Across Australia, AI is starting to change how businesses run, make decisions, and interact with their advisors. For accountants, financial advisors, and business consultants, the impact may be even larger. AI is not just changing how advisory work is done; it is also influencing what clients expect from their advisors.
A new white paper, How AI Is Transforming Business Advisory: Opportunities, Risks and Practical Implementation for Australian Businesses, explores this change and its implications for Australian businesses and the professionals advising them.
Its main message is clear: the future of business advisory lies in blending AI-driven insights with human judgment.
Australia is at an AI tipping point.
AI adoption in Australia is speeding up, but it’s not evenly distributed.
According to the data in the white paper, about 12% of Australian businesses reported using AI in the workplace in 2024 and 2025. Adoption increases to 35% among large businesses, while only around 11% of small businesses reported using AI.
The gap is significant.
Larger organizations generally have better access to funding, technology expertise, and resources for managing change. Many smaller businesses, on the other hand, are still unsure about how AI fits into their operations.
The white paper highlights three major barriers for small and medium enterprises (SMEs): cost, cybersecurity concerns, and a lack of skills and knowledge. It also points out an awareness gap, with some businesses still unsure about what AI can really do for them.
This creates an important opportunity for business advisors.
Advisors can go beyond just helping clients handle technological changes. They can also help businesses spot practical AI opportunities, assess risks, choose the right technologies, and build the necessary skills to use them effectively.
The accountant’s role is changing.
Perhaps the biggest change is happening within accounting and business advisory itself.
Traditionally, much advisory work has focused on looking back: preparing financial statements, reconciling accounts, meeting compliance needs, and explaining past events.
AI is rapidly automating many of these tasks.
Invoice processing, bank reconciliation, data entry, basic reporting, and other repetitive processes can increasingly be managed by AI systems. This allows advisors to spend less time on processing information and more time analyzing it.
The white paper describes this shift as moving from being a “historian” to a “navigator.”
Rather than simply telling a business owner what happened last quarter, the advisor can help answer questions like:
- What is likely to happen next?
- When might cash flow become restricted?
- What if costs rise by 10%?
- Should we hire, invest, or expand?
- Which customers or products offer the best growth potential?
- What emerging risks might we not yet see?
That’s a fundamentally different value offer.
From financial reporting to forecasting.
One of the most powerful uses of AI in advisory is financial forecasting.
Traditional forecasting often relies heavily on historical averages and manually maintained spreadsheets. AI can analyze much larger datasets, including payment histories, seasonal trends, market conditions, and other outside factors to create more dynamic forecasts.
For an SME, this could mean spotting a potential cash-flow issue weeks or months before it becomes critical.
It could also allow for modeling multiple scenarios simultaneously.
For instance, an advisor might help a client understand what happens if:
- material costs increase,
- a major customer pays late,
- interest rates rise,
- a project is delayed,
- sales drop by 10%, or
- the business opens a new location.
AI makes it possible to simulate complex combinations of these variables much quicker than traditional methods.
The result is a move from reactive financial management to proactive decision-making.
The rise of Decision Intelligence.
The white paper takes it a step further by looking at the rise of Decision Intelligence.
Traditional business intelligence mainly answers the question: What happened?
Decision Intelligence aims to answer a more valuable question: What should we do next?
It combines AI, data science, and behavioral science to connect data with business decisions. Instead of just presenting another dashboard or report, AI can identify patterns, predict potential outcomes, and suggest possible actions.
Imagine a business owner asking: Why did our gross margin drop?
An AI-enabled system could analyze financial, operational, and sales data to find the factors behind the decline.
But this is where the human advisor remains crucial.
The advisor needs to understand the client’s strategy, relationships, risk tolerance, and circumstances. They then need to interpret what the insights mean for the business.
AI can highlight key signals. The advisor provides the context.
AI won’t replace good advisors, but it will change how good advising is done.
The white paper emphasizes this point.
