An AI maturity model is a structured scale that shows how far your organization has progressed in using AI, usually across several dimensions such as strategy, data, governance, people and technology. You answer a set of questions, each answer places you at a level, and the result becomes a score or a chart you can act on. The useful part is not the label. It is the list of gaps the result exposes and the order in which you close them.
This guide explains how the common models work, what the levels and scores mean, which Australian benchmarks exist, and how a small or mid-sized business can get a usable result without a six-month consulting project.
CalibreforumSee Where Your AI Adoption StandsCalibre Forum’s AI readiness assessment combines employee insights into a scored report and a clear 90-day action plan.Explore AI readinessTL;DR
- An AI maturity model scores your organization across dimensions (strategy, data, governance, people, technology) and places you on a scale, usually five levels.
- Most models rely on leadership answers. That tells you what management believes, not what staff actually do with AI tools day to day.
- A higher level is not always the goal. Your target depends on your mission, resources and risk, so aim for the level that fits your business.
- Australian benchmarks exist, but most are built for large organizations. Use them for context, not as a verdict on your business.
- Turn any result into a short plan: pick three to five gaps, assign owners, and review in 90 days.
Table of Contents
- What an AI maturity model actually measures
- How the common models structure levels
- You do not need level 5 everywhere
- What an AI maturity score means, and how to read it
- Why leadership-only surveys miss the picture
- How to run an AI maturity assessment, step by step
- Turning a result into a roadmap
- Maturity looks different by industry
- Choosing between a framework, a consultant and a survey-based report
- Get a scored view of your own business
- Common mistakes to avoid
- FAQ
- Sources
What an AI maturity model actually measures
The Local Government Information Technology South Australia (LGITSA) adoption manual describes the purpose well. Its AI maturity model helps councils assess where they stand on data infrastructure, talent, culture and governance, compare progress with similar organizations, find gaps, decide on investments and pilots, and track improvement over time. Swap “council” for “business” and the logic holds.
In practice a model gives you three things:
- A common vocabulary. “We use AI a bit” becomes a named level that your leadership team can discuss.
- A set of dimensions. Instead of one vague rating, you see where you are strong (say, tool adoption) and weak (say, governance or training).
- A direction. Each level implies what the next one looks like, which is the raw material for a roadmap.
You will see the same idea sold under several names: maturity model, maturity assessment, maturity index, maturity score and benchmark. The differences are mostly about format. A model is the framework. An assessment is the act of applying it. An index or score is the number it produces. A benchmark compares that number with other organizations.
How the common models structure levels
Most frameworks use five levels. The wording changes, but the progression is consistent:
| Level | Typical label | What it looks like in a business |
|---|---|---|
| 1 | Awareness / Initial | People use AI tools individually. No shared rules, no overview. |
| 2 | Active / Developing | Some teams run pilots. Basic guidance exists but is patchy. |
| 3 | Operational / Defined | AI is part of regular workflows. Owners, policies and training are in place. |
| 4 | Systemic / Managed | AI use is measured, governed and connected to business goals. |
| 5 | Transformational / Optimizing | AI shapes strategy and is continuously improved and reviewed. |
The labels in the table are a generic composite, not any single vendor’s wording. Check the exact definitions of whichever model you adopt before you quote a level to your board.
Dimensions: where the real detail lives
The level is the headline. The dimensions are where you learn something. MITRE’s model, for example, uses 20 dimensions grouped into six pillars: ethical and responsible use, strategy and resources, organization, technology enablers, data, and performance and application. Each question has one answer, and that answer sets the maturity level for that dimension. When all questions are done, the tool produces a score and a graphical view.
Australia’s FrontierSI takes a slimmer approach in its readiness framework: four domains (strategy, organisation, data and technology), with a spider diagram that shows your profile against a reference. Its argument is that readiness goes beyond technology maturity, and that AI governance should build on the data governance you already have.
For a small or mid-sized business, four to six dimensions are plenty. More than that and you spend the budget on the survey instead of the fixes.
You do not need level 5 everywhere
This is the most common misreading. MITRE is explicit that the target level for each pillar depends on your mission, resources and business practices, and that not every organization needs or wants level 5 across the board.
A 25-person accounting firm does not need the same governance machinery as a bank. It does need to know which client data is going into which AI tool, who approved that, and what happens when a staff member pastes a file into a public chatbot. A sensible target for most small and mid-sized businesses is a solid level 3 on governance and data handling, and whichever level the business case justifies on adoption.
What an AI maturity score means, and how to read it
A score is a compression of many answers into one number. It is useful for tracking change and for starting a conversation. It is not a grade you should chase.
Australian reference points
Three public datasets give Australian context. Treat them as background, because each measures something slightly different.
