Turn rough AI ideas into work teams can actually build.

AI Delivery Ops Skills is an MVP skill pack for producing five delivery artifacts that help teams define scope, reduce uncertainty, hand work to engineering, define evaluation and surface governance concerns before launch.

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Reusable delivery operating layer

Five artifacts. One clearer handoff trail.

Discover

Phased PRD

Scope, phases, acceptance criteria, risks and open questions.

Reduce uncertainty

Prototype Spike

Hypotheses, experiments, evidence and decision gates.

Build

Implementation Handoff

Slices, interfaces, tests, rollout and review plan.

Measure

Evaluation Plan

Datasets, metrics, baselines, rubrics and reporting.

Govern

Governance Review

Risks, controls, owners and go/no-go gates.

Why this exists

AI prototypes move quickly. Delivery evidence often does not.

Teams can move from a rough AI idea to a prototype before they have clear scope, evaluation criteria, implementation handoffs or governance decisions. This skill pack provides a lightweight operating layer that makes those artifacts explicit.

It is intentionally not a replacement for product judgment, engineering review, legal review, security review or production approval.

1Shape the problem

Turn a rough requirement into a phased PRD.

2Reduce uncertainty

Run a prototype spike before committing to a full build.

3Make the handoff explicit

Define interfaces, slices, tests, rollout and review.

4Define quality before claiming it

Set datasets, metrics, baselines and rubrics.

5Review governance before launch

Surface cost, privacy, security, oversight and audit concerns.

How the pieces connect

A delivery operating model around the software.

RunbookOps and CostOps demonstrate concrete AI applications and execution controls. AI Delivery Ops captures the repeatable artifacts that help teams plan, evaluate, govern and hand off similar work.

Product-to-engineering continuity

Phased PRDs and implementation handoffs keep scope, interfaces, tests, rollout and risks connected as work moves between roles.

Evaluation before celebration

Evaluation plans define datasets, metrics, baselines and reporting formats before teams claim a quality improvement.

Governance as a delivery artifact

Risk, oversight, privacy, security, cost and audit questions receive owners and decision gates rather than becoming late surprises.

Lightweight by design

The pack is an MVP team-enablement resource; it is meant to sharpen judgment and handoffs, not replace professional review.

Evidence boundary

Templates improve the delivery trail; they do not approve the product.

The skill pack is an MVP for portfolio and team-enablement use.
It does not replace product, engineering, legal, security or production review.
Human review remains required before execution and launch decisions.
The repository contains no credentials, private customer data or production-only assumptions.
Its value is repeatability: clear artifacts, explicit assumptions and inspectable handoffs.

Delivery operating model

Structure the work around decisions, evidence and handoffs.

AI Delivery Ops five-artifact workflow — Five core delivery artifacts: phased PRD, prototype spike, implementation handoff, evaluation plan and governance review.

AI Delivery operating model — Illustrative Plan → Build → Assure operating model that connects delivery artifacts to ongoing evaluation and governance.

Relevant service

Need an AI initiative to become a governed delivery plan?

Use this operating model as a starting point for product discovery, prototype planning, implementation handoff, evaluation and governance.

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