Challenges associated with volatile oil and gas prices and an enhanced emphasis on a cleaner energy world are pushing the oil and gas industry to re-consider its fundamental existing business-models and establish a long-term, more sustainable vision for the future. That vision needs to be more competitive, innovative, sustainable and profitable. To move along that path the oil and gas industry must proactively embrace the 4th Industrial Revolution (oil and gas 4.0) across every part of its business. This will help to overcome time constraints in the understanding and utilization of the terabytes of data that have been and are continuously being produced. There is a clear need to streamline and enhance the critical decision-making processes to deliver on key value drivers, reducing the cost per barrel, enabling greater efficiencies, enhanced sustainability and more predictable production. Latest advances in software and hardware technologies enabled by virtually unlimited cloud compute and artificial intelligence (AI) capabilities are used to integrate the different petro-technical disciplines that feed into massive reservoir management programs. The presented work in this paper is the foundation of a future ADNOC digital reservoir management system that can power the business for the next several decades. In order to achieve that goal, we are integrating next generation data management systems, reservoir modeling workflows and AI assisted interpretation systems across all domains through the Intelligent Integrated Subsurface Modelling (IISM) program. The IISM is a multi-stage program, aimed at establishing a synergy between all domains including drilling, petrophysics, geology, geophysics, fluid modeling and reservoir engineering. A continuous feedback loop helps identify and deliver optimum solutions across the entire reservoir characterization and management workflow. The intent is to dramatically reduce the turnaround time, improve accuracy and understanding of the reservoir for better and more timely reservoir management decisions. This would ultimately make the management of the resources more efficient, agile and sustainable. Data-driven machine learning (ML) workflows are currently being built across numerous petro-technical domains to enable quicker data processing, interpretation and insights from both structured and unstructured data. Automated quality controls and cross domain integration are integral to the system. This would ensure a better performance and deliver improvements in safety, efficiency and economics. This paper highlights how applying artificial intelligence, automation and cloud computing to complex reservoir management processes can transform a traditionally slow and disconnected set of processes into a near real time, fully integrated, workflow that can optimize efficiency, safety, performance and drive long term sustainability of the resource.