AI Readiness Matrix

Sample audits, scorecards, rubrics, briefs, workflow maps, bottleneck examples, and automate-versus-manual examples for practical AI deployment.

How ADA proves the operating method

These readiness tools show how AI Deployment Authority evaluates a workflow before recommending AI, automation, or a simpler process fix. They are sample operating artifacts, not client case studies.

Readiness Tool Library

• Sample AI Workflow Audit: Use this when a team has a workflow idea but has not proven that the workflow has enough evidence, ownership, review capacity, and measurement to justify an AI build.
• AI Readiness Scorecard: Use this before a strategy session, leadership meeting, or department kickoff to compare candidate workflows on evidence, value, reviewability, and risk.
• First Workflow Selection Rubric: Use this when leadership has too many AI ideas and needs to choose a first workflow that can create business value without creating unnecessary risk.
• Example Deployment Brief: Use this when a workflow has been selected and the team needs the operating detail required before build work begins.
• Before And After Workflow Map: Use this when a team needs to see what will actually change after AI is added to a workflow.
• Anonymous Bottleneck Analysis: Use this when a business complaint sounds like a people problem, but the real issue may be a broken handoff, missing evidence, unclear ownership, or a workflow that is too manual.
• What To Automate Vs What To Keep Manual: Use this before automating a workflow so the team agrees which actions AI can prepare and which decisions must stay with a person.

Why these are stronger than content examples

• Grounded in responsible AI standards: The tools draw from serious ideas: context, oversight, impact, measurement, and ongoing controls.
• Built as operating tools: Each tool includes prompts, quality standards, and use instructions so a buyer can run it against their own workflow.
• No inflated case-study claims: These are examples and decision tools. They demonstrate method without pretending every artifact is a client result.

Deployment Standard

• Workflow before tool: Pick the repeated business process before choosing software.
• Evidence before automation: Name the source material AI needs before it prepares work.
• Owner before output: Assign the person who reviews and improves the workflow.
• Metric before expansion: Measure one result before scaling the workflow.