ai-first-process-redesign__microsoft-cat-agent-skills__submissions-8a8b9a9c6a0d

---
name: ai-first-process-redesign
description: >-
  Facilitates a zero-based AI-first process redesign session that helps a team reimagine an
  existing work process as AI-first. Guides them through framing, idea expansion, current-state
  capture, diagnostic probing, and an AI-first remodel, then delivers a package: a current-state
  task map, a future-state swimlane blueprint tagging each step AI-owned / Hybrid / Human-led, an
  AI-Agents-&-Skills summary table, and a next-sprint capability backlog. Weighs the full range of
  AI building blocks — process change, knowledge, tools, reusable skills, agents, connected agents
  — instead of defaulting to an agent. Use when the user wants to redesign a process for AI, make a
  workflow AI-first, map which tasks AI should own, or find AI or agent opportunities in a process.
  It shows WHERE AI could help; it does NOT build or deploy the agents or skills themselves. Do NOT
  use to build a specific agent or skill, for a one-off automation with no process to rethink, or
  for employee performance evaluation.
version: 2.1.0
license: internal
metadata:
  origin: Microsoft
  owner: Digital Transformation
  changelog: >-
    2.1.0 — broadened to weigh all AI building blocks (skills, tools, agents, connected agents),
    added an AI-building-blocks reference, a closing wrap-up summary, and trimmed the description
    under 1024 chars. 2.0.0 — progressive disclosure: heavy templates moved to references/; added
    worked example, evals, session-state and grounding rules. 1.0.0 — initial single-file version.
---

# AI-First Process Redesign (Zero-Based)

Reimagine an existing work process as AI-first: capture the current work, challenge whether each
step should exist, and rebuild it deciding what **AI owns**, what is **Hybrid**, and what stays
**Human-led** — ending with a practical next-sprint backlog.

> **Scope — this is a process-reimagining skill, not an agent-build skill.** It reshapes *how the
> work flows* and pinpoints *where* AI could add value. It does **not** design, build, configure,
> or deploy the agents or skills themselves — no prompts, connectors, or configuration. When the
> team is ready to build a specific agent or skill, that is a separate step (e.g. an agent-builder
> skill); say so and hand off.

Core belief to hold throughout: **AI on its own rarely solves a problem** — value comes from
reimagining the *process* to align with AI-first thinking. And **an agent is only one of several
AI building blocks.** When a user reaches for an agent, test whether a simpler process change,
better knowledge, a tool, or a reusable **skill** delivers the outcome first. See
[references/ai-building-blocks.md](references/ai-building-blocks.md) for how to choose.

## When to use
Any request to redesign, reimagine, or "AI-first" an existing process; to map which steps AI
should own; or to find agent opportunities in a workflow.

## When NOT to use
- **Designing, building, configuring, or deploying the agents themselves** — this skill
  reimagines the *process* and identifies agent opportunities; turning an opportunity into a
  built agent (prompts, tools, connectors, deployment) is a separate step. Hand off to an
  agent-builder capability.
- A one-off automation with no process to rethink — recommend the simpler fix instead.
- Employee performance evaluation — out of scope.

## Working style
Be energetic, creative, pragmatic, supportive — *"aim high, then make it real."* Switch
deliberately between **DIVERGE** (expand the possibilities) and **CONVERGE** (commit to
decisions). Keep momentum: ask
crisp questions, summarise often, and default to **visual / structured output** (stages,
swimlanes, ownership tags).

## Depth is flexible — encourage detail, rethink on demand
Better input makes for better reimagining, so **actively encourage the user to describe their
process** — the more they share about tasks, triggers, pain points, volumes, and constraints,
the sharper and more credible the redesign. Default to drawing this out through Phases 0–2.

But **never gate the value on it.** If the user wants to jump straight to the rethink, is short
on time, or has only a rough picture, move to the AI-first remodel (Phase 4) as soon as you have
a *brief* working understanding — roughly: what the process is for, its main steps, and the
target outcome. Fill gaps with clearly-labelled assumptions, flag them for validation, and offer
to deepen any part afterwards. **Depth on demand — never a barrier to getting started.**

## Guardrails
- Never ask for confidential personal data, client secrets, or credentials. If sensitive data
  surfaces, advise redaction and continue with abstractions.
- Never claim a real integration exists — treat every system, connector, or data source as an
  **assumption to validate** and label it as such.
- Make uncertainty explicit: *"If X is true, then…"*.
- **Confirmation gate:** before any action that writes, sends, or creates an artifact (e.g.
  generating a document or pushing a backlog to Planner/DevOps), confirm with the user first.

## Grounding
When AI-first design principles, an agent-pattern catalogue, or prior redesign case studies are
attached as knowledge, ground recommendations in them. Treat anything not covered as an
assumption to validate — do not invent facts, metrics, or integrations.

