AI and Accounting: How AI Is Revolutionising the Industry

Advances in artificial intelligence (AI) are bringing significant changes to the accounting industry. AI has the potential to streamline and automate many existing processes, as well as unlock new types of financial analysis and approaches that human accountants can’t do on their own.

The changes on the horizon are likely to impact not only professional accountants but also business owners who rely on accounting tools to maintain compliance with financial regulations and make data-driven decisions. To help you prepare, we’ll cover everything you need to know about AI and accounting.

What Is AI in Accounting?

AI in accounting refers to any use of AI technologies, like natural language processing, deep learning, and generative AI, to help with bookkeeping, financial analysis, auditing, or compliance.

Already, AI is embedded into many accounting software tools. For example, there are bookkeeping platforms for small businesses that incorporate AI to automate financial data entry. However, AI can also be used on its own to help with accounting tasks. For example, you can use ChatGPT to ask accounting-related questions or conduct research without connecting it to your business’ books.

As AI technology continues to advance and more accountants and business owners experiment with it, expect to find even more AI-powered features in accounting software. The future of accounting is also likely to include a rise in chatbots that specialise in answering financial questions and AI agents that can complete specific accounting tasks.

How Widely Is AI Used in Accounting Today?

AI is no longer a fringe experiment in the accounting world. Recent surveys suggest that around half of professionals working in tax, accounting, audit, and related fields now use generative AI in some form in their day-to-day work.

Tax and accounting firms

Looking specifically at tax and accounting firms, research from Thomson Reuters in 2025 found that 21% of firms are already using generative AI, with a further 53% either planning to adopt it or actively considering it. The share of firms with no plans at all has dropped from roughly half to a quarter in just one year, which shows how quickly attitudes are shifting.

UK context

UK data tells a similar story. Surveys of accountants and bookkeepers indicate that the vast majority are either already using AI tools or expect to do so in the near future, with younger professionals leading the charge. One recent UK report found that 91% of accountants are using AI or plan to adopt it, and around two-thirds are already integrating it into their workflows.

Vendors experiences

Vendors are also seeing measurable impact. Research from Sage and Demos suggests that AI-enabled practices expect faster revenue growth and could collectively add around £2 billion to UK GDP, while Xero reports that nearly half of UK accountants and bookkeepers using AI have already seen productivity gains and higher profitability in their firms.

Overall trend

Taken together, these studies suggest that, for a typical UK practice or small business, AI is no longer a “nice to have”. Peers are already using it for tasks like automating bookkeeping, speeding up tax research, and generating forecasts, and the main question is how quickly and in which parts of the workflow you choose to follow.

Applications of AI in Accounting

The field of accounting already relies on structured digital data, which makes it a natural fit for AI. From automating routine tasks to supporting higher-value analysis, AI can slot into almost every stage of the finance workflow.

Automating approval workflows

One of the most powerful use cases for AI is increasing the degree of automation in approval workflows for expenses, large purchases, invoice processing and other transactions.

• Learning from past approvals: AI can analyse historical data to identify patterns in what has been approved or rejected based on transaction value, supplier, department or purpose.
• Making routine decisions: Once those patterns are understood, the system can automatically approve low-risk requests and deny those that clearly fall outside your policies.
• Escalating edge cases: You can keep a human in the loop by assigning a confidence score to each decision and routing anything high-value or low-confidence to a manager for review.

Over time, this can dramatically reduce email back-and-forth and manual checks, without removing human oversight for higher-risk transactions.

Streamlining audits

AI can play an integral role in auditing financial data by reviewing far more information than a human auditor could feasibly check.

• Automated checks: AI tools can verify that financial statements add up correctly, reconcile ledgers and identify unusual balances across large datasets.
• Full-population testing: Instead of relying on sample testing, AI can review 100% of transactions for specific risk indicators or policy breaches.
• Fraud detection: Algorithms can flag suspicious patterns, such as duplicate invoices, unusual payment timings or transactions just below approval thresholds, for a human to investigate.

This doesn’t replace professional judgement, but it does give auditors and finance teams a more comprehensive starting point for their work.

Financial forecasting

Another area where AI is already proving its value is financial forecasting and scenario planning.

• Granular analysis: AI can analyse past performance at a very detailed level – for example, by individual product, customer segment or region – rather than only broad categories.
• Real-time updates: Forecasts can be refreshed automatically as new sales, cost or cashflow data comes in, giving leaders an up-to-date view rather than a static quarterly snapshot.
• “What if” scenarios: AI models can simulate the impact of different decisions (such as changing prices, hiring plans or investment levels) to support more informed strategic choices.

