The Future of Accounting: How Agentic AI Will Reshape the Finance Function

For three decades, technology in accounting has largely done the same thing faster. Ledgers moved from paper to desktop software, then to the cloud. Bank feeds replaced keying. Optical character recognition replaced some of the typing that survived. The shape of the work, though, stayed recognisable: transactions were captured, coded, reconciled and reported after the period had ended.

Agentic AI breaks that pattern. An agent does not wait to be told which button to press. Given an objective, a set of permissions and access to business systems, it plans the steps, carries them out across those systems, and escalates what it cannot resolve. For finance teams, this is a change in kind rather than degree — and it lands directly on processes that carry real financial, fraud and compliance risk.

This article sets out where the profession appears to be heading, what accountants and CFOs should be doing now, and — because this is our field — what has to be true of the underlying Microsoft, identity, data and security environment before any of it is safe.

From accounting automation to autonomous finance

Conventional accounting automation is deterministic. A rule fires when a condition is met: this supplier maps to that expense account, invoices under $500 skip second approval, these bank lines match those ledger entries. Rules are reliable and easy to audit, and they break the moment reality differs from the rule.

Agentic AI is goal-directed. Asked to close accounts payable for the week, an agent retrieves invoices from multiple channels, reads them, validates supplier details against master data, matches to purchase orders and goods receipts, applies coding, identifies duplicates, and presents a short list of items that genuinely need a human. It handles the variation that rules never anticipated, and it does so across systems rather than inside one.

The destination the technology points towards is often called autonomous finance: a finance function operating continuously, where routine transactions are processed and reconciled as they occur, controls test themselves, and people concentrate on exceptions, judgement and strategy. Very few organisations are there. Almost all can move a stage closer, and the stages are described in our Agentic Finance Readiness Framework below.

The continuous financial close

Month-end exists because reconciliation is expensive. When matching, accruals and intercompany postings require human attention, it is rational to batch them. Remove most of that cost and the logic changes: the close becomes something the ledger is always approaching rather than an event the team runs at.

In practice, continuous close means reconciliations run daily or hourly, accrual estimates are refreshed as data arrives, variance commentary is drafted the moment a threshold is breached, and the number of open items is a live figure rather than a month-end surprise. The benefit is not simply a faster close — it is that management decisions stop relying on data that is three weeks old.

It also raises the bar on data quality. A continuous process amplifies whatever is wrong in your chart of accounts, supplier master data or system integrations. Firms that have not cleaned that up will find agents faithfully reproducing existing mess at speed.

Accounts payable becomes exception driven

Accounts payable is where agentic AI is landing first, because the process is high-volume, rule-adjacent and well documented. Agents can already ingest invoices from email and portals, extract line detail, check supplier details against master records, perform two- and three-way matching, apply GST treatment, detect duplicates, and prepare payment runs.

What should not be delegated is equally clear. Payment release, changes to supplier bank details, new supplier onboarding and anything above a defined materiality threshold belong with people, because those are precisely the steps business email compromise and invoice fraud target. An agent that can both create a supplier and pay it has removed segregation of duties, no matter how good the model is.

The realistic near-term operating model is exception-driven AP: agents process the straightforward majority, and the AP officer's day becomes reviewing the small set of items the agent flagged, with the reasoning attached.

Proactive accounts receivable

Receivables is where AI shifts from processing to prediction. Payment behaviour is patterned: which customers pay late, which disputes recur, which invoices stall because a purchase order reference is missing. Agents can act on those patterns before the debt ages.

• Predicting which invoices are likely to be paid late, and prompting earlier
• Personalising collection sequences by customer behaviour rather than a fixed day count
• Detecting and routing disputes on the first signal rather than the third reminder
• Applying cash receipts and part-payments continuously
• Forecasting collections to support working-capital decisions

The commercial effect is measured in days sales outstanding and in the quality of the cash-flow forecast — two numbers most Australian SMEs manage on instinct and a spreadsheet.

AI agents operating across business systems

The reason agentic AI matters more than a smarter chatbot is orchestration. Finance work almost never lives in one system. A single AP query can touch email, the document store, the ERP, the banking platform and a contract. Agents are designed to move across those boundaries.

