Rudy Lai

AI @ Marathon Petroleum

Refining and logistics
Industry
Last updated
July 3, 2025 at 10:44 AM

Summary

  • Marathon Petroleum has progressively integrated AI and machine learning technologies since 2019, evolving from digital monitoring and process optimization to leveraging AI for refinery operations, acquisitions, and large-scale alert management across 4,000+ wells.
  • Key leadership insights include contributions from Les Davis (Q3 2024) on maintenance digitization and a notable strategic emphasis on AI integration highlighted in their annual reports (2023, 2024). Partnerships include Hypergiant Industries (2019) and AWS (2023) to enhance AI capabilities.
  • AI-driven efforts have primarily targeted operational efficiency, safety enhancement, cost reduction, and improving environmental performance. By mid-2025, Marathon Petroleum actively participates in industrial AI innovation forums, signaling a strong internal adoption though customer-facing AI applications are limited.

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5 AI Use Cases at Marathon Petroleum

Enterprise Monitoring
2025
Traditional
Generative
Agentic
Outcome
Through enterprise-scale AI monitoring and alerting systems, Marathon Petroleum enhances process awareness, operational decision-making, and human-centric innovation. [1]
Maintenance Digitization
2024
Traditional
Generative
Agentic
Outcome
Costs
AI technologies are used to digitize and connect maintenance schedules and processes enabling predictive maintenance and mobility, reducing downtime and operational costs. [1]
Operational Optimization
2023
Traditional
Generative
Agentic
Outcome
Costs
Marathon Petroleum uses AI algorithms and machine learning to monitor and optimize refinery and oilfield operations, improving efficiency and reducing downtime across thousands of wells. [1][2][3]
M&A Analytics
2020
Traditional
Generative
Agentic
Outcome
Risk
Executives employ AI analytics in mergers and acquisitions to improve due diligence processes, predict deal outcomes and financial performance. [1]
Safety Management
2019
Traditional
Generative
Agentic
Outcome
Risk
AI-driven monitoring tools and alerts help Marathon Petroleum identify and mitigate process safety hazards, enhancing safety protocols and reducing accident risks. [1][2]

Timeline

2025 Q4: no updates

2025 Q3

2 updates

Multiple reports discussed Marathon Petroleum's ongoing exploration of AI's integration for consistent EPS growth, trading strategies, with new senior hires focused on data science and AI product ownership.

2025 Q2

3 updates

Marathon Petroleum actively discussed safety-first strategy fueling resilience, received moderate AI investment ratings, and shared success at enterprise monitoring and human-centric AI innovation events.

2025 Q1

1 updates

2024 Annual Report emphasized continuing adoption and technological advancement in AI and machine learning to sustain competitive advantages.

2024 Q4

2 updates

Marathon Petroleum reported strong Q2 2024 results while AI-driven refinery optimization was identified as transformative for refinery operations.

2024 Q3

1 updates

Les Davis highlighted the NextGen Maintenance & Mobility initiative, aiming to digitalize operations and embed AI intelligence in maintenance and mobility processes.

2024 Q2

1 updates

Marathon Petroleum prominently utilized AI for operational optimization, cost reduction, safety improvements, and environmental enhancement according to industry analyses.

2024 Q1

2 updates

Marathon Petroleum’s 2023 digital transformation strategy highlighted AI’s growing role; the 2023 annual report discussed cyber risks associated with AI.

2023 Q4

1 updates

Industry commentary anticipated increased adoption of generative AI technologies in oil and gas, including Marathon Petroleum in the near future.

2023 Q3: no updates

2023 Q2

1 updates

Marathon Oil began scaling intelligent alerts using AWS and Seeq to manage alerting across more than 4,000 wells, demonstrating application of cloud AI for operational efficiency.

2023 Q1: no updates

2022 Q4: no updates

2022 Q3: no updates

2022 Q2: no updates

2022 Q1: no updates

2021 Q4: no updates

2021 Q3

1 updates

SPE Research Portal adopted AI technology to support better technical information discovery and analysis in the petroleum sector, facilitating data-driven insights.

2021 Q2: no updates

2021 Q1: no updates

2020 Q4: no updates

2020 Q3: no updates

2020 Q2

1 updates

Executives highlighted AI and analytics integration in mergers and acquisitions, reflecting AI’s role in strategic financial and operational decision-making.

2020 Q1: no updates

2019 Q4: no updates

2019 Q3: no updates

2019 Q2

1 updates

The Digital Oilfield initiative was furthered via collaboration with machine learning firm Hypergiant Industries, expanding AI-driven operational tools.

2019 Q1

1 updates

Marathon Petroleum initiated digital monitoring and optimization efforts by selecting Axens technologies, incorporating AI to enhance process safety and operational reliability.