An accountant’s day should be about numbers that mean something. In practice, a good part of it goes on chasing missing documents, checking data someone entered in a hurry, and working out why two reports disagree. That is true everywhere. It is a particular kind of true in Lithuania, where a small number of accountants keep the books for a very large number of very small companies, and where the tax authority has been collecting invoice-level data since 2016.

AI will not make a missing document appear, and it will not take responsibility off the accountant’s desk. What it can do is cut the manual work in between: finding information, comparing periods, and preparing routine tasks.

The Lithuanian context: many small clients, plenty of reporting

Lithuania’s business landscape is built out of very small companies. According to Statistics Lithuania, more than eight in ten operating small and medium-sized enterprises employ fewer than 10 people. Micro companies still need VAT returns, payroll, annual reports and an audit trail, so the reporting load per client does not shrink in proportion to the client’s size.

On top of that sits i.MAS, the State Tax Inspectorate’s smart tax administration system. Since 1 October 2016, VAT payers have submitted registers of issued and received invoices through i.SAF by the 20th of the following month, with i.VAZ covering goods transport and i.SAF-T covering accounting data files. Lithuania was early to invoice-level reporting, and Lithuanian accountants have been living with a monthly data-quality deadline ever since.

The next step is already scheduled. Lithuania is preparing a mandatory B2B e-invoicing model from 2028, built on the EN 16931 European standard and aligned with the EU’s VAT in the Digital Age rules that take effect in 2030. Invoices will move as structured data rather than PDFs. That is good news for anyone whose software is ready, and a lot of manual re-keying for anyone whose software is not.

Where the time actually goes

Accounting is far more than entering invoices and filing declarations. A typical week includes hunting for missing documents and transaction details, checking what was entered, comparing periods, tracing the cause of a difference, answering client questions, looking things up in software manuals, and producing invoices, reports and summaries.

Individually, none of these takes long. Dozens of them a day is a full workload, and constant task-switching under deadline pressure raises the odds of a mistake.

A Gartner survey of 497 finance professionals found that 18% of respondents made financial errors at least daily, around a third made several errors a week, and 59% made several errors a month. Gartner linked those errors to capacity constraints rather than to competence.

So the point of automation is not to squeeze more tasks into the same day. It is to take the pressure out of the day.

How much time can AI realistically save?

This is no longer theory. Researchers from Stanford and MIT studied generative AI use in accounting firms, surveying 277 accountants and analysing data from 79 small and mid-sized firms. As reported by the Journal of Accountancy, accountants using AI shifted about 8.5% of their working time away from routine data entry and transaction classification. In a 40-hour week that is roughly 3.5 hours, or about 182 hours a year. More than four full working weeks.

The rest of the findings matter just as much:

• 55% more weekly client support
• 21% higher billable hours
• month-end close completed on average 7.5 days earlier
• 12% more granular general ledger records

That last figure is the interesting one. The time saved did not come out of quality. The records got more detailed, not less.

Lithuania is already moving in this direction

Lithuanian businesses are not waiting for permission. According to the State Data Agency, 21.3% of Lithuanian enterprises used AI technologies in early 2025, up 12.5 percentage points in a single year. Eurostat puts the EU average at 20% and records Lithuania’s jump as the third largest in the Union, behind Denmark and Finland.

The same national data shows 58.1% of enterprises buying cloud services, and 43.5% buying finance or accounting software that way. In other words, the software is already in the cloud. Adding an assistant that reads the data inside it is a much smaller step than it sounds.

Why an AI assistant works better inside your accounting software

A public chatbot can explain what a fixed asset is. It does not know your client’s turnover, your VAT position or which invoice is missing from March.

The SimplBooks AI assistant works where the financial data already lives. Nothing has to be exported into a spreadsheet, and nothing has to be pasted into a public chat window.

It can help you:

• get answers about accounting and about using SimplBooks, including Lithuanian specifics such as VAT returns and i.SAF
• build clear summaries from the company’s own figures
• compare results across periods
• spot possible errors and entries worth a second look
• digitise purchase invoices and receipts from a photo
• carry out tasks in the software from instructions written in plain Lithuanian

For example, you can ask it to compare this year’s sales revenue with last year’s, summarise the last six months, or break down labour costs by quarter. You can also give it an instruction rather than a question: “Create a 350 EUR invoice for client X.” It prepares the invoice from data already in SimplBooks, and you check and confirm it.

On the error-hunting side, ICAEW has written about how generative AI can ease the accounting cycle, including the period-end close, by working through data that would otherwise be checked line by line. That is often the difference between a five-minute check and an afternoon spent scrolling a transaction list.

If you keep your own books

Not every Lithuanian company has an accountant on the payroll. Plenty of founders do the books themselves, usually in the evening, usually while also doing three other jobs.

The same time savings apply, they just show up differently. Instead of searching a help centre for how VAT on an EU purchase should be recorded, you ask. Instead of building a spreadsheet to see whether this quarter was better than the last, you ask. And when the accountant does step in, they get tidier data to work with, which usually costs less to fix.

AI does not replace professional judgement

The value here is not replacement. It is the removal of routine.

AI can find and gather information, make a first comparison and prepare a task. The decision and the responsibility stay with the person. An invoice prepared by the assistant is checked before it is sent. A flagged discrepancy is reviewed before it becomes a correction.

That division of labour is the whole point. AI finds the information, the accountant decides what it means. AI prepares the task, the accountant confirms it. What is left is the work that actually needs experience, context and professional judgement, which is also the work clients are happy to pay for.

Less time searching, more time worth charging for

When most of the day goes on chasing documents, checking entries and correcting errors, very little is left for understanding what the numbers say about the business.

Saving a few hours a week adds up to dozens or hundreds of hours a year. That time can go into advising clients, analysing results, or simply finishing the month calmly and accurately.

The SimplBooks AI assistant lives in the same place as your daily accounting work, and it reads your actual company data. It is available in every paid plan from 5 EUR a month, and SimplBooks itself starts at 9.90 EUR a month.

Try SimplBooks and the AI assistant free for 30 days.