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Chevron Engineering

To build scalable digital platforms that enable Chevron to become the global energy company most admired for its technologies

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To build scalable digital platforms that enable Chevron to become the global energy company most admired for its technologies

Strengths

  • PLATFORM: Robust digital infrastructure handling 5PB+ of daily data
  • INNOVATION: Leading-edge technology adoption w/ 3 AI patents filed
  • TALENT: Deep technical expertise across 1200+ tech professionals
  • FUNDING: Strong tech investment at 8.2% of operational budget
  • INTEGRATION: Successful tech-business alignment with 91% team cohesion

Weaknesses

  • LEGACY: 40% of systems still running on outdated technology
  • VELOCITY: Development cycles 2.5x longer than industry benchmarks
  • SILOS: Cross-functional integration limited by departmental boundaries
  • TALENT: Critical skills gap in cloud-native development (32% open roles)
  • SECURITY: Vulnerability management completion rate at only 78%

Opportunities

  • AUTOMATION: 35% of field operations eligible for smart automation
  • ANALYTICS: Untapped reservoir data could improve production by 18%
  • ECOSYSTEM: Strategic tech partnerships with 5+ energy innovators
  • SUSTAINABILITY: Digital solutions could cut emissions by 30% by 2030
  • PLATFORMS: Unified data platform could reduce TCO by 22% annually

Threats

  • COMPETITION: Tech startups disrupting traditional energy operations
  • TALENT: 28% annual attrition rate for senior tech talent to competitors
  • COMPLIANCE: Increasing regulatory tech requirements across markets
  • SECURITY: Sophisticated cyber threats targeting energy infrastructure
  • INNOVATION: Accelerating pace of tech change outpacing adoption rates

Key Priorities

  • MODERNIZE: Accelerate legacy system migration to cloud architecture
  • TALENT: Implement aggressive tech talent acquisition & retention plan
  • INNOVATION: Establish dedicated digital innovation lab for rapid testing
  • INTEGRATION: Deploy unified data platform across all business units
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To build scalable digital platforms that enable Chevron to become the global energy company most admired for its technologies

MODERNIZE

Transform legacy systems into cloud-native platforms

  • MIGRATION: Complete cloud transition for critical operational systems (75% target)
  • DECOMMISSION: Retire 35% of legacy applications with clear migration path for users
  • ARCHITECTURE: Implement microservices architecture for 5 key operational systems
  • PERFORMANCE: Achieve 99.99% uptime for production systems with 40% reduced latency
TALENT EDGE

Build world-class engineering capabilities

  • RECRUITMENT: Fill 90% of critical engineering positions within 45 days of posting
  • DEVELOPMENT: Certify 250+ engineers in cloud, AI, and cybersecurity technologies
  • RETENTION: Improve technical talent retention to 92% (from current 72%) through career paths
  • CULTURE: Achieve top quartile engagement scores (>80%) among engineering organization
DATA MASTERY

Unlock value through unified data platforms

  • PLATFORM: Deploy unified data lake with 95% of operational data accessible via API
  • QUALITY: Implement data quality framework achieving 90%+ reliability across key datasets
  • GOVERNANCE: Establish enterprise data governance with 100% compliance to data standards
  • ANALYTICS: Enable self-service analytics for 2000+ business users with minimal IT support
AI ACCELERATION

Embed AI capabilities across operations

  • PRODUCTION: Implement AI-driven predictive maintenance across 75% of critical assets
  • UPSKILLING: Train 500+ engineers in ML fundamentals with 50+ becoming AI specialists
  • DEPLOYMENT: Launch 10 enterprise-scale AI applications with measurable business impact
  • FOUNDATION: Build enterprise AI platform supporting model deployment across business units
METRICS
  • Digital Transformation Index: 85/100 by EOY (currently 72/100)
  • Tech Delivery Velocity: 2.5x increase in feature deployment frequency
  • Operational Efficiency: 20% cost reduction through technology enablement
VALUES
  • Safety First
  • Trust & Transparency
  • Innovation
  • Inclusion & Diversity
  • High Performance
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Align the learnings

Chevron Engineering Retrospective

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To build scalable digital platforms that enable Chevron to become the global energy company most admired for its technologies

What Went Well

  • DIGITAL: Digital twin implementation reduced field issues by 24% q/q
  • CLOUD: Cloud migration ahead of schedule with 65% workloads migrated
  • AUTOMATION: Robotic process automation delivered $12M in cost savings
  • INNOVATION: New mobile field app adoption reached 88% within 60 days
  • SECURITY: Zero critical security incidents for third consecutive quarter

Not So Well

  • COST: Technology spending exceeded budget by 12% without ROI clarity
  • INTEGRATION: Cross-platform data sharing remains at suboptimal levels
  • PROJECTS: 38% of digital initiatives missed key milestone deadlines
  • LEGACY: Technical debt reduction targets missed by significant margin
  • TALENT: Critical engineering roles remained unfilled for 90+ days avg

Learnings

  • PROCESS: Agile adoption requires more structured change management
  • GOVERNANCE: Clearer technology investment criteria needed pre-approval
  • IMPACT: Business-technology alignment metrics show gaps in priorities
  • ADOPTION: User-centered design critical to field technology acceptance
  • DATA: Cross-functional data governance essential for AI/ML success

Action Items

  • IMPLEMENT: Enterprise tech ROI framework for all investments >$500K
  • ESTABLISH: Technology talent acquisition SWAT team with HR partners
  • ACCELERATE: Technical debt reduction through quarterly modernization
  • LAUNCH: Cross-functional data quality improvement initiative by Q2
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To build scalable digital platforms that enable Chevron to become the global energy company most admired for its technologies

Strengths

  • FOUNDATION: Established AI Center of Excellence with 50+ specialists
  • INVESTMENT: Dedicated $150M annual AI research & development budget
  • INFRASTRUCTURE: Petabyte-scale data lake ready for AI applications
  • PILOT: Successful AI predictive maintenance saving $25M annually
  • LEADERSHIP: Executive-level AI champions driving strategic adoption

Weaknesses

  • FRAGMENTATION: AI initiatives siloed across business units
  • SKILL: Only 15% of technical staff with advanced AI/ML competencies
  • DATA: Inconsistent data quality impeding model training effectiveness
  • GOVERNANCE: Immature AI ethics and governance framework
  • SCALING: Difficulty moving AI projects beyond proof-of-concept stage

Opportunities

  • OPERATIONS: AI could optimize drilling efficiency by up to 20%
  • SAFETY: Computer vision systems could reduce incidents by 35%
  • FORECASTING: AI-driven models improve production forecasting by 40%
  • CUSTOMER: AI personalization could improve B2B service satisfaction
  • EXPLORATION: ML algorithms to increase discovery success rate by 15%

Threats

  • COMPETITIVE: Energy competitors investing 2.5x more in AI annually
  • REGULATION: Emerging AI regulation could limit deployment scope
  • TALENT: Fierce competition for AI expertise from tech sector
  • MARKETPLACE: AI-native energy startups gaining market traction
  • TRUST: Stakeholder concerns about AI ethics and decision-making

Key Priorities

  • INVESTMENT: Increase AI budget by 50% with focus on production use
  • TALENT: Launch AI skilling initiative for 500+ engineers annually
  • GOVERNANCE: Implement enterprise AI ethics & governance framework
  • INTEGRATION: Deploy central AI platform for cross-business leverage