A Framework of Best Practices for Delivering Successful Artificial Intelligence Projects. A Case Study Demonstration

Abstract A practical framework that outlines the critical steps of a successful process that uses data, machine learning (Ml), and artificial intelligence (AI) is presented in this study. A practical case study is included to demonstrate the process. The use of artificial intelligent and machine learning has not only enhanced but also sped up problem-solving approaches in many domains, including the oil and gas industry. Moreover, these technologies are revolutionizing all key aspects of engineering including; framing approaches, techniques, and outcomes. The proposed framework includes key components to ensure integrity, quality, and accuracy of data and governance centered on principles such as responsibility, equitability, and reliability. As a result, the industry documentation shows that technology coupled with process advances can improve productivity by 20%. A clear work-break-down structure (WBS) to create value using an engineering framework has measurable outcomes. The AI and ML technologies enable the use of large amounts of information, combining static it was the process and engineering knowledge that led to the successful outcome. Having a systematic WBS has become vital in data analytics projects that use AI and ML technologies. An effective governance system creates 25% productivity improvement and 70% capital improvement. Poor requirements can consume 40%+ of development budget. The process, models, and tools should be used on engineering projects where data and physics are present. The proposed framework demonstrates the business impact and value creation generated by integrating models, data, AI, and ML technologies for modeling and optimization. It reflects the collective knowledge and perspectives of diverse professionals from IBM, Lockheed Martin, and Chevron, who joined forces to document a standard framework for achieving success in data analytics/AI projects.