Introduction
Although AI use has become increasingly common in Australian businesses, using AI and adopting AI are not the same thing. According to ABC reporting on Treasury advice provided to the Australian Treasurer in August 2026, around two-thirds of Australian businesses reported using AI in some form, while fewer than 10 per cent described their adoption as significant. Treasury characterised uptake as widespread but shallow (see ABC News).
That distinction matters. A business might use an AI assistant to rewrite an email, summarise a document or generate ideas without changing anything about how the business actually operates. Those uses can still be valuable, but they are usually isolated improvements.
Deeper adoption happens when AI becomes part of a better way of working: supported by useful information, clear processes, appropriate controls and people who understand when and how it should be used. For a small business, this does not mean introducing AI everywhere. It means moving deliberately from occasional use toward the places where AI can improve the business system.
Move beyond individual use
The easiest stage of AI adoption is usually individual experimentation. Someone discovers that AI can draft a proposal, summarise meeting notes, analyse a spreadsheet or help prepare customer communication. Time may be saved immediately, without changing software or redesigning a process.
The limitation is that the benefit often stays with that individual. If one employee has developed an effective way to use AI but nobody else knows how it works, the business has gained a useful personal capability rather than a dependable business capability. A deeper approach asks:
Which repeated activities are benefiting from AI?
Could the useful method be documented or standardised?
Should other people use the same approach?
What information does the AI need to do the task well?
What checking should happen before its output is used?
The objective is make useful AI use repeatable, rather than to maximise AI use for the sake of itself.
Quick win
Choose one AI task that already saves time. Write down the input, instructions, expected output and human checking step. That simple record begins turning an individual shortcut into a reusable business practice.
Improve the work, not just the task
One reason shallow adoption produces limited value is that AI may be inserted into a process that has never been examined. For example, AI might make writing a customer response faster. But if the enquiry was entered twice, passed manually between three people and stored inconsistently before anybody started writing the response, improving the drafting stage addresses only one small part of the problem.
Treasury's reported assessment is important here. It said productivity gains depend on organisational investment that includes changes to processes, business models, management practices and workforce skills, rather than technology access alone (see ABC News). For a small business, the lesson is practical: before adding more AI, look at the work around it. Ask where information enters the process, where decisions happen, where work waits, where information is re-entered, and where mistakes or exceptions occur.
AI may then become one part of the improvement rather than the entire solution.
Quick win
Take one repeated workflow, such as enquiries, quoting, invoicing or monthly reporting, and sketch its main steps. Mark where AI is already used and where the actual delays or repeated work occur - they may not be in the same place.
Give AI better information
AI becomes considerably more useful when it can work from relevant, organised business information rather than relying only on whatever someone manually provides in a prompt. This doesn’t have to involve a sophisticated data platform. For a very small business, the foundation might simply be:
current files stored in predictable locations;
consistent names and formats;
important business information separated from obsolete copies;
appropriate permissions;
documented knowledge that would otherwise remain in one person's head;
clear sources for information that needs to be accurate.
This is one reason data management, cloud storage, digitalisation and knowledge control matter to AI adoption. Better foundations improve not only AI, but search, reporting, automation, handovers and everyday work.
The Australian Government's 2026 Intergenerational Report identifies AI as one of the major transitions shaping Australia's economy over coming decades (see Treasury). But at the individual business level, meaningful adoption still depends on much smaller decisions about information, processes and capability.
Quick win
Choose one regular AI-assisted task and identify where its supporting information comes from. If the answer is several folders, old emails, someone's memory and a spreadsheet of uncertain currency, improve that information source before adding more AI.
Build capability around the technology
Deeper adoption also changes what people need to know. This doesn’t necessarily mean formal AI training. In a small business, useful capability can be quite specific:
knowing which AI tools are approved;
understanding what information should not be entered;
knowing when an output needs verification;
recognising tasks where AI is useful and tasks where it is not;
using agreed prompts, templates or procedures where consistency matters;
knowing who remains responsible for the final result.
The goal is confidence with boundaries. A business doesn’t need every employee to become an AI specialist, it needs people to understand the role AI plays in the work they are responsible for.
Put simple controls around deeper use
The more AI becomes involved in real business activity, the more important governance becomes. Occasional brainstorming presents different risks from AI working with customer information, preparing financial analysis, drafting material sent externally or taking actions through connected systems.
Deeper adoption therefore requires proportionate controls, and for a small business, these might include approved tools, access permissions, privacy rules, review requirements and clear responsibility for important outputs. If AI can take actions rather than simply generate suggestions, authority boundaries become even more important.
This need not become complicated governance infrastructure. The controls should grow with the capability and risk. Treasurer Jim Chalmers subsequently described Australia's challenge as translating high AI uptake into better ways of working, while continuing to manage risks (see Treasury Ministers).
Think in stages, not an AI transformation
Small businesses don’t need to jump from occasional chatbot use to extensive automation or AI agents. A more useful progression looks something like this:
Use → Repeat → Integrate → Improve
Use: Find a task where AI provides genuine assistance.
Repeat: Make the useful method reliable enough to use again.
Integrate: Connect it appropriately with the information, applications and processes around it.
Improve: Measure whether the resulting way of working is actually better.
Some AI uses may never need to move beyond the first stage, while others may eventually support an entire workflow. Depth is measured less by how advanced the technology sounds than how effectively it supports the business.
Conclusion
The growing gap between widespread AI use and significant AI adoption suggests that the next opportunity for many businesses isn’t just to ‘find another AI tool’, it’s to look more closely at what happens around the tools they already use. Processes may need simplifying. Information may need organising. Useful practices may need documenting. People may need clearer guidance. Permissions and review points may need strengthening. For a small business, deeper AI adoption doesn’t have to mean making the business more complicated. Done well, it should do the opposite: help technology, information and people work together more effectively in support of what the business is trying to achieve.
Takeaways
Using AI occasionally is different from embedding it into the way the business works.
Valuable individual practices become more useful when they are repeatable and shareable.
Improving the surrounding process may deliver more value than adding another AI tool.
Organised information improves AI as well as reporting, automation and everyday work.
Skills, ownership and simple controls become increasingly important as AI takes on more meaningful work.
Adoption depth should be driven by business value, rather than by how much AI the business can introduce.
Further Reading
Australian Treasury, 2026 Intergenerational Report, September 2026. Treasury
ABC News, Australia risks missing out on AI economic boom due to 'slow' business uptake, Treasury says, 31 August 2026. ABC News
Bibliography
Australian Government Treasury, 2026 Intergenerational Report, 2026.
Armstrong, C., Australia risks missing out on AI economic boom due to 'slow' business uptake, Treasury says, ABC News, 31 August 2026.
Chalmers, J., Address to the Australia and the World 2026 Annual Lecture, Australian National University, September 2026. Treasury Ministers
