在石油和天然气领域驾驭人工智能：为未来提供动力的见解

Where Gen AI stands in oil & gas and where it’s heading,featuring findings from a 2024 commissioned study byForrester Consulting for SoftServe TABLE OF CONTENTS Navigating the Future of Oil & Gas With Generative AI1 1Executive Summary Key FindingsAdoption and ProgressData and Technology UsePartnerships and Future DirectionsGoals and Strategic Needs11122 6Introduction to Gen AI in Oil & Gas Gen AI's Role in Transforming Traditional PracticesAdoption Trends in Oil & GasMoving Beyond Pilots to Full Integration666 7Challenges to Scaling Gen AI Technical ExpertiseUpskilling and AdaptationInfrastructure UpgradesEdge Computing and IoT IntegrationData Management and GovernanceManaging Data From Multiple SourcesRegulatory Compliance and Data Security7899101111 11Building a Strategic AI Roadmap for Success Strategic Prioritization of AI Use CasesThe Role of AI Partners1213 13Key Recommendations EXECUTIVE SUMMARY The oil and gas industry is undergoing a significant shift, with Generative AI (Gen AI) taking centerstage as a key tool for innovation and operational efficiency. From exploration to supply chainmanagement, Gen AI is beginning to change how companies approach their most pressing challenges. A recent study, commissioned by Forrester Consulting on behalf of SoftServe, provides a detailed lookat how theoil and gas sectoris adopting Gen AI. The survey shows that interest in Gen AI iswidespread, with companies exploring its potential across areas like data analytics, IT, engineering,and operations. However, many organizations are still navigating hurdles — particularly aroundupgrading infrastructure, improving data governance, and scaling beyond initial pilot projects. While many oil and gas companies have taken the first steps with Gen AI, few have fully integrated itinto their core operations. The need for clearer strategic roadmaps and prioritization of key Gen AIinitiatives remains critical to unlocking its full potential. Even so, there's growing recognition of thevalue Gen AI brings, from improving process efficiency to enabling predictive maintenance and real-time decision-making. Key Findings: Adoption and Progress Adoption of Gen AI 46% of oil and gas companies are advancing quickly in their Gen AI journey, withprojects already rolled out or in the process of scaling to production. Impact on Operations 59% of respondents are currently applying Gen AI in supply chain management,with 22% planning to scale its use within the next 12 to 18 months. Realized Business Value From Gen AI More than half of oil and gas respondents reported experiencing or having alreadyexperienced the maximum business value Gen AI can bring to their organizations. Data and Technology Use Reliance on Enterprise Data 52% of oil and gas experts report that their organizations rely on enterprise data totrain Gen AI models, but they face challenges in consolidating and streamlining it. Types of Data Used in Gen AI Models Organizations are leveraging a variety of enterprise data types in their GenAI models, with 66% using operational data, 54% using public data, and 49%incorporating customer data. Partnerships and Future Directions Future Use of Technology Partners 88% of oil and gas experts plan to increase or significantly increase the use oftechnology partners for their Gen AI initiatives moving forward. Need for Advanced Technical Partnerships Most respondents agree or strongly agree on the need for partners with advancedtechnical capabilities to realize transformational value from Gen AI, though manyare dissatisfied with existing partners. Goals and Strategic Needs Top Gen AI Goals The top five Gen AI goals for oil and gas companies today are improving softwaredevelopment, business strategy, research & development, customer engagement,and operational efficiency. Key Areas for Improvement in Gen AI Strategy Oil and gas companies identified three main areas where improvement is needed: •Improving the speed of prototyping across new use cases•Improving language model accuracy•Enhancing the ability to leverage data effectively Critical Needs for Scaling Gen AI Four areas of need were identified to fully realize Gen AI capabilities: technicalexpertise, infrastructure upgrades, data governance, and strategic planning. Enthusiasm for Gen AI has been widespread in the oil and gas sector, but much like in otherindustries, pilot programs have been implemented across departments without a clear focus. This lackof a strategic roadmap is limiting the full realization of Gen AI's business potential. In this report, we explore these findings and offer actionable insights for oil and gas companiesseeking to leverage Gen AI to its full potential. SOFTSERVE'S GENERATIVE AI LAB SoftServe’s Generative AI Lab helps businesses achieve faster, better results with Gen AI bytransforming great ideas into real-world solutions. The lab collaborates with leading platforms likeAWS,Google Cloud,Microsoft Azure, andNVID