AI Tools for Accountants: What to Assess

What can AI tools do in an accounting practice?

AI tools can help accountants analyse data, automate repeatable processes and identify business insights, but their usefulness depends on the task, source information and controls around the output. A practice should begin with work that has known inputs, a clear expected result and a person who can review exceptions before the result is used.

CPA Australia’s AI Tools for Accountants course identifies machine learning and predictive analytics, natural language processing, robotic process automation, governance and oversight, and tool integration as relevant areas for accounting professionals. It describes AI tools as a way to analyse data, automate processes and gain business insights using vendor-supplied and open-source technologies.

That does not make every accounting activity suitable for automation. A stable process such as collecting standard information, preparing an internal summary or routing an approval can be assessed differently from work requiring interpretation of incomplete records, unusual client circumstances or professional judgement.

CPA Australia describes one intended outcome as: “Enhance efficiency through automation while navigating challenges and ethical considerations.” That balance matters because efficiency is only useful when the practice can understand the task, verify the information and decide whether the output is fit for its intended purpose.

How should a practice assess an AI workflow?

A practice should assess an AI workflow by defining the required result, identifying the information involved, assigning a reviewer and testing exceptions before routine use. This approach distinguishes a controlled assisted task from an uncontrolled process that creates additional correction work or produces outputs the team cannot confidently explain.

Map the current task before comparing products. Identify what starts the work, where the source information sits, which records are required, what the completed output looks like and where a team member changes the result using judgement. The map should include the hand-offs that occur between bookkeeping, accounting, client service and partner review.

1. Choose one repeatable task: describe its trigger, inputs, expected output and accountable owner.
2. Check the source information: determine whether records are complete, current and consistently labelled.
3. Set the review point: decide who checks the output before it is relied upon or sent onward.
4. Test exceptions: include incomplete records, conflicting information and unusual client circumstances.
5. Record the decision: document the workflow, reviewer, exceptions and changes made after testing.

Data quality remains central to this assessment. In a CPA Australia tax-season series for 2020–21, ATO Assistant Commissioner Tim Loh estimated that incorrectly claimed work-related expenses contributed almost $4.4 billion to the individuals not-in-business tax gap. The figure concerns work-related expense claims, rather than AI performance, but it illustrates why practices should not treat an automated output as evidence by itself.

The same 2020–21 series included an ATO example of a person working full time for 40 hours a week across 48 weeks. Tim Loh said an 80 cents per hour shortcut-method deduction in that scenario was about $1,500, while noting that the shortcut covered only part of the 2020 tax time year. The example reinforces the need to identify which method, records and assumptions sit behind an apparently simple result.

What governance questions should accountants ask?

Governance questions for AI tools should cover access to client information, source-data quality, output review, documented approvals and the practice’s ability to explain how a result was produced. A practice should also establish who may change instructions, approve a workflow or pause it when a result is incomplete, unexpected or unsuitable.

Ethical judgement is still needed when a tool produces a draft, classifies information or identifies a pattern. In an ethical intelligence discussion published by CPA Australia, Brendan O’Connell described APES 110 as “the main ethical standard that CPAs have to follow.” The discussion supports a practical approach: the availability of a tool does not remove the need for professional awareness and review.

Practices can turn governance into operational checks by considering:

• which client information may be supplied to a tool;
• which systems provide the input and whether those records are current;
• who approves a workflow and changes to its instructions;
• where a person must review output before relying on it; and
• how the practice retains evidence of inputs, outputs, exceptions and approvals.

These questions should be answered before broad adoption rather than after a workflow has become embedded. They also help a practice separate an AI tool’s product features from its own responsibilities for client communications, record handling, supervision and professional judgement.

How can a practice compare AI tools before adoption?

A practice can compare AI tools by testing the same bounded workflow, source records and review criteria across each option. The comparison should focus on what the tool reads, what it produces, what it can change, how exceptions appear and whether the practice can retain a usable record of the review and approval process.

Ask vendors to demonstrate the workflow with realistic but safe test material. Check whether the product clearly identifies inputs, whether the output can be reviewed before it is used, and whether staff can see who made a decision or approved a step. Integration should be assessed in the context of the systems the practice already uses, not as a feature list viewed in isolation.

It is also useful to define a stop condition before testing begins. For example, a practice may decide that incomplete inputs, uncertain classifications or material exceptions must be referred to a team member rather than carried through automatically. That makes testing a controlled evaluation rather than an informal experiment.

How does DeskMate support repeatable accounting work?

DeskMate supports repeatable accounting work by letting a practice record a fixed workflow once and run it the same way each time, while showing what the workflow reads, writes and sends for approval. Practices can review the skill library when assessing which structured tasks may suit a recorded workflow.

DeskMate requires approval before any ledger write, and its workflow run logs record what was read, what was produced, who approved it and when. Those logs are kept for eighteen months and can be exported, while the Security & Trust page sets out the product’s Sydney data residency, tenant isolation, no-training approach for customer content and audit-trail controls.

This makes DeskMate relevant to the repetitive operational part of practice work: recording a known process, running it consistently and reducing the need to repeat the same administrative steps by hand. It does not replace professional judgement, tax advice or a practice’s own compliance responsibilities.

The strongest first implementation is usually one workflow with a clear owner and measurable friction, such as collecting recurring information, preparing a standard internal summary or routing a known approval. Once the team can review how that process behaves in practice, it has a stronger basis for assessing further AI-assisted work.