Decision Tool
AI Readiness Map
A neutral map for deciding whether you’re in the right phase for AI—before you spend money, change tools, or introduce new risk.
What this helps you decide
- Decide whether AI is the right next step, or whether you should stabilize the workflow first.
- Separate “we have chaos” from “we have repeatable work worth automating.”
- Avoid adding AI on top of missing process ownership and unclear handoffs.
When to use it
- You’re getting pitched AI tools and you’re not sure if you’re ready.
- You have repeated interruptions (calls, follow-ups, routing) and want relief, but not a rebuild.
- You want a shared language for “where we are” before choosing automation.
The framework
Stage 1: Manual & Fragile
- Signals you’re here: work lives in people’s heads; handoffs are verbal; outcomes depend on memory.
- What to fix before AI: define ownership, create a single intake path, and make “done” explicit.
- What AI helps with: small drafts/summaries after the workflow exists.
- What AI makes worse: ambiguity; unowned tasks; silent failure nobody notices.
Stage 2: Structured but Stretched
- Signals you’re here: you have tools and checklists, but follow-up and routing still slip.
- What to fix before AI: tighten the handoff points, standardize fields, and reduce tool fragmentation.
- What AI helps with: triage, routing, reminders, and consistent summaries.
- What AI makes worse: “optional” steps and inconsistent data entry.
Stage 3: Automatable with Oversight
- Signals you’re here: intake is consistent; exceptions are known; outcomes can be measured.
- What to fix before AI: add monitoring, audit trails, and clear override points.
- What AI helps with: conditional execution, structured extraction, and fast first response.
- What AI makes worse: decisions that require moral/accountability judgment.
Stage 4: AI‑Assisted at Scale
- Signals you’re here: the system runs daily; edge cases are managed; team adoption is real.
- What to fix before AI: governance, access patterns, and data hygiene.
- What AI helps with: internal assistants, reporting summaries, and exception surfacing.
- What AI makes worse: over‑automation that hides accountability behind “the model.”
Common mistakes
Common mistakes
- Treating AI readiness as “buy a tool,” instead of “make the workflow legible.”
- Automating before you define ownership (who is accountable when it fails).
- Measuring “speed” while ignoring quality, trust, and follow-through.
What this does NOT answer
- Which specific vendor or model to use.
- Exact implementation cost or timeline.
- Whether AI should replace a human decision (it shouldn’t).
Optional next step
If you want a tailored view of where you sit, the Readiness Assessment does this with your actual workflows—without sales pressure.