This circular model shapes how many small businesses experience compliance: reactive, time-bound, and disconnected from the day-to-day activities that generate the underlying data. Artificial intelligence is starting to change this by moving compliance from a recurring task to a continuous process built into the workflow itself.
Paul Robson, MYOB Chief Executive Officer.
“One of the big things we’re working on is making it an always-on feature,” Robson said. “Software asks you for information because it needs to pull information out of your head.”
In practice, this means that tasks such as BAS preparation can be moved from periodic execution to tasks that are built in the background as transactions occur. The system can prompt for input at the point of activity rather than reconstructing the information after the fact.
MYOB’s AI BAS support agent is an example of how this approach is being applied, capturing transactional data and user input to prepare positions for review by the end of the reporting period. Similar principles apply to other administrative workflows within MYOB, such as reconciliation and invoice management, where automation reduces the need for manual intervention.
“We’re not only eliminating the burden of frustrating administrators, we’re redefining the way small and medium-sized businesses operate,” says Robson. “By implementing AI in compliance management and day-to-day operations, business owners can quickly see the impact and increase their impact and trust.”
The appeal is simple and clear. Unlike broader AI applications, which may require process or behavior changes, use cases that focus on reducing administrative burden address existing pain points.
According to MYOB’s Bi-Annual Business Monitor, 46% of SMEs are not currently using AI tools and have no plans to use them in the next 12 months, suggesting there is significant room for growth. When actual implementation occurs, it often focuses on areas where the value is clear and workflows are clearly defined.
It also points to the practical issue of integration. Many small businesses operate a combination of different software systems. Deploying standalone AI tools can add complexity if they are not aligned with existing processes. In contrast, embedding AI within the core system allows it to operate throughout the lifecycle of a task.
“To create real value, AI must be deeply embedded in products and the way businesses operate, shaping decisions, anticipating needs, and doing the heavy lifting in the background, allowing business owners to focus on higher-value work,” Robson says.
This challenges providers to build end-to-end workflows. Rather than individual features, the focus is on extending workflows from start to finish, leveraging the same underlying data to support multiple stages of activity.
For small businesses, the practical impact has less to do with the underlying technology and more with how it is delivered, especially in areas such as cash flow and day-to-day financial management. Tools that work within existing systems and address real-world tasks are more likely to be adopted than tools that require companies to change the way they work.
Robson says the impact extends beyond compliance managers themselves.
“Cashflow is the number one reason small businesses fail in the Australian/New Zealand market,” he says.
Reducing the time spent on administrative tasks and improving the flow of financial information through the business are some of the things that make these tools more immediately useful to small businesses.
For policymakers and industry groups, the change is not about the technology itself, but what it unlocks.
Lee Hickin, executive director of the National AI Center, said the first benefits tend to be practical.
“For many small businesses, the initial value of AI is clear,” Hickin says. “By reducing the amount of time spent on routine administrative and compliance tasks, individuals can free up time to do the work that strengthens their business and drives growth and productivity.”
This dynamic helps explain why administrative workflows are emerging as a practical starting point for adoption. Tasks such as record-keeping, reporting, and BAS preparation are rule-based, repeatable, and central to day-to-day operations, making them relatively low-risk and quick-benefit conditions for applying AI.
Lee Hickin, executive director of the National AI Center, said:
“AI works best when it fits naturally into the workflows people already use,” Hickin says. “If it helps simplify tasks such as record keeping, reporting, and BAS preparation, it becomes more practical and more likely to stick.”
However, the challenges are not only technical. Widespread adoption across the small business sector continues to be shaped by skills gaps, cost pressures, and confidence.
Data from the National AI Center’s AI Adoption Tracker shows that while AI adoption is on the rise, many companies remain cautious, especially when use cases are unclear or integration with existing systems is uncertain. Awareness and capacity are often low, especially in small and medium-sized enterprises and local businesses.
That’s where practical guidance and built-in tools can play a role. Companies that start with familiar workflows, see measurable time savings, and build confidence through use are more likely to expand AI to other parts of their operations.
At a broader level, the impact extends beyond individual companies. MYOB analyzed anonymized and aggregated data from hundreds of thousands of small businesses and found that small businesses using AI products and capabilities grew 2.8 times faster than those without.
For small businesses, the direct impact is easy. Less time spent on repetitive tasks means more time spent with customers, staff, and decision-making. Over time, this shift could become one of the more concrete ways in which artificial intelligence contributes to productivity across the economy.
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