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AI Pilots to Production

Anatomy of Failure: Why 88% of AI Pilots Never Reach Production
  • ○ The Illusion of Readiness: 1% vs. 99%
  • ○ The 95% Trap: No Impact on P&L
  • ○ Technical Debt by Default
The Business Contract and Baseline Lock-In
  • ○ Forming the Contract Before Kickoff
  • ○ Decomposing Success Metrics
  • ○ The Budget Gateway
FinOps, Infrastructure, and AI Unit Economics
  • ○ Scale Shock and TCO (Total Cost of Ownership)
  • ○ Infrastructure SLAs and Cold Starts
  • ○ Architectural Cost Optimization
Architectural Wrappers and the AI SDLC
  • ○ Vendor Lock-in and LLM Gateways
  • ○ Multi-Agent Chaos and A2A Protocols
  • ○ Spec-Driven Development in AI
  • ○ Continuous Evaluation
  • ○ Deterministic Paths and Fallbacks
Data Engineering and Overcoming Entropy
  • ○ The Death of Naive RAG and the Shift to GraphRAG
  • ○ Training-Serving Skew
  • ○ Integrating with Enterprise Legacy
  • ○ Freshness and Conflict Resolution
Security-by-Design, Legal, and Compliance
  • ○ Audit, Isolation, and Jailbreak Protection
  • ○ Role-Based Access Control (RBAC) in Vectors
  • ○ The Black Box Problem (Traceability & Audit Trail)
  • ○ Legal Freeze, IP, and Regulation (EU AI Act / GDPR)
Strategy, Governance, and Ownership
  • ○ The “SaaS Wipeout” Threat (Build vs. Buy)
  • ○ Shadow AI and Decentralized Budgets
  • ○ Committees vs. P&L (Passing the Baton)
  • ○ The Responsibility Matrix (Governance Roles)
Business Process Reengineering (BPR) and AI Product UX
  • ○ Chatbot Fatigue and UX Paradigms
  • ○ Controls before Autonomy
  • ○ Eliminating Manual Handoffs
  • ○ Failure Protocols and Bounded Autonomy
Change Management and Team Transformation
  • ○ Trust Penalty
  • ○ Cognitive Overload and Present Bias
  • ○ Designing Usage Guidelines
  • ○ Workforce Readiness and Adoption Metrics
Go-to-Production: Launch Plan and Release
  • ○ Pre-Release Architecture Audit
  • ○ Roll-out Strategy (Shadow Mode)
  • ○ Scaling and Transferring Autonomy

Strategy, Governance, and Ownership

Author

  • Grigoriy Dobryakov · Head of AI Engineering

    Builds AI systems that run how engineering and delivery actually operate — hands-on engineer, not just a manager.

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Topics

  • The “SaaS Wipeout” Threat (Build vs. Buy)
  • Shadow AI and Decentralized Budgets
  • Committees vs. P&L (Passing the Baton)
  • The Responsibility Matrix (Governance Roles)
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