This type of predictive analytics can be used by both firms and in-house finance teams to improve budgeting, inventory planning and growth strategies.

Building software integrations

Most accountants aren’t developers, but many workflows depend on getting data to flow smoothly between systems. AI can help bridge that gap.

• Natural language to code: Generative AI tools can turn plain-English instructions into simple scripts or API calls that connect sales, payroll, banking and accounting platforms.
• Faster prototyping: Rather than waiting on scarce development resources, finance teams can draft and test basic integrations themselves (with appropriate review and security checks).
• Automated data transformations: AI can help clean, map and reformat data so it’s ready to import into your general ledger or reporting tools.

Used carefully, this can reduce manual file uploads and copy-and-paste, while still allowing IT to oversee security and governance.

Centralising financial data

AI is also useful for pulling financial information together from different sources and making it easier to search.

• Ingesting unstructured data: AI can scan emails, PDFs, invoices, receipts and chat messages to extract key financial details and store them in a central location.
• Linking related records: If a particular transaction is under review, an AI tool can help locate the relevant invoice, purchase order, contract or email trail by matching values, dates or supplier names.
• Improving visibility: Centralised, searchable data makes it easier for accountants to spot trends, answer queries quickly and ensure they’re working from the latest information.

This reduces the time spent hunting for documents and helps keep everyone aligned on a single version of the truth.

Research and technical analysis

Finally, AI can act as a research assistant for accountants, auditors and finance leaders.

• Summarising rules and standards: A chatbot can be used to summarise relevant accounting standards, tax rules or sector-specific regulations, based on curated source material.
• Client onboarding and reviews: AI can quickly digest information about a new client – from financial statements to publicly available data – and produce an initial overview for a human to refine.
• Compliance checklists: Auditors and advisers can use AI tools to draft tailored compliance checklists for specific industries or engagements, which are then validated and updated by the team.

These capabilities can save hours of reading and note-taking, as long as outputs are treated as a starting point and checked against up-to-date legislation and professional guidance.

How Different Types of Accounting Firms Are Using AI

AI isn’t being adopted in the same way everywhere. Large firms, mid-sized practices, and small local firms are all experimenting with similar tools, but they’re applying them to different parts of the workflow.

Research from Thomson Reuters’ 2025 Generative AI in Professional Services programme shows that the Big Four tend to embed AI deeply into audit and tax platforms, while smaller firms and in-house teams focus on targeted use cases such as research, bookkeeping, and advisory work.

Large firms and the Big Four

The Big Four and other large firms have the scale to build or customise their own AI platforms. They’re using AI to standardise work globally, reduce risk, and free up senior teams for higher-value advisory.

• AI-enabled audit platforms: Deloitte has expanded generative and agentic AI capabilities inside its Omnia audit platform so that AI can perform first-pass reviews of audit documentation and suggest improvements to clarity and consistency before a human signs anything off.
• Unified AI platforms across services: EY’s EY.ai platform brings together AI and the firm’s existing technology stack across assurance, tax, strategy and transactions, with new AI capabilities now supporting more than 160,000 global audit engagements.
• In-house AI apps and frameworks: PwC reports 20 to 50% productivity gains in its development processes from internal generative AI tools that synthesise data, generate and review code, and troubleshoot issues, while KPMG’s Trusted AI framework is used to design and deploy AI solutions for clients in a more controlled, auditable way.

For UK businesses, this matters because many audit, tax, and advisory services are shaped by methodologies first developed in these larger firms. As their AI playbooks mature, they tend to filter down into mid-tier and regional practices, and into the software tools those firms use.

Mid-sized and regional practices

Mid-tier and regional firms are typically less focused on building their own AI and more focused on plugging AI into existing tax and accounting workflows. According to Thomson Reuters’ 2025 Generative AI in Professional Services Report, the top five use cases among tax and accounting firms using or planning to use generative AI are:

• Tax research: using AI-powered research tools trained on curated, human-edited tax content to answer complex questions quickly and consistently.
• Tax return preparation: automating the extraction and analysis of data from client documents, then using AI to surface relevant reliefs, deductions and credits and flag potential issues before filing.
• Tax advisory: generating scenario models and projections so partners can talk clients through the tax impact of different decisions, backed by data rather than gut feel.
• Bookkeeping and compliance automation: categorising expenses, reconciling bank feeds and producing management reports with minimal manual input, often via cloud accounting software that has AI built in.
• Document summarisation: summarising contracts, engagement letters, invoices and receipts, and highlighting anomalies or clauses that may carry risk.

