The evolution of industrial revolutions has been marked by the increasing use of data and information to improve productivity and efficiency. Industry 3.0 introduced automation and digitalization, which generated a lot of data from various sources and processes. This data was mainly used for monitoring and controlling the industrial activities, such as production, quality, and maintenance. Industry 4.0 leveraged this data to generate insights and intelligence, using technologies such as cloud computing, big data analytics, and the Internet of Things (IoT). These technologies enabled the integration and communication of data across different levels and domains of the industrial system, such as machines, products, processes, and services. Industry 4.0 also introduced the concept of smart factories, which are self-organizing, adaptive, and learning systems that can optimize their performance and efficiency. Industry 5.0 aims to enable human-robot collaboration and artificial intelligence [1], creating a more personalized and sustainable industrial system. Industry 5.0 focuses on enhancing the human capabilities and creativity, rather than replacing them with machines. It also emphasizes the social and environmental aspects of industrial development, such as customer satisfaction, worker well-being, and resource conservation. Industry 5.0 envisions a human-centric and eco-friendly industrial paradigm, where humans and machines work together in harmony and synergy.

One of the sectors that can benefit from the convergence of business intelligence (BI) and artificial intelligence (AI) is the energy industry, which faces challenges such as increasing demand, environmental regulations, and market volatility. By combining BI and AI, energy companies can unlock value from their data and optimize their operations, such as production, distribution, and consumption. BI helps energy companies to collect, store, analyze, and visualize data from various sources, such as sensors, meters, devices, and systems. BI enables energy companies to monitor and manage their assets, processes, and performance, as well as to identify and solve problems, improve efficiency, and reduce costs. AI helps energy companies to augment and automate their decision making, using techniques such as machine learning, natural language processing, computer vision, and deep learning. AI enables energy companies to generate predictions, recommendations, and insights from their data, as well as to optimize their operations, such as scheduling, dispatching, pricing, and trading. AI also helps energy companies to create new products and services, such as smart grids, smart meters, smart homes, and smart cities. By combining BI and AI, energy companies can create a data-driven and intelligent energy system, which can respond to the changing needs and preferences of customers, stakeholders, and regulators, as well as to the dynamic and uncertain market conditions.

This paper discusses the approach of complimenting the established business intelligence (BI) process with Artificial Intelligence (AI) in order to optimize gas production in an oil field in the south of Sultanate of Oman, it details the facts, observations, and insights the multidisciplinary authors have captured throughout the progress of this work, as well as general industry insights and BI process description.

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