- Responsible AI Index 2022 (Fifth Quadrant). It scores organizations out of 100 and uses four bands: Planning (0 to 49), Initiating (50 to 64), Developing (65 to 79) and Maturing (80 and above). The mean for Australian organizations was 62, unchanged from 2021. Organizations where the CEO leads AI strategy averaged 66, against 61 where the CEO does not. Of those surveyed, 14% sat in Planning, 46% in Initiating, 37% in Developing and 3% in Maturing.
- BSI International AI Maturity Model (2024). Based on polling of 932 business leaders across nine markets, it scores categories such as investment, adoption, training, safety guardrails and trust on a one to five scale. Australia’s overall score was 3.1.
- ELMO and YouGov HR benchmark (2026). In a survey of 904 Australian HR professionals in January 2026, HR leaders scored 68 out of 100 on AI maturity, and only 26% felt fully prepared to meet leadership’s AI expectations.
The takeaway: a mid-60s score on a 100-point scale is typical in these surveys, and the gap between leaders and the rest often comes down to who owns the topic. The CEO finding is the most actionable one. If nobody senior owns AI, your score tends to stall.
Weighting matters more than the number
Two indexes can give the same business very different scores because they weight dimensions differently. The DOBA AI Maturity Index, a 2026 study of Slovenia, Croatia and Serbia, weights strategic integration at 30%, governance at 25%, employee competencies at 20%, operational use at 15% and business impact at 10%. Its authors note that a higher score signals readiness for responsible, strategic AI, not simply more tools in use. That distinction is worth keeping in mind. Ten AI subscriptions and no policy is not maturity.
Why leadership-only surveys miss the picture
Most maturity assessments are answered by a handful of executives or the IT lead. They are good at strategy and budget questions. They are poor at the question that decides your real risk: what are people actually doing with AI right now?
Staff often use tools that were never approved, through personal accounts, to get work done faster. Management then scores itself on policies that exist on paper. The result is a level that looks healthy and a workplace that is not. We cover this gap in more detail in our piece on finding shadow AI in your business.
There are two practical fixes:
- Ask everyone, anonymously. People answer honestly when nobody can trace the response to them.
- Separate usage questions from policy questions. Compare what staff report with what management believes. The difference is your most valuable finding.
Our view on maturity models
We built Calibre Forum around one belief: a maturity level is only as good as the evidence behind it. Most models ask leaders to describe their organization. We think the more reliable source is the people doing the work.
So our assessment surveys every staff member anonymously, then combines the answers into one scored report. It maps which tools are in use, how work flows through them, where capability is strong and where the risks sit. It does not install software and does not collect individual data, which is exactly why people answer candidly.
We also think a score without a next step is a missed opportunity. Our report ends with a tailored 90-day action plan, because 90 days is long enough to fix real things and short enough to stay on the calendar. For a business of your size, that is a more useful output than a level label.
We make no compliance or legal claims. What we give you is visibility and a sequence of practical moves.
Anthony
How to run an AI maturity assessment, step by step
Whichever model you pick, the workflow is similar.
- Define the scope. Whole company or a division? A smaller scope finishes faster, but a partial picture can hide shadow use elsewhere.
- Choose four to six dimensions. Strategy and ownership, data handling, governance and risk, tools and workflows, skills and training, and security awareness cover most needs.
- Collect evidence from staff, not just leaders. Use short, anonymous questions about which tools people use, for what tasks and with what data.
- Score by dimension. Avoid averaging everything into one number too early. A 4 on adoption and a 1 on governance is not a 2.5. It is a specific risk.
- Set targets per dimension. Use your risk, clients and resources to decide what “good enough” is, as MITRE suggests.
- Pick the first moves. Choose three to five gaps with the highest impact and lowest effort.
- Reassess on a schedule. Quarterly works well while tools and habits are changing fast.
Turning a result into a roadmap
A result becomes useful when it becomes a list of owned tasks. Enterprise roadmaps can run long: HP Australia’s implementation guide sketches six phases, from a strategic alignment phase of two to three months through to ongoing governance and optimization, including infrastructure, data pipelines and model development. That scale suits organizations building their own AI systems. Most small and mid-sized businesses are adopting off-the-shelf tools, so the roadmap is shorter and more about people and rules.
A 90-day plan for that situation often looks like this:
- Days 1 to 30: see and decide. List the tools in use, name an owner, write a one-page usage guideline and decide which data must never go into public tools.
- Days 31 to 60: fix the biggest gaps. Move people from unapproved tools to approved ones, set up access controls and run a short training session.
- Days 61 to 90: embed and measure. Pick two or three workflows where AI saves real time, track the result and reassess.
Our longer guide, an AI adoption roadmap for Australian businesses, walks through this in more detail.