These firms are generally trying to balance efficiency gains with client expectations: using AI to shorten turnaround times and increase the volume of advisory work they can offer, without losing the “human” relationship that justifies their fees.

Small practices and sole practitioners

Smaller firms and sole practitioners tend to adopt AI more informally at first. The same Thomson Reuters research notes that, among tax firms already using generative AI, 52% are relying on open tools such as ChatGPT, compared with 17% using industry-specific platforms.

In practical terms, that often looks like:

• asking a general-purpose chatbot to draft client emails, engagement letters or first-draft advice (which is then edited for accuracy and tone);
• using AI to summarise accounting and tax standards, or to build checklists for a new sector or service line;
• relying on AI-powered features in tools they already use – for example, automated invoice capture, bank-feed categorisation, or cashflow forecasts inside small-business accounting software.

Survey work from Intuit QuickBooks’ 2025 Accountant Technology Report also suggests that around 8 in 10 accountants expect strategic advisory work to grow, and more than 80% say AI improves productivity, which helps explain why even very small firms are experimenting with these tools.

In-house finance teams and SMEs

AI is also being adopted inside finance teams at SMEs and larger corporates, often through the same cloud platforms and tools their accountants use. A joint study from Sage and Demos argues that widespread AI adoption in UK accounting could add around £2 billion to GDP and create thousands of new jobs, while recent research from Xero suggests that extensive AI use has already boosted sector profitability and delivered measurable productivity gains for almost half of UK accountants and bookkeepers.

Common in-house use cases include:

• automating invoice processing, expense approvals and purchase order workflows;
• using AI-driven dashboards for real-time cashflow, scenario planning and “what if” analysis for senior leadership;
• using AI-powered help and support tools in accounting software to resolve queries more quickly and reduce dependence on external advisers.

For UK business owners, the key takeaway is that AI adoption is not confined to the largest firms. Whether you work with a Big Four auditor, a regional practice or a solo accountant, there is a good chance AI already sits somewhere in the background of your bookkeeping, tax, and forecasting processes – and that presence is only likely to grow over the next few years.

Benefits Of Using AI In Accounting

Leveraging AI in accounting doesn’t just save time – it can improve accuracy, relieve pressure on teams, and give business leaders a clearer view of their finances. Here are some of the key benefits.

Eliminate human errors

Manual data entry and repetitive tasks are where many accounting errors creep in. AI helps reduce this risk by taking over the most error-prone parts of the workflow.

• Automated data capture: AI tools can read invoices, receipts and bank feeds, then post them automatically to the right accounts.
• Consistent coding: Once trained, AI applies the same logic every time, reducing misclassifications and transposed figures.
• Automatic checks and alerts: Anomalies – such as unusual balances or duplicated transactions – can be flagged for review before they reach your financial statements.

This helps keep your books cleaner and reduces the risk of non-compliance or incorrect reporting caused by simple human mistakes.

Enhance productivity

AI is well suited to high-volume, rules-based work that would otherwise absorb a lot of staff time.

• Faster routine processing: Tasks like invoice posting, bank reconciliation and basic reporting can be handled in minutes rather than hours.
• Time back for higher-value work: By taking care of repetitive admin, AI lets accountants spend more time on analysis, advisory and client-facing work.
• Always-on assistance: AI-powered tools can answer simple queries, draft documents or summarise information on demand, without waiting for colleagues to be available.

For firms and finance teams under pressure, this can mean handling a larger workload without immediately adding headcount.

Reduce burnout

The accounting profession has struggled with staff shortages and high workloads, especially around busy periods. AI can ease some of that pressure.

• Fewer late nights on low-level tasks: When AI takes on data entry and first-pass checks, staff can focus on the work that genuinely needs their expertise.
• More manageable peaks: Automating predictable busy-work around month-end, year-end or tax season helps smooth the spikes that often lead to burnout.
• More engaging roles: As the mix of work shifts towards advisory and problem-solving, roles can feel more varied and rewarding.

Used thoughtfully, AI can support retention by making workloads more sustainable and freeing people from the most repetitive parts of their job.

Improve decision-making

AI doesn’t just process data more quickly – it can also help businesses see patterns and possibilities that might otherwise be missed.

• Better visibility: AI-supported tools can pull data together from different systems and present it in dashboards that update in near real time.
• Richer analysis: Forecasting models and scenario tools can show how changes in sales, costs or investment plans might affect cashflow and profit.
• External context: AI can help incorporate market data, peer benchmarks or sector insights alongside internal numbers when you’re planning next steps.

That combination of up-to-date information and faster analysis makes it easier for leaders to make confident, data-driven decisions.

