Last updated: 17 August 2026

Accounting automation uses software and AI to take over the manual work of bookkeeping, from data entry and transaction matching to reconciliation and reporting. How much of that work you can safely hand over, though, is set by something less visible than the tools themselves: the quality of the data underneath them. Clean, categorised, reliable transaction data raises the ceiling on what automation can do while messy data lowers it, however capable the AI model you’re using.

Active use of AI in finance functions rose from 30% in 2024 to 75% in 2026, according to KPMG. AI has moved from something teams pilot to something they run in production, and the constraint on the value it delivers has moved with it from what the models can do to whether the data feeding them is good enough to rely on.

In this article, we explore:

• What accounting automation is, and which tasks it can genuinely take over

• What changes when AI, rather than fixed rules, drives the automation

• Why data quality, not model capability, now sets the limit on what you can automate

• What accounting and ERP platforms can build once the underlying data is clean and categorised.

Building AI-driven features into your accounting or ERP platform? Book a call with Yapily to talk through the data foundation underneath them.

Irene Brime, Director of Sales at Yapily, says: "AI has moved from a talking point to a buying criterion in accounting faster than almost any shift I've seen in this market. The platforms customers are choosing now are the ones that can turn automation into genuine intelligence, and that only happens when the data underneath is clean and reliable. Over the next few years, I expect the distance between the platforms that get their data foundation right and those that don't, to widen quickly."

What is accounting automation, and how does it work?

Accounting automation is the use of software and, increasingly, AI to replace manual accounting tasks with automated workflows: capturing transactions, identifying and categorising them, reconciling accounts, and producing reports. The technologies behind it go by several names, like machine learning, robotic process automation, optical character recognition, and more recently, agentic AI. But they serve one purpose, which is to remove repetitive manual steps so the people involved can concentrate on judgement and review. IBM's definition frames it much the same way.

In practice, accounting automation tools sit on top of a business's financial data and act on it automatically. How much value they deliver depends heavily on how clean and structured that data is when it arrives, a point we’ll be returning to throughout this article.

Which accounting tasks can be automated?

Some tasks lend themselves to automation more readily than others. The ones you will most often see handled by accounting automation tools include:

• Accounts payable and accounts receivable processing

• Bank reconciliation, matching transactions to records

• Transaction categorisation and coding

• Month-end close and consolidation

• Financial and management reporting

• Cash-flow analysis and short-term forecasting

For a wider view of how open banking supports these workflows, see 8 ways accounting businesses can benefit from open banking and our guide to open banking for business accounts.

From rules to reasoning: What AI changes in accounting automation

The difference between rules-based automation and AI in accounting comes down to how each handles messy data. Rules-based automation executes on fixed logic, so if a transaction matches a defined pattern, code it a certain way. It is fast and predictable, but it stalls the moment it steps outside the rules and is less adaptable. AI usually works by reasoning over inputs that do not fit a fixed template, proposing a treatment, and handing it to a person to approve. That is what lets AI tools for accounting handle the variety of real transaction data.

Gartner has found that inadequate data quality and data literacy remain the largest obstacles to AI adoption in finance, and predicts that 90% of finance functions will run at least one AI-enabled solution by the end of 2026. Adoption is already well advanced: KPMG puts active AI use in finance at 75%, with 71% of finance leaders saying AI is meeting or exceeding the return they expected.

The consequence for anyone building these features can be easy to miss. AI raises the limit on what can be automated, but it also moves where that limitation sits. With rules-based automation, the limit was the logic you could write, but with AI, the limit is the quality of the data the model reasons over.

Why accounting automation is only as good as the data behind it

Automation is only as good as the data behind it because AI outputs inherit the quality of their inputs. In accounting, where every entry is reviewed and every reconciliation has to survive an audit, poor data does not stay hidden. It surfaces as miscategorised transactions, broken reconciliations, and numbers a finance team cannot back with certainty. This costs trust that is slow and difficult to rebuild.

The evidence for this is consistent across recent surveys. In a cross-industry study reported by the PEX Network, 52% of professionals named data quality and availability as the biggest challenge to AI adoption, ahead of internal expertise at 49%. KPMG found that 36% of finance leaders cite data quality as both their top barrier and their top opportunity, the single most-named factor on either side. Gartner reaches the same conclusion for finance, specifically naming data quality and data literacy as the largest obstacles to adoption.

In accounting, worries about where problems may occur sit where there’s the most accountability. A survey by Accounting Seed found that 35% of finance-team members fear AI errors or hallucinations, compared with only 11% of IT administrators. The people who sign off the numbers are the most cautious, because they are the ones who answer for them.

What causes AI accounting tools to make mistakes?

When automated accounting tools produce wrong or unreliable output, the cause usually sits in the data rather than the model. Three failure modes account for most of it:

• Data drawn from a wrong or unverified source, so the input is untrustworthy before any processing begins.

• Data that has not been properly prepared, cleaned, categorised, and structured, so the model reasons over noise.

• Plain "rubbish in, rubbish out", where poor inputs produce poor outputs, however capable the tool.

None of these is fixed by a better model or advances in AI. They are fixed by better data.

