Data-driven methodologies using Artificial Intelligence (AI) for prognostic maintenance are increasingly receiving a lot of attention because it is relatively easier to generate and store data from operational engineering assets that it used to be in the past. The field of AI is an area that is developing rapidly and is fast gaining acceptance in various industries. New designs for engineering systems now adopt the interconnection of components and systems in a cyber-physical space (aptly termed “Industry 4.0”), thereby increasing the awareness that current inspection and maintenance methodologies for predictive maintenance (PdM) will have to change. This paper reviews the current state of the use of AI in prognostic maintenance. The engineering, organizational, physical, manpower and infrastructural changes that will have to take place in order for operations to meet up with AI advances are highlighted. The paper further looks at the regulatory perspective, reviewing the preparedness of regulatory agencies for an inspection and maintenance regime that is predominantly AI-driven and what regulatory agencies must do to prepare for the future. The development of standards that will guide professional practice is also discussed. The review concludes that it is indeed imperative to continue to explore the development of AI-driven technologies for PdM, as current systems and methodologies will inevitably become incompatible with future designs and systems.