SWOT
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Eli Lilly Engineering

To revolutionize pharmaceutical development by creating a world-class technology platform that accelerates life-saving medicines to patients

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Align the strategy

Eli Lilly Engineering SWOT Analysis

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To revolutionize pharmaceutical development by creating a world-class technology platform that accelerates life-saving medicines to patients

Strengths

  • INFRASTRUCTURE: Advanced cloud architecture enabling faster simulations
  • TALENT: Leading ML engineers and computational biologists
  • PIPELINE: Strong analytical capabilities for clinical trial data
  • PARTNERSHIPS: Strategic tech alliances with AWS, Google Cloud
  • RESEARCH: Robust data management systems supporting research

Weaknesses

  • INTEGRATION: Legacy systems causing data silos across departments
  • SECURITY: Gaps in cybersecurity protocols for sensitive research
  • TALENT: Shortage of specialized AI/ML engineers in drug discovery
  • AGILITY: Slow software development lifecycle compared to tech firms
  • DOCUMENTATION: Inadequate technical documentation for drug research

Opportunities

  • AI: Apply AI to reduce drug discovery timeline by 40%
  • CLOUD: Migrate all clinical trials to cloud platforms by 2026
  • AUTOMATION: Expand lab automation to reduce manual processes 65%
  • DATA: Implement real-world evidence platforms for post-market data
  • DIGITAL: Develop patient-centered digital therapeutics companions

Threats

  • COMPETITION: Tech giants entering pharmaceutical R&D space
  • REGULATION: Stricter data privacy regulations impacting research
  • CYBERSECURITY: Growing sophistication of pharmaceutical IP theft
  • TALENT: Fierce competition for top AI/ML talent from tech sector
  • COMPLEXITY: Increasing computational demands of modern drug design

Key Priorities

  • TRANSFORMATION: Modernize tech stack to eliminate data silos
  • TALENT: Develop specialized AI/ML recruitment and training program
  • AUTOMATION: Accelerate lab automation to reduce cycle times
  • SECURITY: Strengthen cybersecurity to protect intellectual property
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Align the plan

Eli Lilly Engineering OKR Plan

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To revolutionize pharmaceutical development by creating a world-class technology platform that accelerates life-saving medicines to patients

UNIFY DATA

Create seamless data ecosystem across entire R&D pipeline

  • ARCHITECTURE: Deploy unified data lake with 100% research data accessibility by Q3
  • INTEGRATION: Eliminate top 5 data silos with new API platform serving 2,000+ researchers
  • VALIDATION: Implement data quality framework with 99.9% validation rate across platforms
  • GOVERNANCE: Deploy master data management system covering 100% of research entities
AI TALENT SURGE

Build world-class AI/ML pharmaceutical engineering team

  • RECRUITMENT: Hire 50 specialized AI/ML engineers with pharma experience by EOY
  • TRAINING: Launch AI Academy with 500+ engineers completing advanced ML certification
  • RETENTION: Improve tech talent retention to 92% through career advancement programs
  • DIVERSITY: Increase underrepresented groups in tech roles to minimum 40% of new hires
ACCELERATE R&D

Cut drug discovery timeline through automation & ML

  • AUTOMATION: Deploy next-gen lab automation reducing experiment cycle time by 50%
  • PREDICTION: Launch ML models for candidate prediction with 80% improved accuracy
  • SIMULATION: Scale quantum computing simulation capacity by 300% for molecule testing
  • DEPLOYMENT: Implement continuous deployment pipeline reducing release cycles by 65%
FORTRESS

