically less innovative than other industries, the construction sector is undergoing a technological revolution. Mimicking the innovations brought by the 'Industry 4.0' technologies, often known as the 'fourth industrial revolution', a new paradigm called 'Construction 4.0' is emerging in the sector. Construction 4.0 can be defined as the fusion of cutting-edge industrial production systems, cyber-physical systems, and digital and computing technologies to redefine the design, construction, operation, and maintenance of buildings and infrastructure while considering circularity. Due to this digital transformation, massive amounts of data are produced, and systematic analysis combined with predictive modeling can improve operational and construction safety, lower operational and construction costs, speed up construction, and increase sustainability. However, it is impractical for humans or traditional computer programs to analyze enormous amounts of data and identify patterns using rule-based approaches. Artificial Intelligence (AI), a key component of Construction 4.0, can process massive volumes of data, spot patterns, and build large-scale statistical models. AI is a branch of Computer Science able to provide computers with human-like capabilities and produces quality predictions based on available data, leading to better decision-making and increased productivity. Because present operational procedures and processes are often wedded to the old paradigm (relative primarily to the physical asset and an 'artisanal approach'), they no longer effectively support decision-making in the digital built environment. Implementing AI necessitates the availability of numerous components (e.g., labor skills, data-gathering policies, etc.), and it is necessary to clarify how AI may fit into existing company procedures. Assessing AI feasibility is especially difficult in AM since it involves disciplines with varying disciplines and scales. As a result, the spread of AI technologies in AM is constrained by the following open questions: Can AI technologies address the changes required by users, given that servitization is shifting contractors' focus from facility delivery to providing long-term asset-related services? How can AI be used in AM to guarantee improved services, given that the Construction 4.0 framework requires process changes to exploit the enormous amount of data gathered during the asset lifecycle? And how can the role and applications of AI in the Construction 4.0 paradigm be understood, given that interaction with disruptive technologies (e.g., IoT, drones, robotics) will max mize AI effectiveness? These issues open a new discussion on how to harness the benefits promised by AI technologies with the BE complexity and redefine the Asset Management business process to contribute to a future-proof framework. The innovative contribution of the thesis sits in evaluating and remodeling current AM processes from an AI-applicability perspective. The research is conducted by assessing the industry's level of AI readiness - the ability to deploy AI technologies to enable digital transformation - through the introduced AI Readiness Index and the definition of new or remodeled AM processes at three levels: strategic, tactical, and operational. The study aims to discuss the possible AI applications in AM to enable cutting-edge business processes for managing digital and servitized assets. The framework is articulated in two parts that bring a traditional AM perspective towards a re-engineered, AI-enhanced AM. The first part identifies the industry's areas ready for AI and the significant issues that must be addressed. The second part attempts to innovate traditional AM processes at different levels (strategic, tactical, operational) and validates the proposed new approaches in different case study applications.