Program syllabus

Workflow AI Design Blueprint

Turn messy processes into automation-ready workflows with clear owners, steps, tools, and metrics. Designed for teams who want AI assistants that actually move work forward, not just answer questions.

Program overview · Audio

A short overview of the program: who it's for, what we cover, and how to get the most value out of it as a busy professional.

What we’ll cover

Use this as a playbook for taking one important process from ‘tribal knowledge’ to an automation-ready blueprint.

Module 1

Process discovery

  • • Identify the right workflow to start with
  • • Map actors, triggers, and desired outcomes
  • • Capture the real-world edge cases that break systems

Module 2

Designing human-in-the-loop

  • • Where humans must review, approve, or override
  • • Tiering: low-risk automation vs. human review
  • • Designing safe escalation paths

Module 3

Tools & routing

  • • Choosing which systems own which steps
  • • When to use assistants vs. API-first automation
  • • Routing patterns for multi-step workflows

Module 4

Blueprint & metrics

  • • Produce a sharable workflow blueprint doc
  • • Define success metrics & guardrails
  • • Hand-off to engineering & ops teams

1. Process discovery & mapping

We start with one real workflow and pull it out of people’s heads and Slack threads.

Together we’ll choose one target workflow — onboarding, support escalations, reporting, whatever has the most leverage — and map how it really works today.

  • • Trigger points, inputs, and ‘done’ definitions
  • • Roles and responsibilities across teams
  • • Happy path vs. the actual path
  • • Where work currently gets stuck or dropped

Artifact: current-state workflow map

You’ll leave with a simple diagram + notes that show how the workflow really behaves in production — not the idealized version in decks.

This becomes the baseline for automation and for aligning stakeholders on what we’re actually improving.

2. Designing human-in-the-loop steps

Good workflows don’t remove humans; they reserve them for the judgment calls.

  • • Classifying steps: automate, assist, or escalate
  • • Approval patterns and second-eyes check points
  • • Designing “guardrail questions” for assistants
  • • Capturing feedback loops into the workflow

Artifact: human-in-the-loop matrix

We build a matrix that specifies which steps are fully automated, which are assistant-assisted, and which remain human-only — plus who is on the hook for each.

3. Tool selection & routing patterns

We decide which systems own each part of the workflow and how the pieces talk to each other.

  • • When to use an AI assistant vs. pure automation
  • • Mapping tools to steps (CRM, ticketing, data, docs)
  • • Routing patterns: fan-out, fan-in, and retries
  • • Handling failures and partial success gracefully

Artifact: system interaction sketch

We’ll sketch how your AI assistants, automation layer, and core systems pass work between each other, including where data is read and written.

4. Blueprint docs & metrics your team can own

We end with a concrete blueprint that engineering, ops, and leadership can all read and act on.

  • • Writing the workflow blueprint in plain language
  • • Capturing assumptions, risks, and open questions
  • • Defining success metrics and guardrails
  • • Handoff checklist for build & rollout

Blueprint outline you can reuse

You’ll leave with a reusable outline for future workflows: sections for context, actors, steps, tools, metrics, and rollout plan.

Want a blueprint for your next AI workflow?

We usually run this as a 1-week engagement focused on one high-leverage workflow. By the end, you’ll have a clear map, human-in-the-loop design, tool plan, and metrics ready for implementation.

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Pairs well with AI Assistant Observability & SLOs and AI Guardrails & Safety Engineering for a full “design → ship → monitor” loop.