AI is treated as a tech purchase, not a strategy. Someone signed up for a tool, a few people are experimenting, but there's no clear plan for how AI fits into the business.

Sounds like: "We got a ChatGPT license. That's our AI strategy."

There's a real commitment to AI and active projects underway, but it's hard to show ROI yet. Different parts of the business are pulling in different directions, and decisions about governance keep getting pushed off.

Sounds like: "We have several AI projects going. Some people love it, others can't get buy-in. Nobody agrees on what success looks like."

Leadership is jointly accountable for AI outcomes. Investment has shifted from buying tools to redesigning how work gets done. AI is treated as a core part of how the business operates, with clear ownership.

Sounds like: "Our leadership co-owns the AI roadmap. We've shifted most of our AI budget from tools to redesigning workflows."

AI knowledge lives with one or two people. Everyone else is either unaware or experimenting on their own with no guidance. When someone figures something out, that learning doesn't get shared.

Sounds like: "One person built something great with AI. They left, and now nobody knows how it works."

You've invested in training and people are starting to collaborate on AI. But the focus is still on hiring technical talent rather than building skills across the business. Projects stall when they move from experiment to daily use.

Sounds like: "We did AI training. A few people ran with it. Most went back to doing things the old way."

Roles have been redesigned — not eliminated — to work alongside AI. People manage AI-assisted workflows as part of daily operations. AI literacy is a core skill across the business, not just a tech thing.

Sounds like: "Everyone on our team works with AI tools daily. We have clear processes for what AI handles and when a human steps in."

Data lives in disconnected places — spreadsheets, different apps, individual drives. Getting clean data for an AI project takes a lot of manual work. There's no single source of truth.

Sounds like: "We spent weeks cleaning data before we could even try anything with AI. Then someone found another file that changed everything."

You've centralized your data, but different parts of the business still do things their own way. Getting access to the data you need takes time. Quality is hit or miss depending on where it comes from.

Sounds like: "We have our data in one place, but the sales numbers don't match what finance sees. Every AI project starts with a cleanup phase."

Data is a real-time, governed asset. Automated pipelines feed AI tools directly. Quality and access are monitored continuously — your data is ready for AI to use without manual prep.

Sounds like: "Our AI tools pull from clean, connected data automatically. Anyone who needs data can access it in hours, not weeks."

People are using disconnected AI tools — ChatGPT here, Copilot there — with no connection to the systems you actually run your business on. AI usage is scattered and uncoordinated.

Sounds like: "People are using AI tools they found on their own. Someone pasted client data into a public chatbot before anyone realized."

Your core systems are in the cloud with some integrations, but connecting AI to them is still hard. Getting something from proof-of-concept to daily use takes a long time. Every new use case feels like starting over.

Sounds like: "The AI prototype worked great. Then we tried to connect it to our actual business systems and it took months."

Your systems are connected and AI tools can pull from them in real time. New AI workflows can be set up in days, not months. Everything works together — including older systems.

Sounds like: "Our AI tools connect to our CRM, scheduling, and finance systems automatically. Adding a new workflow is a setup change, not a development project."

No formal AI policy exists. Security and ethics decisions are made on the fly. There's no record of what AI produced or how. If someone asked how your AI makes decisions, you couldn't answer clearly.

Sounds like: "Nobody's reviewed the legal side of our AI use. We're moving fast and hoping compliance catches up later."

You have written AI policies and basic guardrails, but oversight is manual and inconsistent. Risk gets assessed on a case-by-case basis rather than across the whole business. Compliance is reactive.

Sounds like: "We have an AI use policy. Some people follow it. We review AI risks when we hear about them — which isn't always."

Governance is built into how you use AI, not bolted on after. Automated monitoring catches issues like bias and drift. You can trace any AI-generated decision back to its source data.

Sounds like: "Every AI workflow has a compliance check built in. We can trace any AI output back to the data it used."