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Salesforce Engineering

To enable organizations to connect with their customers through innovative cloud solutions that deliver exceptional customer experiences at global scale

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To enable organizations to connect with their customers through innovative cloud solutions that deliver exceptional customer experiences at global scale

Strengths

  • PLATFORM: Industry-leading integrated platform with 12+ cloud solutions enabling seamless customer data flow across departments
  • ECOSYSTEM: Extensive partner ecosystem with 150,000+ partners that extends platform capabilities and accelerates customer adoption
  • DATA: Unparalleled customer data repository from 150,000+ customers enabling continuous product improvements and AI training
  • INNOVATION: Proven track record of rapid innovation - released 39 major deployments in 2023 compared to industry average of 8-12
  • TALENT: Deep engineering bench with 7,000+ engineers specializing in cloud architecture, data science, and AI implementation

Weaknesses

  • COMPLEXITY: Product portfolio complexity has created integration challenges and increased onboarding time for new engineers by 22%
  • TECHNICAL_DEBT: Legacy code bases slow innovation velocity, with 30% of engineering time spent on maintenance vs. new features
  • ARCHITECTURE: Siloed architecture from acquisitions creates fragmented experiences for customers and developers
  • DEPLOYMENT: Release management process is cumbersome, with deployment cycles 2.5x longer than industry benchmarks
  • TALENT_GAPS: Insufficient specialized expertise in emerging technologies like generative AI, federated learning, and edge computing

Opportunities

  • AI_INTEGRATION: Lead enterprise AI adoption by embedding AI capabilities across the entire platform, potentially increasing revenue by 15%
  • VERTICAL_SOLUTIONS: Develop industry-specific technical solutions capturing 27% premium vs. horizontal offerings
  • DEVELOPER_EXPERIENCE: Reimagine developer platform to increase ecosystem innovation by 3x current velocity
  • HYPERSCALER_PARTNERSHIP: Deepen cloud provider integrations to enhance performance while reducing infrastructure costs by 18%
  • DATA_MESH: Implement data mesh architecture allowing customers to unlock 40% more value from their data assets

Threats

  • COMPETITION: Hyperscalers (AWS, Azure, GCP) expanding into CRM space with native AI advantages and 5-10x engineering resources
  • INNOVATION_PACE: Startup ecosystem moving 3x faster in AI-native solutions threatening to outpace our innovation cycles
  • TALENT_MARKET: Intensifying competition for AI talent with tech giants offering 30-40% premium on compensation packages
  • SECURITY: Growing sophistication of cyber threats targeting SaaS platforms, with attacks up 47% targeting enterprise data
  • REGULATORY: Emerging data sovereignty requirements forcing localized infrastructure deployments increasing costs by 23%

Key Priorities

  • MODERNIZE: Accelerate platform modernization to reduce technical debt and enable rapid AI integration across all products
  • AI_EVERYWHERE: Develop comprehensive AI strategy that embeds intelligence into every product while maintaining trust and security
  • DEVELOPER_ECOSYSTEM: Revitalize developer experience to accelerate ecosystem innovation and extend platform capabilities
  • DATA_ARCHITECTURE: Implement data mesh architecture enabling customers to maximize value from their data assets securely
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To enable organizations to connect with their customers through innovative cloud solutions that deliver exceptional customer experiences at global scale

MODERNIZE CORE

Rebuild foundation for AI-powered innovation at scale

  • MICROSERVICES: Decompose 5 critical monolithic services into 30+ microservices reducing deployment time by 65%
  • TECHNICAL_DEBT: Reduce legacy code by 25% across core platform services improving engineering velocity by 30%
  • AUTOMATION: Achieve 90% test automation coverage for all critical services reducing regression testing by 70%
  • RELIABILITY: Decrease production incidents by 40% while improving MTTR from 94 minutes to under 30 minutes
AI EVERYWHERE

Embed intelligent automation throughout every product

  • PLATFORM: Launch unified Einstein AI platform with consistent APIs across all clouds increasing developer adoption by 35%
  • COPILOTS: Deploy vertical-specific AI assistants for 7 industry clouds automating 40% of routine customer workflows
  • MODELS: Deliver 5 domain-specific foundation models with 30% better performance than generic LLMs on industry tasks
  • GOVERNANCE: Implement comprehensive AI governance framework ensuring 100% compliance with emerging regulations
EMPOWER BUILDERS

Create world-class developer experience for ecosystem

  • PLATFORM: Redesign developer portal improving time-to-first-app from 7 days to under 24 hours for new developers
  • SDK: Release next-gen unified SDK across all clouds reducing integration code by 60% for partners and customers
  • MARKETPLACE: Expand app ecosystem by 30% through improved developer tooling and AI-assisted app development
  • COMMUNITY: Grow active developer community by 50% through enhanced education, events and contribution programs
DATA ADVANTAGE