Minimise compliance risk

Compliance obligations are only becoming more demanding, particularly as tax authorities and regulators push towards digital-first systems. AI can help firms and businesses stay on top of these requirements.

• Proactive monitoring: AI can continuously scan transactions and processes for signs of non-compliance or unusual activity that warrants investigation.
• Keeping up with rule changes: Research tools and specialist platforms can track changes in accounting standards or sector-specific regulations and highlight relevant updates.
• Supporting documentation: AI can help generate and organise the working papers, audit trails and reports you need to demonstrate compliance if you’re audited or reviewed.

While AI doesn’t remove the need for professional judgement, it can act as an always-on assistant that lowers the chance of missing something important.

Challenges Facing AI Use For Accounting

While AI has potentially revolutionary benefits for accounting and financial management, implementing it isn’t completely seamless. Business owners, accountants and auditors need to be aware of some key challenges before rolling out new tools.

Upfront costs

The first barrier for many firms and finance teams is the cost of AI-enabled tools and the work required to embed them.

• Higher software prices: Accounting software that incorporates AI is often more expensive than basic packages without automation or predictive features.
• Implementation and integration spend: If you’re adding standalone AI tools (for example, a chatbot or document-processing engine), there may be extra costs to integrate them with your existing systems.
• Training and change management: Staff need time and support to learn how to use new tools safely and effectively, which can mean lost productivity in the short term.

These upfront costs can be worthwhile if AI delivers sustained time savings and error reductions, but they do need to be factored into budgets and business cases.

Data privacy and security

Financial data is among the most sensitive information a business holds, so any use of AI must be carefully controlled.

• Where your data goes: Many AI systems process information on the provider’s servers rather than on your own network, so it’s essential to understand how and where data will be stored and used.
• Use in model training: Some providers may use customer data to train or improve their AI models, which raises confidentiality concerns if controls aren’t clear.
• Regulatory obligations: Firms must ensure that any AI tools they adopt align with data protection duties (for example, under UK GDPR), client confidentiality commitments and professional ethics requirements.

Before adopting AI, businesses should review vendor contracts, security documentation and data-handling policies, and decide what information should never be entered into public or consumer tools.

Integration challenges

AI is most effective when it has access to data from across your finance stack, but connecting everything together can be technically demanding.

• Legacy systems: Older accounting, ERP or payroll platforms may not integrate easily with modern AI tools or APIs.
• Fragmented data: If information is spread across spreadsheets, email inboxes and separate apps, it can take significant effort to centralise and clean it before AI can add value.
• Ongoing maintenance: Integrations and automation scripts need to be monitored and updated as systems change, or they can silently break and undermine trust in the results.

In many cases, firms find it easier to start with AI capabilities built into their existing cloud software, then work towards more ambitious integrations once they have a stable foundation.

How To Introduce AI Into Your Accounting Workflows

Bringing AI into your accounting processes works best when it’s treated as a structured change project, not just “switching on” a new feature. Professional bodies such as ACCA
and ICAEW recommends starting with clear objectives, a written AI policy, and a people-focused approach so that staff and clients understand how these tools will be used.

1. Clarify the problems you want AI to solve

Rather than asking “what can AI do?”, start with the specific pain points in your firm or finance team. For example, are month-end close tasks taking too long, is invoice processing overly manual, or are staff spending hours on simple tax research?

ICAEW guidance on implementing AI emphasises defining the business problem, securing agreement from senior partners, and setting success criteria before you buy or configure any tools.

At this stage, it can be helpful to list the top two or three areas where automation or better insights would have the biggest impact – whether that’s cutting admin time, reducing errors, or freeing up capacity for advisory work.

2. Map your existing systems and look for built-in AI

The easiest way to adopt AI is often to use capabilities that already exist in your current stack. Cloud platforms like Xero, Sage and QuickBooks are steadily adding AI features around invoice capture, bank reconciliation, cashflow forecasting, and client communications, and many firms are already seeing gains simply by switching these on or extending how they’re used.

Xero’s AI guides for accountants and Sage’s AI accounting resources both encourage practices to start by exploring AI tools embedded in software they already know.

Alongside core accounting software, review your tax platforms, document management tools, and practice management systems to see where AI-enabled add-ons or upgrades might help with the problems you identified in step one.

3. Start with a small, low-risk pilot

Once you’ve chosen a target area and a tool, run a limited pilot rather than rolling AI out firm-wide on day one. ICAEW’s generative AI guidance and vendor playbooks from Xero both recommend experimenting on real but low-risk tasks first – for example, using AI to draft internal working papers, summarise standards, or pre-populate expense categories that a human then reviews.

