Description
AI adoption is no longer blocked by model capability. It’s blocked by trust, governance, cost, and architecture decisions. This course series is designed for enterprise leaders and AI decision-makers who need to move beyond experimentation — and design AI systems that can scale legally, operationally, and economically. Across the chapters, you’ll learn how leading organizations are: • Designing sovereign, compliant LLM stacks • Building production-grade RAG architectures • Orchestrating agentic AI systems at enterprise scale • Engineering AI unit economics that CFOs actually approve This is not a tooling tutorial. It’s a decision framework for building AI systems your board, regulators, and customers can trust. What you’ll learn In the first four chapters, you’ll gain clarity on: • How to design Sovereign AI architectures aligned with the EU AI Act, GDPR, and NIS2 • Why RAG 2.0 is an architectural discipline — not a vector database choice • How LLM orchestrators and agent meshes are redefining enterprise operating models • How to reduce LLM operating costs by up to 10× using MoE, SLM cascades, and speculative decoding Which architectural decisions create long-term advantage — and which create hidden risk Each chapter builds decision intelligence you can apply immediately — whether you’re advising, buying, or building. Who this series is for This series is built for: • CIOs, CTOs, CDOs, CISOs • Enterprise architects and AI platform leaders • Board advisors and AI strategy leads • Tech vendors selling into regulated or enterprise markets If you’re responsible for AI outcomes, not experiments, this series is for you.