The importance of human oversight

AI is unlikely to replace accountants in an audited function, but it will likely change what they spend their time on. There is a clear pattern settling across finance where the AI proposes a treatment and a qualified person approves it. In a function where accuracy and granular detail are so important, it’s critical that the accountable human doesn’t leave the loop. The systems that earn trust are the ones designed around that reviewer rather than around removing them.

Raw bank data versus enriched data: What accounting automation software can actually do

What data do accounting automation tools actually need to work well? Raw bank data is necessary but not sufficient. It becomes reliable only once transactions are categorised, merchant names are cleaned up, and the intermediaries in a payment are identified. The gap between what accounting automation software can do on raw bank data feeds and what it can do on enriched banking data is wide, and this is what enables a platform to turn automation into genuine intelligence.

Raw vs. enriched/categorised data

Open banking data can refresh up to four times a day, and a standard connection gives up to 12 months of transaction history, which is plenty for cash-flow analysis and forecasting. Yet frequently refreshed data that is still uncategorised only moves the cleanup problem closer to real time. Freshness is a necessity for real-time analysis, but accurate categorisation is what makes it most usable.

This is the layer Yapily’s Data Plus occupies. It sits on top of raw account data and does the preparation: categorising and enriching every transaction, cleaning messy merchant strings into recognisable names, identifying intermediaries such as payment processors, and aggregating multiple data vendors into a single enrichment layer behind one integration, across both consumer and business accounts, in accordance with Yapily's applicable data-protection obligations. For the full product details, see the Data Plus explainer or try our demo below.

Why accounting and ERP platforms build on Data Plus

Everything so far points to the same requirement: to build AI-driven accounting features people can trust, you need clean, categorised data as the foundation, not raw strings your own team has to fix first. This is why accounting and ERP platforms build on Yapily.

Aga Prawda, Senior Product Manager at Yapily, says: "Data Plus takes the raw account data and does the work that makes it usable: categorising and enriching every transaction, and cleaning messy merchant strings so a ledger reads 'Starbucks' rather than 'STBKS-123'. It aggregates multiple markets' unstructured data into a single enrichment layer behind one integration, across both consumer and SME accounts, so an accounting platform gets clean, categorised data its models and finance teams can work with directly. That's the layer that turns basic automation into reconciliation, cash-flow and insight features people can actually trust."

Enriched, categorised transaction data out of the box

The product owner building AI-enabled accounting needs data their models and finance teams can trust from the first call, not raw feeds someone has to clean up first. Data Plus categorises transactions across 40+ incoming categories and 100+ outgoing or spend categories, spanning both consumer and business accounts. Categorisation accuracy runs at 80–95%, depending on the use case and market.

Clean merchant data your models can read: Starbucks, not STBKS-123

Raw merchant strings are one of the biggest sources of pre-processing work. Data Plus cleans them into recognisable names and identifies the intermediaries behind a transaction, such as payment processors, so your data teams and models get usable data directly. Its models have been trained on more than 100 million unique merchants globally, which is what lets the categorisation hold up across the long tail of real-world transactions.

One integration across consumer and business accounts, aggregating multiple data vendors

Enrichment is not one job; it is several, and stitching together separate providers to cover them adds cost and complexity behind your platform. Data Plus aggregates multiple data vendors into a single enrichment layer behind one integration, covering both consumer and business accounts, so you contract and maintain one connection rather than several.

A data foundation breadth accounting platforms can rely on

Beneath the enrichment sits the coverage. Yapily connects to over 2,000 banks across 19 countries, with 99.5% UK bank coverage, which gives accounting platforms the reach they need across the accounts its customers actually use. Clients like Intuit QuickBooks, Yapily's first customer, along with Pleo for expense management, and Kolleno for reconciliation, have built on this foundation. For engineering teams, the Data Plus technical documentation covers the details.

The next step

If you are weighing up how to give your accounting platform a data foundation that AI features can rely on, the practical next step is to see the enrichment layer in action and compare it against what you are working with today. Book a call with Yapily to walk through Data Plus, the coverage behind it, and how it fits the features on your roadmap.

Sources

The external statistics in this article are drawn from the following sources:

1. KPMG International, 2026 Global AI in Finance: The Decision Advantage. https://kpmg.com/xx/en/media/press-releases/2026/05/ai-adoption-in-finance-doubles-but-assurance-readiness-determines-who-wins.html

2. PEX Network, PEX Report 2025/26, reported by AI Data & Analytics Network. https://www.aidataanalytics.network/data-science-ai/news-trends/data-quality-availability-top-list-of-ai-adoption-barriers

3. Gartner finance-practice survey (November 2025), reported by CFO Dive. https://www.cfodive.com/news/cfos-ai-adoption-slows-challenges-mount-gartner/805949/

4. Accounting Seed, 2026 AI in Accounting Industry Survey. https://www.accountingseed.com/resources/the-state-of-ai-in-accounting-2026/

5. insightsoftware, AI in Finance research (June 2026). https://www.insightsoftware.com/blog/new-insightsoftware-research-reveals-trust-not-technology-is-the-biggest-barrier-to-ai-adoption-in-finance/

6. IBM, What Is Accounting Automation? https://www.ibm.com/think/topics/accounting-automation (corroborated by BlackLine, Accounting Automation glossary. https://www.blackline.com/resources/glossaries/accounting-automation/)