Document what you’re testing, which teams are involved, and how long the pilot will run. Make it clear that AI is there to support staff, not to replace their professional judgement, so that people feel comfortable giving honest feedback.

4. Put an AI policy and data safeguards in place

Before AI becomes part of everyday workflows, you’ll need some guardrails. ACCA’s guidance on using AI in accounting stresses the importance of a written AI policy that covers which tools are approved, what data can and cannot be entered, how outputs are reviewed, and who is accountable for decisions.

That policy should sit alongside your existing UK GDPR and confidentiality obligations. Public chatbots and generic tools should never be fed client-identifiable financial data unless the provider offers appropriate contractual and technical safeguards.

Recent guidance from bodies such as ACCA on AI ethics and EY and ACCA’s joint paper on AI assessments also suggests putting in place basic governance over vendor due diligence, risk assessments, and periodic reviews of how AI tools are performing.

5. Train your team and update your processes

AI projects tend to succeed or fail based on how people use them. Studies from Sage and Demos show that many accountants are optimistic about AI but don’t yet feel fully prepared in terms of skills.

Investing in training – whether via ICAEW’s AI resources, ACCA’s AI insights, or vendor-led courses – helps staff understand where AI is strong, where it’s weak, and how to review outputs critically.

As you roll AI out, update process documentation and checklists to reflect the new steps. For example, you might add a stage where a junior uses AI to draft a memo and a senior reviews both the draft and the prompt, or where AI flags unusual transactions and a human documents how they were investigated.

6. Measure impact and iterate

Finally, treat AI adoption as an ongoing improvement project rather than a single switch-on moment. Research summarised by Sage and Xero suggests that AI-enabled firms see higher productivity and faster revenue growth, but they also report gaps around training and change management.

Define a small set of metrics you’ll track over time, such as:

• time taken to complete specific processes (for example, invoice approvals or management accounts);
• error rates or the number of adjustments identified at month-end or year-end;
• staff satisfaction or reported workload around busy periods;
• the volume and value of advisory work you can deliver alongside core compliance tasks.

Review these regularly, retire experiments that aren’t working, and double down on the use cases that clearly save time, reduce risk or improve client service. That way, AI becomes a deliberate part of how your firm operates, rather than a collection of one-off tools scattered across different teams.

Emerging Trends in AI and Accounting To Watch

AI in accounting is moving beyond simple automation and into more autonomous, connected systems. Recent developments from players like Thomson Reuters, Intuit, and a wave of AI-native startups point towards a future where AI agents handle larger chunks of finance work and traditional ledgers and ERPs are gradually reimagined.

Agentic AI in tax, audit and accounting

Industry providers are beginning to roll out so-called “agentic” AI systems that can plan, reason, and carry out multi-step workflows with limited human intervention.

Thomson Reuters’ 2025 generative AI report for tax professionals highlights agentic AI as the next wave after basic chatbots, and its CoCounsel Tax, Audit and Accounting updates
introduce agents that can help automate elements of return preparation and audit workflows.

Analysts at Gartner also expect agentic AI to be built into a growing share of enterprise software, while warning that many early projects may be cancelled if they don’t deliver clear value.

AI agents inside mainstream accounting software

AI is also being embedded directly into the accounting platforms that many UK businesses already use. Intuit has launched a suite of AI agents and Intuit Assist in QuickBooks,
designed to help with tasks like sending invoices, reconciling bank activity, and chasing overdue payments.

A recent Intuit announcement describes these agents as a “virtual team” that manages day-to-day finance workflows, while a separate multi-year partnership with OpenAI
aims to deepen the integration of AI models across QuickBooks, TurboTax and other tools. For many SMEs, the first experience of advanced AI will simply be new capabilities appearing inside the software they already use.

AI-native accounting and ERP platforms

Alongside the incumbents, a new generation of AI-native platforms is emerging to challenge traditional ledgers and ERPs. Start-ups such as Rillet and DualEntry have raised large funding rounds to build accounting and ERP systems with AI at their core, promising faster closes, automated bank matching, anomaly detection and quicker data migrations than established vendors.

Rillet, for example, positions itself as an AI-powered ledger that can help companies close their books in days rather than weeks, while DualEntry markets an AI-first ERP aimed at mid-sized businesses outgrowing entry-level software.

Other tools, like Basis, are pitching AI “agents” that behave like junior accountants inside firms, handling repetitive workflows and freeing qualified staff to review and advise. For UK practices and finance teams, these developments signal a gradual shift from add-on automation towards accounting platforms where AI is built into the fabric of how transactions are captured, checked, and reported.