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Forrester:2024生成式AI在油气行业发展战略中的导航作用研究报告(中译版)(16页).pdf

上传人: 白**** 编号:402038 2024-12-31 16页 8.82MB

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1、NAVIGATING THE FUTURE OF OIL&GASWith Generative AIWhere Gen AI stands in oil&gas and where its heading,featuring findings from a 2024 commissioned study by Forrester Consulting for SoftServe ReportNavigating the Future of Oil&Gas With Generative AI2TABLE OF CONTENTS Navigating the Future of Oil&Gas

2、With Generative AIKey FindingsAdoption and ProgressData and Technology UsePartnerships and Future DirectionsGoals and Strategic NeedsGen AIs Role in Transforming Traditional PracticesAdoption Trends in Oil&Gas Moving Beyond Pilots to Full IntegrationStrategic Prioritization of AI Use CasesThe Role o

3、f AI PartnersTechnical ExpertiseUpskilling and AdaptationInfrastructure UpgradesEdge Computing and IoT IntegrationData Management and Governance Managing Data From Multiple SourcesRegulatory Compliance and Data Security11122789910111166612131161113157Executive SummaryIntroduction to Gen AI in Oil&Ga

4、sBuilding a Strategic AI Roadmap for SuccessKey RecommendationsMethodologyChallenges to Scaling Gen AIReportNavigating the Future of Oil&Gas With Generative AI3Adoption of Gen AI46%of oil and gas companies are advancing quickly in their Gen AI journey,with projects already rolled out or in the proce

5、ss of scaling to production.Reliance on Enterprise Data 52%of oil and gas experts report that their organizations rely on enterprise data to train Gen AI models,but they face challenges in consolidating and streamlining it.Impact on Operations 59%of respondents are currently applying Gen AI in suppl

6、y chain management,with 22%planning to scale its use within the next 12 to 18 months.Types of Data Used in Gen AI Models Organizations are leveraging a variety of enterprise data types in their Gen AI models,with 66%using operational data,54%using public data,and 49%incorporating customer data.Reali

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本文主要探讨了石油和天然气行业如何利用生成式人工智能(Gen AI)来应对行业挑战并实现创新。主要观点包括: 1. 石油和天然气公司正在快速采用生成式AI,但大多数公司仍处于试点阶段,尚未完全整合到核心运营中。 2. 生成式AI在供应链管理、勘探、钻探优化和数据管理等多个领域显示出应用潜力。 3. 成功实施生成式AI面临的主要挑战包括技术专业知识、基础设施升级和数据治理。 4. 89%的受访者认为需要与具有更高级技术能力的合作伙伴合作,以充分发挥生成式AI的价值。 5. 58%的受访者尚未建立正式的AI路线图,导致各部门的AI项目缺乏协调。 6. 建议包括:建立清晰的AI路线图,投资可扩展的基础设施,加强数据治理框架,并利用AI合作伙伴来加速路线图的制定和AI项目的优先级排序。
油气行业如何利用生成式AI实现创新和运营效率? 油气公司面临哪些挑战才能充分发挥生成式AI的潜力? 油气公司如何制定清晰的生成式AI战略路线图以实现业务目标?
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