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Most mid-sized businesses across Australia and New Zealand are already using AI in some form. But usage, on its own, is not a reliable indicator of progress.
A chatbot in one team, some task automation in another, or a handful of point solutions across finance or operations can deliver useful gains. They can reduce manual effort, improve turnaround times, and create early momentum. But they don’t necessarily reflect how prepared the business is to scale those gains or embed them into day-to-day operations.
That gap between activity and readiness is where many businesses lose clarity. It’s also why a maturity lens matters.
Why “using AI” isn’t the full picture
It’s easy to assume that once AI is present in the business, progress is underway.
In practice, outcomes vary significantly depending on what sits behind that usage. In some organisations, AI is layered onto existing processes, helping individuals work more efficiently but leaving underlying workflows unchanged. In others, it is embedded into core systems, where it can influence how decisions are made and how work moves across the business.
The difference is not always visible at the surface level, but it becomes clear in performance. Businesses that treat AI as an isolated tool tend to see incremental improvements. Those that build it into connected systems and processes are more likely to see consistent, repeatable value.
Understanding that distinction requires looking beyond adoption and into how the business is structured to support it.
Introducing the MYOB Business Autonomy Maturity Model
In The Autonomous Business Report 2026, we assess progress through the MYOB Business Autonomy Maturity Model (BAMM). This model helps leaders identify whether they should focus next on systems, data, governance, capability, or all four.
The model looks at two dimensions: readiness and ambition.
Readiness reflects the operational foundations already in place, including systems, data quality, governance and workforce capability. Ambition reflects the intent to embed AI and autonomous practices more broadly across the business.
This framing is deliberate. Many businesses are ambitious about what AI can deliver, but are still building the underlying conditions required to scale it. Others have invested in core systems and data environments, but are moving more cautiously as they work through risk, governance or change management.
Looking at both dimensions together provides a more accurate picture of where a business sits today.
The four stages of the autonomy journey
When mapped against readiness and ambition, four distinct cohorts emerge across the ANZ mid-market.
Accelerating businesses combine high readiness with high ambition. Representing 43% of those surveyed, they are moving beyond experimentation and treating AI as part of core business capability. In these organisations, AI is increasingly embedded into workflows, and the focus shifts from testing use cases to scaling impact.
Operationalising businesses show strong readiness but more measured ambition, making up 39% of the market. They have often invested in data, systems and integration, but are progressing carefully as they work through governance, risk and strategic alignment. The foundations are in place, but the pace of change is more controlled.
Exploring businesses are a much smaller segment at 2%. These organisations have high ambition but lower readiness. In many cases, they are looking to move quickly, but are constrained by fragmented systems, limited data maturity or gaps in capability.
Reacting businesses, representing 16% of respondents, sit lower on both dimensions. AI activity may exist, but it is typically isolated, with less structure, weaker governance and limited momentum toward broader adoption.
These cohorts are not fixed categories. They are a snapshot of how businesses are balancing intent with operational reality.
What the model reveals about business performance
One of the clearest insights from the model is how strongly outcomes are linked to maturity.
Businesses further along the autonomy journey are significantly more likely to report meaningful productivity gains from AI. Among the most advanced organisations, 92% report a positive impact, compared with 37% of those at earlier stages.
Within the Accelerating cohort, 62% report a significant productivity uplift, rising to 70% among those operating at the highest levels of autonomy.
The pattern is consistent. The more embedded AI becomes within systems and workflows, the more reliable and repeatable the outcomes.
This is what shifts AI from isolated efficiency gains to broader operational impact.
Where businesses often misread their progress
For many leaders, the challenge is not a lack of activity, but a misreading of what that activity represents.
It’s common to see businesses using modern tools while still relying on people to connect systems, reconcile data and manage exceptions manually. Information may still sit across multiple platforms. Reporting may still require intervention. AI may still be assisting at the edges rather than shaping core processes.
In those environments, it’s easy to feel further along than the underlying structure supports.
That’s where a model like BAMM is useful. It separates visible activity from operational readiness, and highlights the conditions required for progress to continue.
A more useful way to assess where you stand
The value of a maturity model is in helping leaders take a more objective view of their current position.
Across Australia and New Zealand, most mid-sized businesses are not starting from zero. There is strong ambition, growing confidence in AI, and clear intent to modernise the systems that underpin operations.
The questions business leaders should be asking are: where are foundations strongest today, and where do they need to be strengthened to support the next stage of growth?
That might mean improving data consistency, consolidating systems, clarifying governance, or building internal capability. In many cases, it is a combination of all four.
Businesses don’t become autonomous through isolated AI wins. They get there by building the operational foundations that let AI scale.
Frequently asked questions
What is the business autonomy journey?
The business autonomy journey describes how a business moves from isolated AI use and manual processes toward more connected, intelligent and scalable operations. For mid-sized businesses in Australia and New Zealand, it is a way to assess how ready the organisation is to embed AI into day-to-day workflows, not just experiment with individual tools.
Why is using AI not the same as being ready for autonomy?
Using AI does not automatically mean a business is ready for autonomy. Many organisations have chatbots, task automation or point solutions in place, but still rely on disconnected systems, inconsistent data and manual intervention behind the scenes. Readiness depends on the operational foundations that allow AI to scale reliably across the business.
What is the MYOB Business Autonomy Maturity Model?
The MYOB Business Autonomy Maturity Model, or BAMM, is a framework used to assess where a business sits on the autonomy journey. It looks at two dimensions: readiness and ambition. Together, these provide a clearer picture of how prepared a business is to turn AI activity into broader operational impact.
What does readiness mean in the BAMM model?
Readiness refers to the business foundations already in place to support autonomy. That includes system connectivity, data quality, governance, internal capability and the ability to embed AI into workflows in a controlled and repeatable way.
What does ambition mean in the BAMM model?
Ambition reflects how strongly a business intends to expand the use of AI and autonomous practices across the organisation. A business may be highly ambitious, but if systems, data and governance are not mature enough, that ambition can be difficult to scale.
What are the four stages of the autonomy journey?
The four stages in the autonomy journey are Accelerating, Operationalising, Exploring and Reacting. These cohorts reflect different combinations of readiness and ambition, and help businesses understand whether they are scaling AI confidently, building foundations carefully, pushing ahead without enough structure, or still operating with fragmented progress.
Information provided in this article is of a general nature and does not consider your personal situation. It does not constitute legal, financial, or other professional advice and should not be relied upon as a statement of law, policy or advice. You should consider whether this information is appropriate to your needs and, if necessary, seek independent advice. This information is only accurate at the time of publication. Although every effort has been made to verify the accuracy of the information contained on this webpage, MYOB disclaims, to the extent permitted by law, all liability for the information contained on this webpage or any loss or damage suffered by any person directly or indirectly through relying on this information.
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