In a Microsoft environment, that typically means Copilot and agents working over Microsoft 365 data, Dataverse and Power Platform connectors, Dynamics 365 Business Central or a third-party ERP, and line-of-business data in Azure. Our agentic AI services and managed AI automation work is mostly about making those connections deliberate rather than accidental — and making Microsoft 365 and Copilot readiness a prerequisite rather than an afterthought.

Every connection is also an access decision. An agent inherits the reach of the identity it runs as, which is why secure identity and access management is the first technical control in any agentic finance design, not the last.

The changing role of the CFO

CFOs have spent a decade being told to become strategic while still owning a function that spends most of its hours processing. Agentic AI genuinely shifts that balance — and adds a new responsibility at the same time.

• From reporting the past to running scenarios continuously against current data
• From approving transactions to approving the rules and thresholds under which agents transact
• From owning a finance system to owning a finance operating model that spans several systems
• From delegating technology risk to IT to sharing accountability for AI risk with the board

The last point is the one that surprises people. If an agent misposts, misapproves or leaks, the CFO owns the financial and disclosure consequences. That makes AI governance a finance leadership issue, supported by an IT strategy and technology roadmap rather than solved by one.

Accountants as the control and governance layer

The most useful way to think about the profession's direction is a move up the stack. Where accountants once performed the control (checking the invoice, matching the receipt, reconciling the account), they increasingly design, monitor and attest to controls that operate automatically.

That work is skilled and it is not going away. Somebody has to determine what an agent may approve, what evidence it must retain, when it must stop, how conflicts are resolved, and whether the resulting numbers are true and fair. Those are accounting judgements expressed in system configuration.

AI and the future of audit

Audit has always been constrained by sampling. Testing a hundred transactions out of two million is an economic compromise, not a preference. Agents remove much of that constraint: full-population testing becomes feasible, and testing can run continuously rather than annually.

Two consequences follow. First, anomalies are surfaced close to the event, which is when they can still be corrected cheaply. Second, the audit's centre of gravity moves further towards evaluating control design and operation — including controls over the AI systems themselves. An auditor will reasonably ask how an agent's permissions were set, who approved them, what it did, and how you know.

Firms already familiar with control frameworks have an advantage here. If you are working towards ISO 27001 and independent certification, much of the evidentiary discipline is the same discipline.

AI governance and financial controls

Most AI failures in finance will not be model failures. They will be permission failures: an agent that could see a payroll folder it should never have reached, or write to a ledger it should only have read. This is where AI governance and cybersecurity stops being an IT topic and becomes a financial control.

A workable governance baseline for finance agents has five parts:

• A distinct identity per agent, with least-privilege access reviewed on a schedule
• An explicit statement of what each agent may read, write, approve and execute
• Approval thresholds tied to materiality, with human sign-off above the line
• Segregation of duties enforced between agents as strictly as between people
• A named human owner accountable for each agent's outputs

Underneath that sits conventional security hygiene — device compliance through Microsoft Intune, sensitivity labelling and data protection across SharePoint and Microsoft 365 content, and baseline mitigations aligned to the Essential Eight. Agents make weak permissions consequential; they do not create the weakness.

AI audit trails

An agentic finance environment needs to answer a simple question at any moment: what did the agent do, why, on whose authority, and what did a human decide? That requires logging of prompts and actions, retention of the data an agent relied on, immutable records of approvals, and monitoring that flags unusual behaviour — an agent transacting outside business hours, or touching a system it has never used.

Monitoring is the part organisations underestimate. Detecting a misbehaving agent is a security operations problem, which is why 24/7 security monitoring belongs in the design from the outset, alongside Azure logging and retention that will survive an auditor's questions and a business continuity event.

The changing accounting technology stack

The classic stack was an accounting system plus bolt-ons. The emerging stack has more layers, and finance leaders will be asked to make decisions about all of them.