Governance and risk are part of maturity
Governance is usually the lowest-scoring dimension for smaller businesses, and it is the one that most affects trust. Australia’s government publishes the Voluntary AI Safety Standard with 10 guardrails to guide organizations on safe and responsible AI use. It is voluntary, but it gives you a sensible checklist to compare against your own practices. For a deeper look at how executive ownership and policy fit together, this AI model governance guide is a good companion. For managing the risk side, see our article on AI model risk management for Australian businesses.
Maturity looks different by industry
The same score can mean different things depending on what you do. A few examples of what changes:
- Accounting firms carry client confidentiality and need tight rules on what leaves the firm. See our assessment for accounting firms.
- Managed service providers often use AI tooling for clients and themselves, so the risk crosses both. See the assessment for MSPs.
- Manufacturers tend to focus on operations, quality and supply chain data. See the assessment for manufacturers.
- Training providers and RTOs deal with student records and assessment integrity. See the assessment for RTOs.
- Not-for-profits work with sensitive community data and tight budgets. See the assessment for not-for-profits.
If your sector is different, the method is still the same: ask staff, score by dimension, set targets that fit your risk.
Choosing between a framework, a consultant and a survey-based report
There are three broad routes.
| Route | Best for | Trade-off |
|---|---|---|
| Public framework you apply yourself | Teams with time and an internal owner | You design the questions and analysis |
| Consulting engagement | Large or complex organizations | Longer, higher cost, often leadership-focused |
| Survey-based assessment with a scored report | Small and mid-sized businesses wanting fast visibility | Focused on usage and next steps rather than deep technical build |
Pick based on how much internal time you have and what decision you need to make. If the decision is “what is happening in our business and what do we do in the next quarter”, a survey-based report fits.
Get a scored view of your own business
Calibre Forum’s assessment is built for Australian and New Zealand businesses. Your staff complete a short anonymous survey. We combine the responses into a scored report that maps your current capabilities, highlights risks and sets out a tailored 90-day action plan. Nothing is installed and no individual data is collected.
The report is a one-off A$1,990. If you want quarterly reassessments to track progress, the annual membership is A$2,388 per year. You can see exactly what the report contains on the what you get page, and how the setup works on the how it works page.
Common mistakes to avoid
- Chasing a level for its own sake. Choose targets that match your risk and goals.
- Scoring only what leadership knows. If staff are not asked, shadow use stays invisible.
- Treating one number as the answer. Read the dimension scores.
- Running it once. AI habits shift quickly, so repeat on a rhythm.
- Skipping ownership. Benchmarks suggest that executive ownership coincides with higher scores, so name a senior owner early.
FAQ
What is the difference between an AI maturity model and an AI readiness assessment?
A maturity model is the scale of levels and dimensions. A readiness assessment is the process of applying a scale to your organization to see how prepared you are to adopt or expand AI. In practice the terms overlap, and both end with a score and a set of gaps.
How many levels does an AI maturity model have?
Most have five, running from an early awareness stage to an optimized, strategic stage. Some frameworks use four bands instead, such as the Responsible AI Index with Planning, Initiating, Developing and Maturing. Check how each level is defined before comparing results across models.
Is a higher AI maturity score always better?
No. A higher score signals more capability and control, but the right target depends on your mission, resources and risk. Many organizations do not need the top level in every dimension. Focus on closing the gaps that matter to your clients and your data.
How often should we reassess?
Quarterly is a practical rhythm while tools and staff habits are changing quickly. It is frequent enough to see whether your 90-day actions worked, and spaced enough to avoid survey fatigue.
Can a small business use an AI maturity model?
Yes. Choose a model with a handful of dimensions and gather input from all staff, not only managers. A small team can move from a first result to a working plan in weeks.
Sources
- Local Government AI Adoption Manual (LGITSA, 2025)
- The MITRE AI Maturity Model and Organizational Assessment Tool Guide
- Governance in the Age of AI (FrontierSI, 2025)
- Responsible AI Index 2022 Executive Summary (Fifth Quadrant)
- Trust in AI: Actions and attitudes around AI adoption (BSI, 2024)
- HR Industry Benchmark Report 2026 (ELMO/YouGov)
- DOBA AI Maturity Index 2026
- Your Essential AI Implementation Roadmap for Australian Businesses (HP Australia)
- Voluntary AI Safety Standard (Department of Industry, Science and Resources)
Recommended
- AI Readiness Assessment for Managed Service Providers
- AI Readiness Assessment for Manufacturers
- AI Security Assessment for Business Leaders: Find Shadow AI
- AI Risk Mitigation: A Practical Guide for Australian Businesses
General information only. Articles on this blog are written with the help of AI writing tools and published by Calibre Forum. They are not legal, insurance, tax or financial advice, so check anything important with a qualified adviser.