Deploy AI Without Surrendering Control: A Field Playbook for Operations
ATLANTA, GA, UNITED STATES - January 5, 2026. AI adoption isn't failing because models are weak. It's failing because leaders ship systems without governance. That's the core message of "AI Operations & Usage Playbook: How to Think, Deploy, Govern, and Live With Artificial Intelligence," released by Intellectual Enlightenment Press, an imprint of PeachWiz, Inc.
"Authority can't be delegated to machines, only exercised through them." - Alexious Fiero
This book treats AI like what it is: probabilistic prediction systems, not reasoning minds. The warning is blunt: "The danger isn't artificial intelligence. It's artificial authority." If you run operations, you need AI that's observable, governable, and accountable - with humans still in the pilot seat.
What Operations Leaders Will Get
- Clarity on AI's limitations: tokens, context windows, and why hallucinations happen
- Model selection with update risk management and rollbacks
- Prompting as engineering: Role → Context → Intent → Constraints → Output Contract
- RAG done right, evaluation systems that catch drift, and agent governance
- Auditability, traceability, and accountability mapping tied to real workflows
- Role-based playbooks for executives, engineers, analysts, and creators
- How to live with AI without cognitive atrophy or dependence
Operate AI Like Infrastructure
AI is now core infrastructure. Treat it like you would a payments rail or an ERP: controlled changes, clear owners, measurable outcomes, and logs that stand up to audits. If the system can't be inspected or explained, it can't own a decision.
AI Is Infrastructure. Govern It or Ship Liability.
Blueprint Highlights (Directly Usable in Your Org)
- Governance guardrails: decision rights matrix, approval tiers, exception handling
- Change control: model/version registry, release notes, A/B and canary, rollback plans
- Evaluation: golden datasets, scenario tests, red teaming, bias and safety checks
- Observability: prompts, context, outputs, latency, cost, and feedback all logged
- Human-in-the-loop: pre-approval for high-risk actions, post-hoc review for low-risk
- Vendor risk: data residency, retention, update cadence, SLAs, incident playbooks
- Accountability mapping: every automated output ties to a human steward
Practical Rollout Checklist
- Define outcomes: the metric that must move, the metric that must not regress
- Choose scope: one process, one KPI, one accountable owner
- Select model(s) with an exit strategy: compatible alternatives and fallback rules
- Design prompts as contracts: inputs, constraints, and expected output schema
- Add retrieval (RAG) only if your data beats the base model's prior
- Stand up evals before launch; ship with monitors and alert thresholds
- Gate high-risk actions; document human approval points
- Launch with a canary cohort; review logs daily; iterate with evidence
Why This Matters to Ops
AI will touch escalations, customer comms, pricing, compliance, and finance close. Without audit trails and change control, you're pushing invisible code into production. With them, you get leverage without losing accountability.
The playbook is blunt about trade-offs: speed without oversight creates shadow AI and legal risk. Governance without agility kills adoption. The goal is disciplined speed - small, observable releases that compound.
Inside the Playbook
Expect plain language on tokens, context windows, and how to keep hallucinations from hitting customers. Concrete patterns for RAG that don't implode under domain nuance. Evaluation pipelines that make "it seems better" unacceptable as a launch criterion.
It also includes role-based guidance: how executives set policy, how engineers enforce it in code, how analysts validate outputs, and how creators protect their voice while moving faster.
Availability
Hardcopy, Paperback, Audiobook, Ebook.
Further Reading and Standards
Upskill Your Team
If you're building role-based enablement and certifications for your ops staff, explore:
The message is simple: keep humans accountable, make systems observable, and treat AI as infrastructure. Authority stays with you - the system is just the tool you use to apply it.
Your membership also unlocks:
