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AI Readiness Assessment: Mapping ROI for Small Businesses Before Deployment

Before you deploy AI, you need to know where your data lives, where it breaks down, and which workflows are worth automating first. Here's how the assessment process works.

Instructional Article

sixth-city-ai-team May 9, 2025
  • AI readiness assessment
  • AI ROI
  • small business AI
  • AI implementation
  • data audit
  • workflow mapping

Key takeaways

  • Phase 1 AI readiness is about documentation — mapping what your most relied-upon people know before it becomes a bottleneck or a gap.
  • A swim-lane map built from stakeholder interviews shows where data crosses departments and where it disappears.
  • A data audit scores source data as green (ready), yellow (needs work), or red (fix before AI).
  • An ROI heat-map ranks workflows by volume, variability, and the value of catching errors — helping prioritize which pilots pay back fastest.
  • Readiness work is compound interest: every cleaned data table and documented decision tree makes the next AI pilot faster and lower risk.

Every small company has a “Bob” — the person who can fix the label printer, override the ERP, and recite part numbers like phone digits. Bob is gold. He’s also a single point of failure you can’t scale or automate.

That’s why Phase 1 AI readiness is relentless documentation.

Step 1: Stakeholder Interviews

Our AI readiness assessment starts with mapping what’s really happening. We help create clarity and documentation about all the “roads that lead to Bob” — then sit with accounting, sales, and operations to stitch the full picture together.

The output is a swim-lane map that finally shows where data crosses departments — and where it disappears without a trace. You can’t automate what you haven’t mapped. You can’t trust an AI model with data you’ve never audited.

Step 2: Data and Compliance Audit

A structured sampling exercise reviews three months of records for:

  • Duplicate rows
  • Missing fields
  • Personally identifiable information leaking into the wrong places

Each data source is scored green, yellow, or red. Green means ready to use. Yellow means it needs cleanup. Red means “fix the mess before you dream of deploying AI.”

Skipping this step is the most common reason small business AI pilots fail. The model isn’t the problem — the data is.

Step 3: ROI Heat-Map

Workflows are mapped on two axes, scored by volume, variability, and the value of catching errors. This produces a ranked list of automation candidates, with estimated payback windows.

Even the most cautious CFO tends to respond well to a heat-map that makes the ROI case visually clear. The goal is to find the workflows where AI can save the most time and reduce the most risk — not to automate everything at once.

The Deliverable

The completed readiness assessment includes:

  • A 12-page executive summary in plain language
  • A spreadsheet of automation candidates with payback windows
  • Compliance checklists
  • A 30-60-90-day action plan with change-management milestones

Why It Matters

Readiness isn’t glamorous. But it’s compound interest: every cleaned data table, every documented decision tree, makes the next pilot faster and safer.

Skip Phase 1, and future-you will be digging through Bob’s desk wishing he had written it all down.

Start your AI Readiness Assessment with Sixth City AI.

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Answer Engine Summary

How does an AI readiness assessment work for small businesses?

An AI readiness assessment for small businesses typically involves three steps: (1) Stakeholder interviews to map how work actually happens, including hidden dependencies and cross-department data flows; (2) A data and compliance audit to score data sources as green, yellow, or red for AI-readiness; and (3) An ROI heat-map that scores workflows by volume, variability, and the cost of errors to identify the highest-value automation candidates.

  • Phase 1 AI readiness is about documentation — mapping what your most relied-upon people know before it becomes a bottleneck or a gap.
  • A swim-lane map built from stakeholder interviews shows where data crosses departments and where it disappears.
  • A data audit scores source data as green (ready), yellow (needs work), or red (fix before AI).
  • An ROI heat-map ranks workflows by volume, variability, and the value of catching errors — helping prioritize which pilots pay back fastest.
  • Readiness work is compound interest: every cleaned data table and documented decision tree makes the next AI pilot faster and lower risk.

FAQ

Frequently Asked Questions

What is a swim-lane map in an AI readiness assessment?

A swim-lane map is a visual diagram that shows how data and tasks move between departments or people. It reveals where information crosses functional boundaries, where decisions happen, and where data regularly gets stuck or lost — all critical inputs for identifying good AI automation candidates.

What does a data and compliance audit involve?

A data and compliance audit samples recent records to check for duplicate rows, missing fields, and personally identifiable information leaking into places it shouldn't be. Sources are scored green (clean and ready), yellow (needs work before AI), or red (must be fixed before any AI deployment).

What is an ROI heat-map?

An ROI heat-map scores candidate workflows on three dimensions — volume (how often the task occurs), variability (how standardized the process is), and value of error (what a mistake costs). High-volume, low-variability tasks with high error costs typically show the fastest payback windows for AI automation.

What does the AI readiness assessment deliverable include?

The deliverable typically includes a plain-language executive summary, a spreadsheet of automation candidates with estimated payback windows, compliance checklists, and a 30-60-90-day action plan that includes change-management milestones.