Artificial intelligence is already changing accounting, but the next development goes beyond tools that simply answer questions or draft emails. AI agents can plan and complete multi-step tasks across accounting systems, documents and workflows—subject to the permissions and controls set by the firm.
For UK accounting practices and finance teams, this could represent a significant productivity opportunity. Agents may help process transactions, prepare reconciliations, monitor deadlines and assemble working papers. Yet their ability to take action also introduces greater risk. Successful adoption will depend not only on the technology, but on governance, professional judgement and robust human oversight.
What is an AI agent?
A conventional AI assistant normally responds to an individual request. An AI agent can pursue a defined objective through a sequence of actions.
For example, instead of merely explaining why a supplier balance does not reconcile, an agent might:
Retrieve the relevant ledger entries and invoices.
Compare them with supplier statements.
identify likely duplicates or missing transactions.
Prepare proposed corrections.
Route exceptions to the appropriate reviewer.
Record what it did in an audit trail.
This distinction matters. The more autonomy a system receives, the more carefully its access, limits and approval points must be designed.
Where AI agents could be used in UK accounting
Bookkeeping and transaction processing
Agents can collect invoices and receipts, extract information, suggest account codes and match transactions with bank feeds. They can also identify missing documents, contact the relevant colleague or client and track the request until it is resolved.
This could reduce routine administration, particularly for practices managing large numbers of small-business clients. However, unusual transactions, estimates and judgement-sensitive classifications should continue to receive human review.
Accounts payable and receivable
An accounts-payable agent could match purchase orders, invoices and goods-received records before preparing a payment run. An accounts-receivable agent might monitor overdue balances, draft appropriately worded reminders and escalate high-risk cases.
Giving an agent authority to communicate or prepare payments requires strong safeguards. Changes to supplier bank details, high-value payments and unusual transactions should trigger independent verification and approval.
Month-end and year-end close
AI agents could coordinate recurring close activities, including requesting information, preparing reconciliations, checking control accounts and highlighting unexplained movements. They may also assemble draft schedules and maintain a record of outstanding items.
The practical benefit is not simply a faster close. By monitoring information continuously, agents could help move accounting teams away from last-minute correction and towards earlier exception management.
Tax compliance
Tax agents could organise client records, check whether expected information has been received and prepare draft calculations or returns for review. They could also monitor filing obligations and identify transactions that may require specialist consideration.
This is particularly relevant as Making Tax Digital expands. Sole traders and landlords with qualifying income above £50,000 entered Making Tax Digital for Income Tax from 6 April 2026. The threshold falls to £30,000 from April 2027 and £20,000 from April 2028. Affected taxpayers must maintain digital records and submit quarterly updates through compatible software. HMRC guidance
AI may help practices manage the resulting increase in recurring data and client contact. It does not, however, remove the accountant’s responsibility to verify that the correct legislation, thresholds and client circumstances have been applied. Updated professional guidance emphasises that AI can support tax work but cannot replace professional judgement, competence or ethical responsibility. ICAEW guidance
Audit and assurance
Potential audit applications include journal analysis, document comparison, anomaly detection and the preparation of draft working papers. Agents could examine an entire population, flag higher-risk items and assemble supporting evidence for the audit team.
The Financial Reporting Council has recognised the potential of AI to improve audit quality and efficiency. Its guidance stresses the need to understand and mitigate the risks associated with generative and agentic systems and to consider their use within a firm’s wider quality-management obligations. FRC guidance on AI in audit
An auditor cannot simply accept an agent’s output because it appears comprehensive. The engagement team must still evaluate the reliability of the information, investigate exceptions and obtain sufficient appropriate audit evidence.
Management reporting and advisory services
AI agents could monitor performance against budgets, detect changes in cash flow and prepare tailored commentary for different stakeholders. Within a practice, an agent might identify clients facing deteriorating margins, rising debtor days or approaching funding requirements.
This may allow accountants to spend more time discussing decisions with clients and less time assembling reports. The value remains in the accountant’s interpretation: understanding the commercial context, challenging assumptions and communicating an appropriate course of action.
Why the UK market is well suited to adoption
UK accounting is becoming increasingly digital. Alongside Making Tax Digital, Companies House has announced that, from April 2028, all UK companies will have to file annual accounts using commercial software in iXBRL format. Companies House announcement
These reforms will increase the volume of structured financial information moving between clients, accountants, software providers and government systems. That creates favourable conditions for carefully governed automation.
The commercial pressure is also clear. Many firms face capacity constraints, rising client expectations and difficulty recruiting experienced staff. AI agents could absorb parts of the repetitive workload while enabling professionals to concentrate on review, advice and client relationships.
The principal risks
Incorrect or fabricated outputs
An AI system can produce a confident but inaccurate answer. Tax treatments, accounting standards and filing requirements may be misinterpreted or based on obsolete information.
Controls should therefore be linked to risk. Low-risk administrative work may need only sample-based monitoring, while tax advice, journal postings, statutory filings and audit conclusions require qualified human approval.
Confidentiality and data protection
Accounting data may contain payroll details, bank information, tax records and other personal or commercially sensitive information. Firms must understand what information an AI provider receives, where it is processed, how long it is retained and whether it may be used to train models.
Where personal data is involved, UK GDPR and the Data Protection Act remain applicable. The Information Commissioner’s Office advises organisations to address fairness, lawfulness, transparency and risks to individual rights when deploying AI. ICO guidance
Excessive access
An agent with broad access to email, banking and accounting platforms could cause substantial harm if it misunderstood an instruction or if its credentials were compromised.
Access should follow the principle of least privilege. Agents should receive only the systems and permissions required for a specific role, with separate approval for payments, filings, master-data changes and external communications.
Weak accountability
Responsibility cannot be delegated to software. Each agent should have a named business owner, documented purpose and defined escalation route. Firms should be able to reconstruct what information the agent used, what actions it took and who approved the result.
Clients should also receive an honest explanation of material AI use where it affects the service, decision-making process or handling of their information.
Automation bias
Staff may stop challenging an output when an agent is usually correct. Training should therefore focus not only on operating the technology but also on recognising its limitations. Reviewers need sufficient knowledge and time to question results meaningfully.
A practical adoption framework
Firms should begin with a narrow, measurable process rather than attempting to automate an entire service line.
A sensible implementation programme would:
Select a repetitive, rules-based workflow with clear source data.
Map the current process, controls and exception routes.
Classify the information involved and complete the necessary privacy and security assessments.
Restrict the agent’s permissions and require approval before consequential actions.
Test normal cases, unusual cases and deliberate failure scenarios.
Compare performance against human work using accuracy, time saved, exception rates and rework.
Maintain logs, version records and evidence of review.
Reassess the agent whenever its model, instructions, integrations or intended use changes.
Supplier due diligence is equally important. A firm should understand the provider’s security arrangements, data-processing terms, service resilience, subcontractors, model-update practices and options for retrieving or deleting information.
The future role of the accountant
AI agents are unlikely to remove the need for accountants. They are more likely to change where professional value is created.
Routine collection, comparison and follow-up can increasingly be delegated to software. Accountability, scepticism, ethical judgement and commercial interpretation cannot. The accountant of the agentic era will need to supervise digital work, assess the strength of evidence and explain conclusions clearly to clients and regulators.
For UK firms, the strongest strategy is neither uncritical adoption nor avoidance. It is controlled experimentation: choosing useful applications, placing clear boundaries around them and retaining human responsibility wherever the consequences matter.
AI agents may perform more of the accounting process, but trust will continue to depend on the professionals who govern their work.