Build impenetrable security for intellectual property

  • ZERO-TRUST: Implement zero-trust architecture across 100% of research platforms
  • ENCRYPTION: Deploy homomorphic encryption for 95% of sensitive research computations
  • MONITORING: Launch AI-driven threat detection reducing incident response time by 75%
  • COMPLIANCE: Achieve SOC2 Type II certification for all clinical and research systems
METRICS
  • DEVELOPMENT TIME: Reduce discovery-to-IND submission time by 30%
  • ENGINEERING VELOCITY: Increase development team velocity by 45%
  • AI MODEL ACCURACY: Achieve 85%+ accuracy in drug candidate prediction models
VALUES
  • Integrity: We conduct business with honesty and transparency
  • Excellence: We pursue innovation and continuous improvement in all we do
  • Patient-Focused: We place patients at the center of every decision
  • Collaboration: We foster inclusive teamwork across disciplines
  • Speed: We move with urgency to bring new medicines to patients
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Align the learnings

Eli Lilly Engineering Retrospective

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To revolutionize pharmaceutical development by creating a world-class technology platform that accelerates life-saving medicines to patients

What Went Well

  • AUTOMATION: Laboratory automation reduced experiment cycle time by 32%
  • CLOUD: AWS migration of clinical data improved analysis time by 41%
  • INTEGRATION: New data platform launched connecting 4 major research areas
  • PRODUCTIVITY: Engineering team velocity increased 28% through DevOps

Not So Well

  • TALENT: Critical AI engineering positions remained unfilled for 6+ months
  • SECURITY: Three significant security incidents requiring remediation
  • TECHNICAL DEBT: Legacy system maintenance consumed 38% of IT resources
  • INTEGRATION: Cross-platform data sharing still requires manual processes

Learnings

  • RECRUITMENT: Specialized pharma-tech recruiting strategy needed urgently
  • ARCHITECTURE: Modular system design enables faster regulatory approval
  • COLLABORATION: Cross-functional teams accelerate ML model deployment
  • VALIDATION: Automated testing critical for maintaining compliance

Action Items

  • TALENT: Launch pharma-tech academy with guaranteed interview program
  • SECURITY: Implement zero-trust architecture across all research systems
  • MODERNIZATION: Accelerate legacy system replacement with cloud services
  • AUTOMATION: Expand CI/CD pipeline coverage to all critical applications
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Drive AI transformation

Eli Lilly Engineering AI Strategy SWOT Analysis

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To revolutionize pharmaceutical development by creating a world-class technology platform that accelerates life-saving medicines to patients

Strengths

  • MODELING: Advanced protein-folding AI models accelerating discovery
  • INVESTMENT: Dedicated AI research center with $250M annual budget
  • PARTNERSHIPS: Key collaborations with AI research institutions
  • FOUNDATION: Strong computational biology expertise in key areas
  • DATA: Rich proprietary datasets from decades of clinical trials

Weaknesses

  • TALENT: Gap in specialized AI/ML talent for pharma applications
  • INTEGRATION: Isolated AI initiatives not connected to core pipeline
  • TRAINING: Limited high-quality training data for rare diseases
  • GOVERNANCE: Unclear AI governance and validation frameworks
  • ADOPTION: Resistance to AI-driven decision making in R&D teams

Opportunities

  • DISCOVERY: AI could reduce candidate identification time by 70%
  • TRIALS: Smart trial design could improve success rates by 25%
  • MANUFACTURING: AI optimization could reduce production costs 20%
  • PERSONALIZATION: AI for targeted therapies could expand portfolio
  • SAFETY: Predictive models could reduce adverse event risks by 35%

Threats

  • COMPETITION: Tech giants developing specialized pharma AI platforms
  • REGULATION: Uncertain FDA guidance on AI in critical drug decisions
  • EXPLAINABILITY: Challenge of interpreting complex AI recommendations
  • BIAS: Risk of biased algorithms affecting diverse patient groups
  • TRUST: Physician reluctance to accept AI-generated insights

Key Priorities

  • INTEGRATION: Create unified AI platform across drug lifecycle
  • TALENT: Launch specialized AI/pharma recruitment initiative
  • VALIDATION: Develop rigorous AI validation framework with FDA
  • ADOPTION: Create AI literacy program for all R&D personnel