• Systems of record — ERP or accounting platform, payroll, banking
• Data layer — a defined source of truth, clean master data, API access
• Identity layer — user and agent identities, conditional access, privileged access
• AI layer — Copilot, agents, models and the connectors between them
• Control layer — permissions, thresholds, logging, monitoring, evidence
• Human layer — review, judgement, approval and accountability

Most Australian finance teams already run the first layer well and the middle three informally. That gap is the real project, and it is where managed IT services and Microsoft 365 management do the unglamorous work that makes AI adoption safe.

Organisational readiness

Readiness is rarely about the AI licence. It is about whether financial data is trustworthy, whether systems can talk to each other, whether permissions reflect current roles, and whether anyone has decided what AI is allowed to do. Those questions are answerable in weeks, and answering them honestly usually reorders the project plan.

The framework below exists to make that assessment concrete. For a hands-on self-assessment, use our interactive Agentic AI Readiness Checklist for Accounting Firms, which scores your environment across data, identity, security, governance, financial controls and people. Governance readiness, in particular, is often best approached through a formal governance and compliance readiness review rather than a policy document written after deployment.

The impact on accounting jobs

The honest position is that the volume of routine processing work will fall and the demand for judgement, control and advisory work will rise. Graduate pathways that were built on years of transaction processing will need redesigning, because the apprenticeship those years provided has to come from somewhere else.

That is a workforce planning problem, not an extinction event. Roles most exposed are those defined purely by data entry and matching. Roles that grow are those combining accounting capability with data, technology risk and governance literacy — which is precisely the direction professional development is already moving.

What does this mean for Australian accountants?

Adopting AI does not reduce the need for professional development. It broadens it. Increasingly, Australian accountants need capability spanning accounting and financial reporting, AI literacy, data analysis, cybersecurity, privacy, ethics, AI governance, automation, technology risk, systems integration and professional judgement.

Those obligations are formalised. CPA Australia currently requires applicable members to undertake 120 hours of Continuing Professional Development over each three-year triennium, with at least 20 hours during each year. Within that, at least 10 hours of Ethics and Professional Responsibility must be completed over the triennium, and at least 2 hours each year. See the CPA Australia Continuing Professional Development requirements and the ethics requirements for CPD. Requirements can change, and individual circumstances differ — verify your own obligations directly with CPA Australia.

AI is already becoming part of accounting professional development

The shift is visible in professional education itself. CPA Australia publishes courses addressing AI directly, which is a reasonable signal of where the profession expects capability to sit.

• AI Applications in Accounting — covering areas such as financial reporting automation, tax compliance, fraud detection, continuous auditing and the management of AI in accounting.
• AI Tools for Accountants — covering AI fundamentals, automation, machine learning, natural language processing, AI governance and adoption.
• AI Ethics and Governance for Accountants — connecting directly to the privacy, accountability, bias, transparency, governance and risk management themes discussed above.
• AI for Financial Reporting and Compliance — supporting evidence that reporting automation, compliance and AI risk are now legitimate professional development areas.

CPD hours shown against those courses are current information published by CPA Australia and should be verified before enrolling. Mycelium 365 is not affiliated with, endorsed by or accredited by CPA Australia, does not deliver CPD, and does not provide accounting or taxation advice. Reading this article does not itself constitute CPD.

Comparable material is published internationally by AICPA & CIMA, and Australian guidance on privacy obligations when handling personal information in AI systems is available from the Office of the Australian Information Commissioner.

What businesses should do now

• Define the source of truth for financial data and fix master data before automating anything on top of it.
• Review identity: enforce MFA, control privileged access, and govern service accounts.
• Decide, in writing, what AI may read, write, approve and execute in your environment.
• Pilot in one bounded process — usually accounts payable or reconciliation — with human approval retained.
• Log everything an agent does and monitor for behaviour outside the expected pattern.
• Include AI literacy, privacy and governance in your team's professional development planning.

Accounting as an intelligent control system

The through-line of every change described here is the same: accounting is becoming an intelligent control system rather than a processing function. Transaction processing gives way to exception management. Historical reporting gives way to continuous insight. Manual controls give way to continuous controls. Software assistance gives way to agent orchestration. And the accountant becomes a financial governance and decision professional.

None of that removes the requirement for competence, scepticism and accountability. It raises it. The organisations that do well will be the ones that built the identity, data and governance foundations before they needed them.