AI Readiness Assessment: 14 Questions & 5 Stages

Two colleagues reviewing a printed bar chart with pencils at a desk, discussing AI readiness assessment results

An AI readiness assessment tells you how prepared your company is to get real business value from AI, across strategy, leadership, people, data, and the tools you actually use. Faye’s version asks 14 questions, takes about five minutes, and gives you a 0 to 100 score in one of five stages: Starting, Adopting, Expanding, Optimizing, or Pioneering.

That matters because most mid-sized companies already use AI, and most of them have hit snags getting it to work. In RSM’s 2025 Middle Market AI Survey, 91% of respondents said their organization uses generative AI, and 92% of companies using it ran into challenges during rollout. Among the companies that hit problems, data quality was the most common issue, named by 41%.

Readiness is the gap between “a few people use ChatGPT” and “AI shows up in the P&L.” Below are all 14 questions, what each stage looks like in a real company, and the one next step that makes sense at each stage. Read it before you take the free AI Readiness Assessment, or after, with your score in hand.

Key Takeaways

AI readiness covers five areas, not one. Faye’s assessment looks at strategy and ownership, leadership and governance, people and culture, data and measurement, and tools and use cases.
It’s 14 questions and about five minutes. You get a 0 to 100 score and one of five stages: Starting, Adopting, Expanding, Optimizing, or Pioneering.
Your stage is a starting point, not a grade. The most useful part of your result is the area where you score lowest, because that’s the one holding the rest back.
Data is the usual blocker. Among middle market companies that ran into AI rollout problems, 41% named data quality as the top issue, per RSM’s 2025 survey.
Each stage has one sensible next step. Starting companies need an owner and a short list of use cases. Pioneering companies need governance that keeps up with automated, agentic workflows.

What does an AI readiness assessment actually measure?

It measures whether the conditions for AI to pay off are in place. Tools are part of that, but they’re not most of it. A company with great tools, no owner, no policy, and messy data will stall. A company with modest tools and clear ownership usually won’t.

Faye’s assessment groups its 14 questions into five areas:

Strategy and ownership: Is there a plan, and is someone accountable for it?
Leadership and governance: Are executives driving adoption, and are there real guardrails?
People and culture: Do your teams know how to use AI, and do they want to?
Data and measurement: Is your data ready for AI, and can you tell whether AI is working?
Tools, integration, and use cases: What you use, how connected it is, and where it delivers value.

Every question has five answer options, one for each stage. So the assessment doesn’t just score you; it shows you what the next column over looks like.

What are the 5 stages of AI readiness?

The five stages describe a path from “we haven’t really started” to “AI is part of what we sell.” Here’s what each one typically looks like in a mid-sized company.

Stage	What it looks like
Starting	No formal AI strategy and no one responsible for it. Leadership isn’t involved, there are no AI guardrails, and AI use is minimal or still exploratory.
Adopting	AI is being discussed, and IT handles it informally. A few early adopters use one or two tools, mostly on their own. Wins are anecdotal, and sensitive data handling is informal.
Expanding	A documented strategy and a designated AI lead or working group. AI policies are drafted, some teams get structured training, and several teams use multiple tools with some integrations.
Optimizing	An active roadmap with KPIs and owners, executive sponsorship, and formal governance. Most employees use AI daily, data policies are documented, and ROI is tracked in several areas.
Pioneering	A funded, board-level roadmap with KPIs tied to the P&L. Governance is audited continuously, AI training is part of onboarding, workflows run end to end, and AI is part of the customer value proposition.

Most companies don’t sit neatly in one row. It’s common to look like Optimizing on tools and Adopting on governance. That uneven profile is the useful part, because it tells you where to spend the next quarter.

What are the 14 AI readiness questions?

Here’s every question in the assessment, grouped by area, with what a strong answer looks like for each group.

Strategy and ownership (questions 1 and 2)

How would you describe your organization’s current AI strategy? Are you just beginning to explore possibilities, or do you have a clear roadmap for creating business value with AI?
Who is currently responsible for guiding AI decisions and helping your organization turn AI opportunities into action?

What good looks like: A documented roadmap with named owners and defined KPIs, sponsored at the executive level. If you’re building that roadmap from scratch, Faye’s guide to AI strategy consulting covers what the work involves.

Leadership and governance (questions 3 and 4)

How involved is your leadership team in your AI journey? Are executives actively supporting and guiding AI adoption?
What approach do you currently have for AI governance, including security, data usage, acceptable use, and selecting AI tools?

What good looks like: Senior leaders driving adoption company-wide, backed by AI policies that are enforced, not just drafted.

People and culture (questions 5 to 7)

How comfortable are your teams with understanding what AI can do, where it can help, and how to use it effectively?
What AI training or enablement have you provided to help employees build confidence and apply AI in their work?
How would you describe your organization’s culture around AI? Are people hesitant, curious, experimenting, or actively embracing AI?

What good looks like: Ongoing, role-based training and a culture where people experiment willingly instead of waiting for permission. If training is your gap, here’s what effective AI training for employees looks like.

