The layer-by-layer map showing how every playbook in this series fits together into one enterprise AI reference architecture, with build sequencing, governance, and a reusable architecture readiness scorecard.
A decision framework for building the people and organizational capability behind an enterprise AI program — build-vs-hire strategy, operating model design, maturity model, governance, cost, and a reusable talent strategy scorecard.
A decision framework for building a deliberate multi-model, multi-vendor LLM strategy — maturity model, readiness checklists, build-vs-buy, governance, security, cost, and a reusable vendor/model selection scorecard.
A decision framework for building AI cost visibility, attribution, and optimization practice — maturity model, readiness checklists, build-vs-buy, governance, security, and a reusable AI FinOps maturity scorecard.
A decision framework for building runtime content-safety and policy-enforcement guardrails for AI systems — maturity model, readiness checklists, build-vs-buy, governance, security, cost, and a reusable guardrails maturity scorecard.
A decision framework for standing up an enterprise-wide AI governance program — risk classification, accountability, and regulatory compliance, with a maturity model, readiness checklists, build-vs-buy, security, cost, and a reusable governance scorecard.
A decision framework for threat-modeling and defending AI agents and LLM applications — prompt injection, tool-output poisoning, and agentic attack patterns, with a maturity model, readiness checklists, build-vs-buy, governance, and a reusable security posture scorecard.
A decision framework for instrumenting AI agents and RAG pipelines with the tracing, logging, and quality signals production systems need — maturity model, readiness checklists, build-vs-buy, governance, security, cost, and a reusable observability scorecard.
A decision framework for designing where and how human oversight gates AI agent workflows — risk-tiered maturity model, readiness checklists, build-vs-buy, governance, security, cost, and a reusable oversight design scorecard.
A decision framework for designing how AI agents retain and use information across sessions — tiered memory maturity model, readiness checklists, build-vs-buy, governance, security, cost, and a reusable memory architecture scorecard.
A decision framework for assessing and building the data foundation production AI depends on — maturity model, readiness checklists, build-vs-buy, governance, security, cost, and a reusable readiness scorecard.
A decision framework for operationalizing LLM-powered features and agents in production — prompt versioning, continuous evaluation, monitoring, build-vs-buy, governance, security, cost, and a reusable LLMOps maturity scorecard.