Enterprise AI Playbook

Seven field guides on operationalizing AI in the enterprise — four lifecycle stages (assess, decide, build, advance) around a spine of three cross-cutting capabilities (governance, data, security). Each part ships as an architecture infographic, a deep-dive PDF you can hand to your team, and a checklist — written from twenty-plus years of Fortune 500 platform work, in the same vendor-neutral spirit as the rest of this site.

By Tom Ward, Enterprise Architect — Cloud & AI

• Part 1 of 7 PDF

  Assessing the AI Stack from Infrastructure to Interface

  Where the playbook starts: a vendor-neutral reference architecture for taking stock of what you actually have. Five layers — compute & networking, data platform, model layer, orchestration, experience — scored on a four-level maturity scale, with security, cost, observability, and portability assessed at every layer. Know what you have, then decide what to build.

  Read part 1
• Part 2 of 7 PDF

  Aligning AI Initiatives with Business Readiness

  A readiness model for matching AI ambition to organizational capacity across five dimensions — strategic fit, data foundation, platform & skills, process fit, and risk & governance — multiplied by the change capacity nobody budgets for. Readiness Gap = Ambition − Capacity.

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• Part 3 of 7 PDF

  Operationalizing LLMs as Enterprise Tools

  A six-layer operating model for taking large language models from promising pilot to governed, production-grade enterprise capability — demand, models, grounding, guardrails, delivery, and LLMOps, with governance as the spine.

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• Part 4 of 7 PDF

  Leading Agentic AI-Driven Workflow Change

  What changes when AI stops suggesting and starts acting. A five-rung autonomy ladder (Assist → Draft → Propose → Supervise → Autonomous) and the five design decisions — delegation, permissions, human checkpoints, reversibility, trust — that must sharpen at each rung, over a human control plane that scales with autonomy.

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• Part 5 of 7 PDF

  Establishing Ethical, Accountable AI Governance

  How to turn AI principles into platform-enforced controls across five pillars — accountability chain, principles made testable, model risk management, controls as code, and evidence & audit — anchored to the NIST AI RMF and mapped to the EU AI Act and ISO/IEC 42001 so regulators recognize the vocabulary.

  Read part 5
• Part 6 of 7 PDF

  The Data & Knowledge Foundation

  A cross-cutting capability, not a lifecycle stage. Five pillars for data that's actually ready to serve AI — sourcing & ingestion, quality & curation, governance & rights, retrieval & serving, and knowledge structure — plus an 8-point AI-ready data test. Poor data quality is Gartner's #2 reason GenAI projects stall; your data is also the one advantage a competitor can't buy.

  Read part 6
• Part 7 of 7 PDF

  Securing the AI Attack Surface

  A cross-cutting capability, not a lifecycle stage. Five pillars for defending enterprise AI end to end — input defense, data & supply chain, access & least privilege, output & content safety, and detection & response — plus a 10-control AI Security Baseline mapped to OWASP's LLM Top 10 and MITRE ATLAS. Prompt injection has no perfect defense; agents that act multiply the blast radius.

  Read part 7