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Verizon Communications Engineering

To build and operate the most reliable networks that move the world forward by enabling people to innovate and drive positive change

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Align the strategy

Verizon Communications Engineering SWOT Analysis

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To build and operate the most reliable networks that move the world forward by enabling people to innovate and drive positive change

Strengths

  • INFRASTRUCTURE: Industry-leading 5G and fiber network footprint
  • RELIABILITY: Highest network reliability rating in telecom sector
  • SCALE: Largest US wireless subscriber base at over 140M customers
  • SECURITY: Advanced cybersecurity capabilities and protocols
  • TALENT: Strong engineering expertise across networking disciplines

Weaknesses

  • LEGACY: Technical debt from aging systems requiring modernization
  • AGILITY: Slow deployment cycles compared to cloud-native competitors
  • INTEGRATION: Siloed systems hampering cross-functional development
  • INNOVATION: Gap in emerging technology adoption vs tech companies
  • COMPLEXITY: Overly complex architecture increasing maintenance costs

Opportunities

  • EDGE: Expansion of edge computing services leveraging 5G network
  • IOT: Growing market for connected devices requiring reliable networks
  • CLOUD: Integration with hyperscalers to create hybrid networking
  • AUTOMATION: Network function virtualization to reduce OpEx by 30%
  • PARTNERSHIPS: Strategic tech alliances to accelerate transformation

Threats

  • COMPETITION: Cable companies expanding wireless offerings rapidly
  • REGULATION: Changing policy landscape affecting network investment
  • DISRUPTION: New satellite technologies bypassing traditional networks
  • INVESTMENT: Continuous CapEx requirements to maintain leadership
  • TALENT: Losing key engineering talent to tech-focused companies

Key Priorities

  • MODERNIZATION: Accelerate network architecture transformation
  • AUTOMATION: Implement next-gen automated operations platforms
  • CONVERGENCE: Develop unified cloud-network integration strategy
  • INNOVATION: Establish rapid prototyping for emerging technologies
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Align the plan

Verizon Communications Engineering OKR Plan

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To build and operate the most reliable networks that move the world forward by enabling people to innovate and drive positive change

MODERNIZE

Transform network architecture for digital agility

  • MIGRATION: Transition 60% of core network functions to cloud-native architecture by Q3
  • AUTOMATION: Deploy zero-touch provisioning across 85% of network elements reducing config time by 70%
  • SIMPLIFICATION: Reduce technology variants by 30% through standardization of platform components
  • VELOCITY: Decrease mean time from code commit to production deployment from 14 days to 3 days
INNOVATE

Pioneer next-gen network capabilities and services

  • INCUBATION: Launch network innovation lab with 5 commercial-ready pilots by end of quarter
  • AI-NETWORK: Develop and deploy ML-based predictive maintenance across 40% of network nodes
  • EDGE-COMPUTE: Release edge computing platform in 25 markets with <10ms latency guarantee
  • INTEGRATION: Establish unified API gateway with 99.99% availability for partner ecosystems
CONVERGE

Unify cloud and network platforms seamlessly

  • HYBRID: Enable seamless workload portability across 3 major cloud providers and edge network
  • ORCHESTRATION: Implement cross-domain service orchestration reducing provisioning time by 65%
  • SECURITY: Deploy zero-trust architecture across 90% of network-cloud interfaces
  • ABSTRACTION: Create unified control plane supporting 85% of network functions across domains
AMPLIFY

Scale AI capabilities across engineering operations

  • PLATFORM: Build centralized ML operations platform supporting 200+ models in production
  • TRAINING: Upskill 1000 engineers with hands-on AI/ML capabilities through practical projects
  • OPTIMIZATION: Reduce network energy consumption by 18% through AI-powered dynamic controls
  • AUTOMATION: Automate 70% of tier-1 and tier-2 network operations through ML-driven systems
METRICS
  • 5G RELIABILITY: 99.9% availability with <15ms latency across metro markets
  • DEPLOYMENT VELOCITY: 3x increase in code deployment frequency with 80% reduction in failures
  • OPERATIONAL EFFICIENCY: 30% reduction in engineering cost per petabyte delivered
VALUES
  • Customer-First
  • Integrity
  • Respect
  • Performance Excellence
  • Innovation
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Align the learnings

Verizon Communications Engineering Retrospective

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To build and operate the most reliable networks that move the world forward by enabling people to innovate and drive positive change

What Went Well

  • WIRELESS: Consumer wireless service revenue grew 3.1% year over year
  • FIBER: Verizon Business Group added 52,000 net new broadband customers
  • EFFICIENCY: Cost reduction initiatives delivered $1.3B in annual savings
  • CUSTOMERS: Postpaid phone churn remained low at 0.84% for the quarter

Not So Well

  • ENTERPRISE: Business wireline revenue declined 5.2% year over year
  • CAPEX: Network investment exceeded forecast by 8% without ROI boost
  • MARGIN: EBITDA margin contracted by 120 basis points vs prior year
  • PRODUCTIVITY: Engineering deployment velocity below targeted metrics

Learnings

  • INTEGRATION: Cross-platform initiatives deliver better ROI than siloed
  • AUTOMATION: Self-healing network capabilities reduced outages by 23%
  • CLOUD: Hybrid infrastructure approach balances performance and cost
  • METRICS: Engineering output KPIs need alignment with business outcomes

Action Items

  • CONSOLIDATE: Reduce redundant network management systems by 40%
  • ACCELERATE: Cut service deployment time from 90 to 45 days by Q3
  • STREAMLINE: Implement standard API gateway across all service platforms
  • TRANSFORM: Complete migration of 75% of workloads to cloud-native
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Drive AI transformation

Verizon Communications Engineering AI Strategy SWOT Analysis

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To build and operate the most reliable networks that move the world forward by enabling people to innovate and drive positive change

Strengths

  • DATA: Massive customer and network data sets for AI training
  • INFRASTRUCTURE: Distributed computing resources across network edge
  • EXPERIENCE: Established ML operations for network optimization
  • PARTNERS: Strategic relationships with leading AI technology vendors
  • RESOURCES: Significant capital available for AI investments

Weaknesses

  • TALENT: Limited AI/ML engineering specialists compared to tech firms
  • INTEGRATION: Disconnected AI initiatives across business units
  • GOVERNANCE: Inconsistent data quality and accessibility standards
  • CULTURE: Traditional telecom mindset slowing AI adoption
  • TOOLING: Fragmented AI development environments and frameworks

Opportunities

  • PREDICTION: Network issue prevention through predictive analytics
  • PERSONALIZATION: Enhanced customer experiences through AI insights
  • EFFICIENCY: 40% OpEx reduction through AI-driven automation
  • SECURITY: Advanced threat detection using behavioral analytics
  • SERVICES: New AI-powered business solutions and revenue streams

Threats

  • COMPETITION: Tech giants developing AI-native network solutions
  • EXPERTISE: Talent acquisition challenges in competitive AI market
  • REGULATION: Emerging AI governance requirements adding complexity
  • DEPENDENCY: Over-reliance on third-party AI platforms and tools
  • ETHICS: Potential reputational risks from AI deployment decisions

Key Priorities

  • UNIFICATION: Establish centralized AI strategy and governance
  • UPSKILLING: Launch comprehensive AI training for engineering teams
  • FOUNDATION: Build enterprise-wide AI/ML deployment platform
  • ACCELERATION: Create dedicated AI innovation lab with clear KPIs