CERAWeek Panel Full Session: The digitalization transformation of oil & gas: What’s next?
Oil and gas companies are rapidly investing in a more digitalized approach to business – but what does this mean in practical terms? On this panel at CERAWeek, Maana CEO, Babur Ozden joined Wan Shamilah Saidi, Chief Digital Officer, Petronas; Jonathan Crane, VP of Wells Technology Deployment; Vivek Chidambaram, managing director, global digital lead, Accenture Strategy; and, Arshad Matin, CEO and board member, Paradigm (acquired by Emerson) share their perspectives.
Ozden noted that the most successful digitalization pursuits are when you disrupt something in a positive way – not just to increase efficiency or productivity, but to achieve a scalable result that has a major impact on the business. This requires identifying a rich set of high-impact business use cases for AI and digital transformation that can be executed as pilots, tested, and based on the results, either ended or rapidly scaled across the enterprise. The key is to test, fail fast, and move on to the next use case. This requires having an AI platform that lets you cost effectively pilot and scale across your business.
And finally, every member of the panel highlighted the importance of involving and capturing the knowledge of subject-matter experts from the very beginning in these AI initiatives. In order to drive operational efficiencies through digitalization, AI applications must capture employee knowledge, look for patterns in data from across the enterprise, and create algorithm-based solutions that scale across entire business facets. New AI tools enable oil and gas firms to instantly put AI apps into the hands of drilling and refining engineers, for example, who make lots of complex decisions with huge business implications. AI can empower them with expert insights based on knowledge from colleagues around the world – from instructions on how to do something to recommendations to help them make better and faster decisions. This requires building bridges between the human knowledge and data.