Chapter 01
Why most enterprise AI fails
Pilots without architecture, demos without security, and tools without governance. The recurring patterns we see across industries — and the early signals that an AI program is heading for trouble.
The Enterprise AI Playbook
Twenty chapters on scaling advanced AI inside the enterprise.
Written for boards, CIOs, CISOs, CTOs, COOs, and the operators who actually have to make this work. The thinking behind every MXP engagement, written down.
Chapter 02
AI as the new operating layer
Why AI has moved from feature to operating layer — and what that demands from leadership, architecture, security, and the operating model.
Chapter 03
The MXP Six-Layer Scaling Framework
A structured way to think about strategy, architecture, security, data, operations, and governance as a single, coherent system.
Chapter 04
Building an enterprise AI strategy
How to translate AI ambition into an executable plan — value mapping, sequencing, capability planning, and risk-aware investment decisions.
Chapter 05
Designing the AI architecture
Reference architecture for the model gateway, retrieval, agent runtime, identity, and observability — built to be operated, not just diagrammed.
Chapter 06
AI security as its own discipline
Why AI security is not just cybersecurity. The new threat surface, the new control set, and the new operating practices required to defend it.
Chapter 07
Identity and access for AI
Identity is the new perimeter for AI. Designing authorization, delegation, and audit for non-human actors that act on behalf of humans.
Chapter 08
Data foundations for AI
Curated, permissioned, observable knowledge that AI is allowed to use — and not. The data work that determines AI quality and AI risk simultaneously.
Chapter 09
Agentic AI without governance is risk
Designing agentic AI from the runtime up — with identity, scope, audit, escalation, and human oversight built in from the first iteration.
Chapter 10
Prompt injection and the new threat model
Working through realistic enterprise threat scenarios for prompt injection, data exfiltration, and agent abuse — and the design patterns that defend against them.
Chapter 11
Governance for the enterprise AI program
Standing up a governance operating model — registries, classifications, review workflows, and assurance reporting that boards and regulators accept.
Chapter 12
Shadow AI and how to handle it
A practical posture toward shadow AI: visibility first, risk-based remediation second, and a path to bring high-risk usage into the sanctioned environment.
Chapter 13
AI risk reporting for the board
How to give the board a credible, repeatable read on AI risk — without theater, and without burying the real issues.
Chapter 14
Operating the AI program
The cadences, metrics, roles, and decision rights that keep an enterprise AI program honest as it scales.
Chapter 15
AI in customer-facing operations
Where AI most often touches customers, and the controls required to deploy it without creating reputational or compliance risk.
Chapter 16
AI in security operations
Augmenting the SOC with AI — detection, triage, and response — under proper governance and with clear human checkpoints.
Chapter 17
Vendor and supply chain risk for AI
How to evaluate AI features in vendor products and AI-driven third parties — and how to keep that risk assessment current as products evolve.
Chapter 18
Talent and operating capability
The team you actually need to run enterprise AI safely — not the team that runs pilots, and not the team that runs traditional IT.
Chapter 19
Regulatory readiness
How to align the AI program with the regulatory landscape that is now forming — without paralyzing the business or doing it twice.
Chapter 20
The next 24 months
A pragmatic forward look — what enterprises that get AI right will look like, and what those that do not will be forced to explain.
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