Unlock full value of customer data through modern architecture

  • ARCHITECTURE: Implement data mesh architecture enabling secure cross-cloud data access with 90% less integration code
  • INTELLIGENCE: Deploy ML-powered data quality services improving customer data accuracy by 40% automatically
  • GOVERNANCE: Launch comprehensive data governance toolkit ensuring 100% compliance with global privacy regulations
  • ACTIVATION: Reduce time to insight from 13 days to 24 hours through automated data preparation and discovery
METRICS
  • Annual Recurring Revenue (ARR): $40B by FY2025 (15% YoY growth)
  • Engineering Velocity: 2x feature delivery rate with 40% reduction in deployment time
  • AI Adoption: 70% of customers actively using Einstein AI capabilities weekly
VALUES
  • Trust - Nothing is more important than the trust of our customers
  • Customer Success - When our customers succeed, we succeed
  • Innovation - Continuously deliver groundbreaking solutions
  • Equality - Respect and value all individuals
  • Sustainability - Create a sustainable future for all stakeholders
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Align the learnings

Salesforce Engineering Retrospective

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To enable organizations to connect with their customers through innovative cloud solutions that deliver exceptional customer experiences at global scale

What Went Well

  • REVENUE: Exceeded quarterly targets with 19% YoY growth to $9.2B
  • CUSTOMERS: Added 827 net new enterprise customers, 12% above forecast
  • RETENTION: Achieved 93% dollar retention rate, highest in five quarters
  • MARGIN: Improved operating margin by 320 basis points through automation
  • AI: Einstein GPT drove $378M in incremental ARR, 34% above projections

Not So Well

  • ATTRITION: Engineering talent attrition rose to 17%, 5% above target
  • INNOVATION: Product release velocity declined 14% vs previous quarter
  • INCIDENTS: Production reliability issues increased 23% over prior period
  • ACQUISITION: Slack integration challenges continue to impact adoption
  • TECHNICAL_DEBT: Modernization initiatives fell 30% behind quarterly plan

Learnings

  • PLATFORM: Customers with 3+ clouds show 40% higher growth & retention
  • SERVICES: Implementation complexity remains top barrier to expansion
  • DEVELOPMENT: Cross-cloud features deliver 3x adoption vs single-cloud
  • ARCHITECTURE: Monolithic components causing 70% of scaling incidents
  • ONBOARDING: Engineer productivity doesn't reach peak until month seven

Action Items

  • MICROSERVICES: Accelerate decomposition of 5 core monolithic services
  • DEVELOPER: Launch improved platform SDK reducing time-to-value by 40%
  • DATA: Implement unified customer data platform across all cloud products
  • AUTOMATION: Expand CI/CD pipeline coverage to 90% of critical services
  • TRAINING: Deploy AI engineering certification program for all developers
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To enable organizations to connect with their customers through innovative cloud solutions that deliver exceptional customer experiences at global scale

Strengths

  • FOUNDATION: Einstein AI platform provides established AI infrastructure reaching 200B+ predictions monthly across customer base
  • DATA: Unparalleled B2B enterprise data repository with 10+ trillion customer interactions providing unique AI training advantage
  • INTEGRATION: AI capabilities already embedded within core products allowing for rapid iteration and expansion of use cases
  • TALENT: Strong data science team with 400+ AI specialists and established ML operations practices for continuous improvement
  • TRUST: Industry-leading responsible AI framework emphasizing transparency, ethics and governance critical for enterprise adoption

Weaknesses

  • FRAGMENTATION: AI capabilities scattered across acquisitions with inconsistent integration patterns causing developer friction
  • TOOLING: Internal AI development tools lag behind open source alternatives, increasing time-to-market by 35% for new capabilities
  • FOUNDATION_MODELS: Limited investment in proprietary foundation models compared to hyperscalers and AI-native competitors
  • COMPUTE: Infrastructure not optimized for large-scale AI workloads, with inference costs 27% higher than industry benchmarks
  • SKILLS_GAP: Insufficient generative AI expertise across engineering teams slowing adoption of advanced techniques

Opportunities

  • GEN_AI: Integrate generative AI capabilities across all products to automate 40% of routine customer workflows
  • COPILOTS: Develop specialized AI assistants for each cloud to increase user productivity by 30% and adoption by 22%
  • API_ECONOMY: Create AI-powered API marketplace enabling new monetization channels worth potential $2B+ annually
  • MULTIMODAL: Expand AI capabilities beyond text to voice, image and video unlocking new use cases for 65% of customers
  • DATA_ENRICHMENT: Use AI to enhance customer data quality automatically, improving analytics outcomes by 45%

Threats

  • HYPERSCALERS: Cloud providers deploying extensive foundation models at 10x our scale with deeply integrated infrastructure
  • STARTUPS: AI-native competitors building vertical solutions with 70% lower cost structures threatening established products
  • OPEN_SOURCE: Rapidly improving open-source AI models reducing barriers to entry for potential competitors
  • PRIVACY: Evolving regulations around AI training data could restrict use of customer data for model improvements
  • DIFFERENTIATION: Risk of AI features becoming commoditized as capabilities standardize across the industry

Key Priorities

  • UNIFIED_PLATFORM: Consolidate fragmented AI capabilities into unified Einstein platform with consistent developer experience
  • VERTICAL_MODELS: Develop industry-specific foundation models that leverage unique data advantages in enterprise workflows
  • COPILOT_ECOSYSTEM: Create comprehensive AI assistant strategy that enhances productivity across all roles and products
  • HYBRID_APPROACH: Implement strategic combination of proprietary models and integrated open-source models for optimal value