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Before And After Workflow Map

A sample map that shows how AI should change a workflow without hiding owner review or exception handling.

Direct Answer

A before/after workflow map should show what changes, what stays human-owned, what gets faster, and where the workflow stops when evidence is missing.

How to use this

• Step 1: Draw the current workflow in five to seven steps.
• Step 2: Mark where work waits, gets copied, loses context, or needs a judgment call.
• Step 3: Insert AI only where it prepares evidence or reduces handoff friction.
• Step 4: Keep owner review and exception handling visible on the map.
• Step 5: Define the after-state in operational terms: faster route, cleaner packet, fewer corrections, or better review.

Workflow map example

• Before: Inquiry arrives, someone reads it later, context is copied manually, ownership is unclear, and follow-up quality varies.
• AI prepares: Summary, urgency, service fit, missing fields, duplicate check, and recommended owner.
• Human reviews: High-value accounts, unclear fit, pricing questions, custom requests, and customer-visible replies.
• After: Inquiry is routed with context, review flags, owner task, and response-time measurement.
• Stop point: Workflow pauses if identity, consent, fit, or ownership is unclear.
• Measured result: Faster qualified response and fewer wrong-route corrections.

Worksheet prompts

• Before-state delay: Where does the workflow wait for someone to read, copy, interpret, or chase context?
• Evidence handoff: Where is the source information today, and what needs to be carried forward?
• AI preparation step: What can AI prepare that makes the owner faster or more accurate?
• Review step: Where does a person approve, correct, or stop the output?
• After-state output: What task, note, draft, route, packet, or report exists that did not exist before?
• Measured change: What observable result should improve after the workflow runs?

What the map proves

The map proves the workflow is not just a prompt. It has a trigger, source data, review point, exception path, and output.

What most maps hide

Most maps show happy-path automation. ADA maps the handoff, review, stop rule, and measurement because that is where production workflows break.

Where to use it

Use this before building lead response, onboarding, reporting, support, or proposal workflows so everyone understands what changes and what remains accountable.

Quality bar

• The map shows the messy middle, not only the clean final state.
• The AI step is narrow enough to test.
• The owner review point is not hidden in the diagram.
• The exception path is visible.
• The after-state can be measured against the before-state.

Where This Helps

• Process redesign
• Automation scoping
• Team handoff clarification
• Workflow owner training

Research basis

• NIST AI RMF Playbook: Supports mapping use context and managing risk through the lifecycle.
• OECD AI Principles: Supports safeguards, oversight, and accountability in context.
• NIST Generative AI Profile: Adds generative-AI-specific risk context for evaluating workflow changes and safeguards.

Related Resources

• Lead Capture Workflows
• AI Workflow Automation
• Client Onboarding

FAQ

• What is a workflow map?: It is a simple view of how work moves before and after AI assistance.
• Why include stop points?: Stop points prevent AI from guessing when evidence is missing or risk is too high.
• Does the map replace SOPs?: No. It helps define the operating path before detailed SOPs or tool configuration.