AI excels at processing vast amounts of data, spotting patterns, running scenarios, and performing repetitive analytical tasks. However, humans are still better at grasping ambiguity, understanding context, building relationships, showing empathy, dealing with ethical issues, and handling unique situations.
This means the future is not just about choosing between humans and AI.
It is about creating a better combination of both.
The advisor of the future will need more than just technical accounting skills. They will require data literacy, technology awareness, strategic thinking, and strong communication abilities. The paper even mentions the rise of an “AI Advisor,” someone who can link AI technology with practical business strategy.
This change will also affect pricing.
If AI allows an advisor to complete a task in minutes that previously took several hours, charging solely based on time becomes harder to justify. The white paper suggests that advisory firms will increasingly shift toward fixed fees, outcome-based, and value-based pricing models.
But AI comes with serious risks.
The opportunity should not overshadow the risks.
AI-generated information can be incorrect. AI models can carry biases from their underlying data. Cybersecurity threats can increase as firms connect AI systems to sensitive information. Using private client data with unsuitable AI tools can lead to privacy, ethical, and regulatory issues.
For advisors, one of the key principles is human oversight.
AI-generated outputs should not automatically turn into professional advice.
Advisors need to question the assumptions behind AI outputs, verify important information, and recognize situations where the technology may not be reliable.
The paper argues that professional skepticism—already essential in accounting and advisory—needs to extend to AI as well.
Governance should come before scaling.
Successful AI adoption isn’t just about buying a subscription to an AI platform.
Businesses must establish policies on how AI can be used, what information can be entered into AI systems, how outputs are verified, and who is accountable for decisions.
The white paper recommends that AI governance cover areas like:
- acceptable and prohibited uses of AI,
- data governance,
- output verification,
- human oversight,
- incident reporting,
- employee training,
- privacy and security,
- and transparency with clients and stakeholders.
For boards, AI governance should integrate into broader organizational risk management rather than being seen solely as an IT issue. The paper also points to ISO/IEC 42001 as a framework Australian businesses can consider when developing robust AI governance practices.
So where should businesses begin?
One of the strengths of the white paper is that it does not claim businesses must transform everything overnight.
Instead, it suggests a phased approach.
The first 30 days: Understand and prepare.
Start with an AI readiness assessment.
Identify where AI could provide value, evaluate the quality of your existing data, find skill gaps, and select a few low-risk use cases.
Equally important, establish an AI Acceptable Use Policy and offer basic training to employees.
The first 90 days: Pilot and learn.
Move from experimentation to controlled pilot programs.
Choose low-risk, measurable use cases that can demonstrate visible benefits. Track time savings, errors, costs, employee experience, and output quality.
Training should also become more hands-on, helping employees learn how to use AI and critically evaluate its outputs.
Six to twelve months: Integrate and scale.
Once successful use cases have been established, the focus can shift toward deeply integrating AI into business processes and moving toward Decision Intelligence.
The goal now is not just to have employees “using AI.”
The aim is to build an organization where better data, improved technology, and better human decisions work together.
The competitive edge will go to businesses that take action.
The most important takeaway from the white paper is that AI adoption should not be seen as a tech project.
It is a business transformation project.
For advisory firms, this means rethinking the services they offer. For business owners, it means looking beyond AI as just a productivity tool and considering where it can enhance forecasting, decision-making, customer relationships, risk management, and growth.
And for both, it means understanding that successful AI adoption hinges on more than just the technology itself.
It requires good data.
It requires skilled people.
It requires governance.
And most importantly, it requires human judgment.
Australia is still relatively early in its AI adoption journey, especially among SMEs. This presents a significant opportunity for businesses and advisors willing to move from experimentation to purposeful implementation.
This article summarises the findings of the 2026 white paper How AI Is Transforming Business Advisory: Opportunities, Risks and Practical Implementation for Australian Businesses by Tim Hawthorne. The white paper draws on research from organisations including the Australian Bureau of Statistics, Reserve Bank of Australia, National AI Centre, Deloitte, KPMG and PwC, alongside Australian business case studies. You can read the full white paper here