AI Labs by The Ops Toolbox
Decision guides
Opinionated guides for architecture workshops and steering forums. Each opens with an executive summary for sponsors, then technical depth with links to official documentation.
Worked examples and decision guides you can run in reviews and pilots — an evidence base from The Ops Toolbox.
15
Guides
Program, architecture, and governance
Program & sponsorship
Sponsorship, pilots, and metrics.
AI councils & champion programs
How to stand up a cross-functional AI council, a distributed champion network, and an operating rhythm that scales pilots without losing control.
6 related examples
Scoping a six-week pilot
How to pick one workflow, define stop rules, and leave with metrics executives will fund, pivot, or kill.
4 related examples
Measuring AI success
Business KPIs and technical signals that belong in the same dashboard, so finance and engineering do not talk past each other.
4 related examples
Coexisting with Microsoft Copilot
Copilot for M365 breadth; custom Azure-backed patterns for governed, system-specific workflows with citations and approvals.
4 related examples
Architecture & patterns
Patterns, stacks, and model choice.
RAG vs fine-tuning
When retrieval is enough, when custom weights earn their cost, and how to compare both with the same golden questions.
4 related examples
Agent vs durable workflow
Chat agents for ambiguity and tool selection; durable workflows for steps that must survive retries, time, and human approval.
4 related examples
Choosing a cloud anchor
Pick the control plane your procurement, identity, and data residency commitments already favour, then compare patterns, not logos.
4 related examples
Vendor and model selection
How to run a defensible bake-off when procurement, platform, and the business each have a favourite model.
4 related examples
Governance, risk & security
Risk, security, cost, and production gates.
Production readiness checklist
What pilots skip, and what security, legal, and operations ask for before go-live.
5 related examples
AI security controls & practices
A practical control set for generative AI: identity, data, prompt injection, tools, logging, and secure change management, for councils, champions, and engineering.
7 related examples
AI cost controls
Token economics, routing, and guardrails so pilots do not become open-ended invoices.
3 related examples
Data privacy and retention
PII, retention, and regional boundaries for prompts, logs, and indexes, without blocking sensible pilots.
3 related examples
Human oversight and approval
When humans must stay in the loop, how to design queues, and how to earn automated writes later.
4 related examples
Security review evidence pack
What InfoSec and procurement typically ask for before production, and how to answer with demos plus artefacts. Pair with the AI security controls guide for ongoing practices.
4 related examples
Frameworks map
How NIST AI RMF, ISO 42001, and vendor responsible-AI programs map to concrete patterns you can demo.
4 related examples
Next step
Plan your next pilot
Worked examples and decision guides you can run in reviews and pilots — an evidence base from The Ops Toolbox.
Prefer the web form? The Ops Toolbox.
- One workflow, clear metrics
- Your cloud, your keys
- Written handoff, not dependency