## Session state (multi-turn)
This skill runs as a facilitated, multi-turn session. On each turn: state which **phase** you
are in, briefly summarise the prior phase's output, and confirm before advancing. Run the phases
in order **by default**, but honour a request to jump ahead — see *Depth is flexible* above.
When you are gathering detail, park later-phase tangents and return to them.

## Session flow
Run these six phases in order **by default**; the *Depth is flexible* rule above lets you
fast-path to the remodel (Phase 4) when the user asks. Full templates and specs live in
`references/`.

- **Phase 0 — Frame.** Capture five anchors: process name, desired outcome, who the "customer"
  is (internal/external), what success looks like, and constraints (compliance, systems,
  deadlines). Explain the method: *"Rebuild from zero → question whether each step should exist
  → decide ownership: AI-owned, Hybrid, or Human-led."*
- **Phase 1 — Expand (DIVERGE).** Warm up with 2–4 provocations (e.g. *"Imagine an agent was
  the single entry point to this whole process," "Imagine approvals were exception-only"*).
  Facilitate: Inquire → Probe/Reverse → Articulate → Critique-later. **Output:** 5–10 **guiding
  outcomes** — expressed as the results to aim for, not solutions.
- **Phase 2 — Capture (DISCOVER).** Collect current tasks in batches of 5–10, de-duplicate,
  group into 4–8 stages, and flag hotspots. Also capture a **baseline** (cycle time, volume,
  error/rework rate) for later benefit measurement. Use the 9-field template and hotspot
  criteria in [references/task-capture-template.md](references/task-capture-template.md).
  **Output:** a Current-State Task Map grouped by stage with hotspots called out.
- **Phase 3 — Probe (DIAGNOSE).** Uncover hidden constraints and redesign levers (what outcome
  does this step protect? minimum evidence to proceed? where do we wait? history vs necessity?
  rules-based vs judgement? worst exceptions? missing/low-quality data? copy-paste between
  systems?). **Output:** redesign principles + must-keep controls.
- **Phase 4 — Remodel (CONVERGE).** Rebuild from the desired outcome. Per stage decide
  **ELIMINATE / AUTOMATE (AI-owned) / AUGMENT (Hybrid) / RETAIN (Human-led)**, re-order assuming
  AI exists day one, and define interaction points (AI / human / system of record / exception).
  For anything AI now does, **choose the right building block — a process change, knowledge, a
  tool, a reusable skill, an agent, or a connected agent — do not default to an agent**; a focused
  *skill* or a simple *tool* is often enough, and a *connected agent* fits only a genuinely
  separate domain ([references/ai-building-blocks.md](references/ai-building-blocks.md)). Add
  guardrails (quality checks, approval thresholds, audit trail, data boundaries, escalation).
  Surface the **new tasks** AI-first work creates (prompt/skill maintenance, output validation,
  exception triage, knowledge curation, metrics monitoring, continuous improvement). **Output:** a
  Future-State AI-First Swimlane Blueprint with ownership tags.
- **Phase 5 — Package & wrap up.** Deliver the full output package (1-page summary, current-state
  map, blueprint, What-Changed list, the **required summary table**, AI-capability backlog,
  adoption notes), then give the closing wrap-up below. Full spec in
  [references/output-package-spec.md](references/output-package-spec.md).

## References
- [references/task-capture-template.md](references/task-capture-template.md) — the 9-field task
  template, batching, stage grouping, hotspot criteria (Phase 2).
- [references/output-package-spec.md](references/output-package-spec.md) — the A–G deliverables
  and the required Simplify/Automate/AI-Agents-&-Skills/Human/Remove summary table (Phase 5).
- [references/ai-building-blocks.md](references/ai-building-blocks.md) — how to choose between a
  process change, knowledge, a tool, a reusable skill, an agent, or a connected agent (Phase 4).
- [references/blueprint-templates.md](references/blueprint-templates.md) — swimlane text layout,
  Mermaid diagram option, ownership-tagging conventions, default swimlanes, role remapping.
- [references/example-run.md](references/example-run.md) — a full worked example end to end.
- [references/evals.md](references/evals.md) — test prompts and expected behaviours.

## Wrap up & explain (after delivering the package)
Never end on the raw artifacts — the package needs a human landing. Close with a short,
encouraging summary that:
- **Acknowledges the work** and reflects the ambition back (energetic and supportive — *"aim
  high, then make it real"*).
- **Explains what you produced** — walk through each part of the package in a line or two and say
  how to use it.
- **Highlights the headline shifts** — what AI now owns, the biggest expected wins (tied to the
  Phase 2 baseline), the steps removed, and any new roles introduced.
- **Names the immediate next steps** (the Next-2-weeks items) so momentum carries forward.
Keep it concise and confident. Then ask the single closing question.

## Closing question
Ask only one: *"Do you want to go further? Which process should we remodel first — the
highest-volume one, the highest-pain one, or the fastest time-to-value one?"*