Data and measurement (questions 8 and 9)

How prepared is your organization’s data foundation for AI? Consider data quality, accessibility, and how you manage sensitive information.
How are you measuring AI adoption and business impact today? Are you tracking usage, outcomes, or return on investment?

What good looks like: Well-organized, accessible data with documented rules on what can go into AI, and defined KPIs reported to leadership.

Tools, integration, and use cases (questions 10 to 14)

What AI tools or platforms is your organization currently using, and how broadly are they being adopted across teams?
How connected are your AI tools to the systems where your teams do their work, such as CRM, ERP, ticketing, or other business platforms?
Where is AI already helping your organization save time, improve processes, or create better outcomes? How are you measuring that impact?
How are you currently using AI to improve customer experiences, create revenue opportunities, or deliver value externally?
Are there repetitive, multi-step workflows across your systems where AI could help automate work or connect processes more effectively?

What good looks like: An integrated AI stack connected to your CRM, ERP, or ticketing system, with automated workflows and measurable ROI in several areas.

What should you do next at each stage?

The fastest way to move up is to fix your lowest-scoring area first, not to double down on your best one. Here’s the move that usually unlocks the next stage.

Starting: name an owner and pick two use cases

You don’t need a strategy deck yet. You need one accountable person, even part-time, and two or three specific use cases tied to real work, like summarizing meeting notes or drafting first-pass replies. That alone moves your strategy and ownership answers out of the first column.

Adopting: put basic guardrails in place

People are already using AI here, often on personal accounts with no rules about what data goes in. Write a short acceptable-use policy that covers approved tools and sensitive data, then move your early adopters onto a sanctioned tool. Start writing down wins, even informally, so you have something to measure against. Faye’s AI governance framework for CX and CRM is a practical place to start.

Expanding: connect AI to the systems where work happens

You have a strategy, a lead, and several teams using AI. The jump to Optimizing usually comes from integration: connecting AI to your CRM, ERP, or ticketing system so it works with live business data instead of copy-and-paste. Here’s how to connect AI, CRM, and CX tools without breaking your stack. If you want an outside view on which workflows to connect first, start with Faye’s AI Strategy Workshop.

Optimizing: tie AI to the P&L

Most employees use AI daily, and you track ROI in several areas. The next step is making that measurement financial, with KPIs that show up in the P&L, and adding customer-facing uses where AI creates revenue instead of only saving time. Governance has to grow with it, because more automation means more to audit.

Pioneering: keep governance ahead of your agents

You run end-to-end, agentic workflows, and AI is part of what customers buy from you. The risk now is speed outrunning oversight. Continuous auditing of AI data flows and a named owner for every automated workflow keep your lead from turning into exposure.

Why is data readiness the most common blocker?

Because every other area depends on it. A clear strategy, trained teams, and good tools all hit the same wall when the data AI needs is scattered, inconsistent, or off-limits.

The research backs this up. In RSM’s 2025 survey, data quality was the top problem among middle market companies that ran into AI implementation issues. And in Precisely’s data integrity research with Drexel LeBow, only 12% of organizations said their data was of sufficient quality and accessibility for AI.

Question 8 asks about exactly this: data quality, accessibility, and how you handle sensitive information. If it’s your lowest score, fix it before adding more tools. Clean, connected data makes every AI tool you already pay for more useful. For a closer look at the CRM side, see how data quality shapes an AI-powered CRM.

Take the AI Readiness Assessment

It’s free, it’s 14 questions, and it takes about five minutes. You’ll get a 0 to 100 score and your stage, so you know which of the next steps above applies to you. Faye’s team then follows up with a suggested first step matched to your stage.

Take the free 5-minute AI Readiness Assessment

Frequently Asked Questions

What is an AI readiness assessment?

An AI readiness assessment is a short, structured review of how prepared your company is to get business value from AI. Faye’s version asks 14 questions across strategy, leadership and governance, people and culture, data, and tools, then gives you a 0 to 100 score and one of five stages.

What score means we’re ready for AI?

There’s no single pass mark, because readiness depends on what you want AI to do. A higher score means more foundations are in place, but the more useful signal is your lowest-scoring area. A company can be ready for internal productivity tools long before it’s ready for customer-facing AI.

What is data readiness for AI?

Data readiness means the data AI needs is accurate, accessible, and governed. That includes clean records across systems, clear rules on what data can go into AI tools, and controls for sensitive information. In RSM’s 2025 survey, data quality was the top problem among companies that hit AI rollout issues.

What are the stages of AI maturity?

Faye’s assessment uses five stages: Starting, Adopting, Expanding, Optimizing, and Pioneering. They run from no formal strategy or owner, through drafted policies and early integrations, to a funded, board-level roadmap with AI tied to the P&L. Most companies land at different stages in different areas.

Is the AI readiness assessment free, and how long does it take?

Yes, it’s free, and it takes about five minutes. You enter your name and business email, then answer 14 multiple-choice questions. Each option describes a stage of maturity, so there’s nothing to research first. Answer for where your company is today, not where you plan to be.

What happens after I get my score?

You’ll know your score and your stage, so you can jump to the matching next step in this guide and start with your weakest area. Faye’s team also follows up with a suggested first step for your stage, and can help you turn it into a plan.