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Solution Architecture for AI: Why Most AI Projects Fail Before the Model Ever Runs - and the Architecture Playbook That Fixes It
Paperback
Publisher: Independently Published
Published: Aug 3 2026
Pages: 196
Weight: 0.59
Height: 0.42 Width: 6.00 Depth: 9.00
Language: English
Most AI projects do not fail because the model is incapable. They fail because the surrounding architecture was never designed to support secure, reliable, scalable, and economically sustainable AI.
Solution Architecture for AI provides enterprise architects, technology leaders, and AI practitioners with a practical playbook for turning promising AI capabilities into dependable business solutions.
Rather than treating the model as the complete solution, this book examines the full architecture around it: business outcomes, workflows, data, knowledge retrieval, integration, security, governance, evaluation, observability, infrastructure, operating models, and lifecycle ownership.
Inside, you will learn how to:
- translate business opportunities into measurable and architecture-ready AI use cases;
- select the right pattern across predictive AI, generative AI, RAG, agentic AI, multimodal AI, and conventional automation;
- design secure and permission-aware data and knowledge architectures;
- establish clear boundaries for AI agents, tools, memory, and autonomy;
- build evaluation, evidence, identity, and governance into the architecture from the beginning;
- manage model dependencies, deployment options, operating costs, and provider risk;
- move AI initiatives beyond prototypes and pilot purgatory;
- apply a practical 90-day roadmap, decision frameworks, maturity models, and architecture review tools.
The book introduces original enterprise frameworks, including the AI Solution Architecture Coherence Test, the Bounded Intelligence Model, the Evidence Plane, the Autonomy Ladder, and the Architecture Confidence Score.
Written for solution architects, enterprise architects, CIOs, CTOs, chief data and AI officers, engineering leaders, consultants, security professionals, and transformation executives, this is a practical reference for designing AI systems that create measurable value while remaining controlled, explainable, and operationally resilient.
The model is not the solution. The architecture is.
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