AI in Accounting: Practical Steps for Safer, Smarter Workflows
AI is already changing how accountants work. It can summarise ATO updates or other documents, draft client emails, prepare first pass research or reports, assist with meeting notes and reduce time spent on repetitive tasks.
The opportunity is clear. But the real question is not whether AI can save time. It is how firms can use it safely, consistently and in a way that supports professional judgement.
This becomes even more important as AI tools become more capable. Some systems can now plan tasks, ask follow up questions, decide next steps and, in some cases, trigger actions. This is often described as agentic AI.
For accountants, the questions in using AI are around accountability, risk & control, safety and ensuring output is appropriate for the client.
1. Let task risk guide how you use AI
The best way to introduce AI is to start small.
Begin with tasks where the downside risk is low and the benefit is easy to measure. Examples include summarising public ATO updates, drafting internal notes, preparing first draft client emails, summarising meetings or brainstorming advisory topics.
These tasks help your team build confidence without exposing the firm to unnecessary risk.
Higher risk tasks need stronger controls. This includes tax positions, client advice, lodgements, payment approvals, reconciliations, journal entries and updates to client records. In these areas, AI can assist, but it should not act on its own.
Task risk should influence how the firm uses AI. It should determine which tool is appropriate, what information can be entered, who needs to review the output, whether peer review is required and what evidence should be retained on file.
A simple rule is useful. The more material the outcome, the stronger the review process should be.
2. Match the tool to the task
Not every AI tool should be used for every task.
Generic AI tools can be useful for brainstorming, drafting and summarising non confidential information. They are a good way for teams to learn how prompts work and how output can differ depending on level of interaction with the AI tool.
Technical work is different.
For tax, the issue is not whether the answer sounds polished. The issue is whether it is grounded in credible sources and can be verified. That means legislation, ATO rulings and ATO administrative guidance, not general web content or unsupported summaries.
For simple drafting, a generic tool may be enough. For technical tax research, consider a tool built for that workflow, with citations, source links, saved history and human review built in.
This is where Praxio AI - Tax Assistant fits. It is designed for Australian tax professionals who need faster research and drafting, while keeping the practitioner in control.
3. Keep the human in the loop and accountable
Human oversight is not just reading the final answer before it goes to a client.
A good human in the loop process means the practitioner actively shapes the output. This includes checking facts, adding missing context, asking follow up questions, challenging assumptions and testing whether the answer fits the client’s circumstances.
Treat AI like a junior researcher. It can help you move faster, but it still needs supervision. The practitioner remains accountable to the client for the final advice, regardless of which tools were used to support the work. It is the practitioner’s licences and insurance on the line.
A practical workflow could look like this.
Start with the scenario
Add relevant information such as dates, entity types, and research required, then run the task in AI
Review first output, and add additional information, correct assumptions, question validity of AI output
Review resources used by the AI by reviewing the source
Re-run the task in AI
Go back to step 3 and reiterate the output until satisfied
For complex matters, peer review is also important. A senior accountant, manager or partner should review higher risk research before advice is issued. Praxio AI – Tax Assistant supports this with follow up questions to the user and chat sharing functionality allowing users to share chats with colleagues or managers so they can review and contribute to the research.
4. Verify before relying on the answer
One of the biggest risks with AI is false confidence. A well written answer can still be wrong. A citation can look convincing but fail to support the point. A small change in facts can change the outcome.
Use a simple verification process.
First, check the source. For tax, review the answer against legislation, ATO rulings and ATO administrative guidance.
Second, test the facts. Confirm dates, thresholds, transaction steps and assumptions.
Third, look for false confidence. Check whether the cited source actually supports the answer, whether it is current and whether there are exceptions.
Fourth, use a source, logic and outcome review. Ask: What source supports this? Does the reasoning follow? What is the impact if this is wrong?
Finally, document the review. Keep the AI output, prompts, iterations, sources checked and final human decision in the file.
The goal is to move from “that sounds right” to “I can prove why this is right”.
5. Create simple guardrails
AI governance does not need to start with a long policy.
For most small and medium firms, a practical starting point is enough.
Create an AI register that records what tools are used and for what purpose. Define approved use cases, such as internal research, first draft emails and meeting summaries. Define prohibited use cases, such as entering sensitive client information into public tools or sending AI output to clients without review.
Set data handling rules. Staff should sanitise prompts and avoid entering names, TFNs, bank details, addresses and other identifiers unless the tool has been approved for that use.
Set review rules based on risk. Low risk internal tasks may only need light review. Client advice, tax positions and complex matters should require source checking and senior review.
Then monitor and improve. Review usage, check quality, collect feedback and update your rules as the firm learns what works.
Useful Australian resources include Australia’s AI Ethics Principles, the Voluntary AI Safety Standard 10 guardrails, and the National AI Centre’s Guidance for AI Adoption. These provide a principle based approach and a great starting point to safe AI adoption, with practical guidance on accountability, risk management, transparency, privacy, security and human oversight.
The bottom line
AI adoption does not need to be overwhelming.
Start with low risk tasks. Use the right tool for the task. Keep a human in the loop. Verify sources. Document the review. Build simple guardrails. Then improve the workflow as your team gains confidence.
For accountants, AI should not be a shortcut around professional judgement. It should be a better way to perform research, prepare drafts and support quality advice.
Praxio AI - Tax Assistant was built with that philosophy. It helps Australian tax professionals move faster while keeping credible sources, review and practitioner accountability at the centre.
The best next step is simple. Pick one workflow, try AI in a controlled way and measure whether it improves the way your team works.
About the Author
William Young FCPA GAICD is a contributor of Praxio AI, a purpose-built AI platform for Australian tax and accounting professionals. He is a recognised speaker on AI in accounting, known for delivering practical, ethical, and forward-looking advice to the profession.