What accounting automation with AI concretely means in 2026
“AI automation” in accounting doesn’t mean a system that keeps a company’s books on its own. It means a few repetitive, well-defined workflows, where a model or a set of rules makes the first pass over the data — and a person checks, corrects and approves. At the level of a general finance function, McKinsey estimates that technologies that already exist can automate almost half of activities fully (42%) and nearly another quarter (19%) partially — but the figure is a global average across financial activities in general, not a specific promise for a small Romanian business on SAGA.
What matters in practice is granularity: you don’t “automate accounting”, you automate capturing an invoice, matching a payment, or a format check before filing — each with its own level of risk if it goes wrong.
Capturing invoices from e-Factura/SPV: what can be automated
From 1 January 2024, businesses were required to report B2B invoices through the RO e-Factura system, and from 1 July 2024 transmission through the system became fully mandatory, with penalties for non-compliance. The system is based on the same European e-invoicing standard set out in Directive 2014/55/EU — originally designed for public procurement, later extended by Romania to B2B transactions as well. Invoices therefore circulate as structured XML files through SPV, not as free-form PDFs, and it’s precisely this standardised structure that makes automation possible: a workflow can automatically download new invoices from SPV, extract the supplier, the amount, VAT rates and product lines, and match them against suppliers already on record.
The useful part for a small business is that no one has to manually read every invoice to enter it anymore — but someone still has to validate the exceptions: a new supplier not yet registered, an invoice with a discrepancy between SPV and the original order, or a line that doesn’t match any existing account. Automation reduces the volume of manual entry, not the need for checking.
Automatic matching of bank statements with invoices and payments
Bank reconciliation — matching each line on a statement with the corresponding invoice or payment — is a good candidate for automation because the rules are, in most cases, predictable: the amount, date and payer name match often enough for a system to make the first pass automatically. What’s left for a human are the exceptions — partial payments, a payer with a name different from the invoiced company, a duplicate payment, or an exchange-rate difference on payments in another currency.
A well-built workflow doesn’t hide the exceptions, it brings them to the surface: the line that didn’t match automatically should appear clearly, with the reason it didn’t match, not just “unresolved” in a long list.
Automatic classification of expenses by account and tax category
Based on the company’s accounting history, a model can learn the pattern “this supplier usually goes on this account” and suggest the classification for similar new invoices. It’s especially useful for businesses with a high volume of repeat expenses — fuel, utilities, software subscriptions — where the pattern stays stable from one month to the next.
The real risk isn’t an isolated error, it’s silent drift: a model trained on an old pattern keeps classifying “correctly” by the old rules, even after the company changes suppliers or its expense structure, and no one notices until an audit or the year-end close. That’s why automatic classification needs a sample checked periodically by an accountant, not just an initial setup left to run on its own.
Automated pre-checks before filing SAF-T (D406)
ANAF publishes its own schemas (XSD) and a validation tool for the SAF-T file, and as the D406 reporting obligation has extended to small businesses, micro-enterprises and companies with no employees, the number of firms going through this periodic process has grown a lot. An automated check, run before filing, can catch typical format problems early — for example, the wrong tier of synthetic account or an incorrectly filled-in IBAN — using the official validator itself, or rules equivalent to it.
The useful automation here doesn’t replace filing, it prepares for it: it runs the validation, lists exactly what doesn’t pass, and leaves the final decision on correcting and filing to the accountant responsible for the file.
What actually stays a human decision
A recent ICAS (Institute of Chartered Accountants of Scotland) study of professionals in the field shows a consistent pattern: 74% say AI speeds up their tasks, but only 22% felt a real increase in the volume of work actually completed — the gap shows that acceleration at the level of an individual task doesn’t automatically translate into extra capacity across the whole workflow. In the same study, 72% of respondents worry that generative AI can produce errors or reach wrong decisions, and the most frequent uses remain routine tasks — drafting text, summarising, working in spreadsheets — not decisions with tax implications.
Month-end closing, choosing a tax treatment in unclear areas, provisions, and any interpretation that involves taking responsibility in front of an audit remain, structurally, human decisions. A model can prepare the information and flag inconsistencies; it can’t carry the professional responsibility of the signature.
What an automation workflow over SAGA C looks like, in practice
SAGA C doesn’t have a public API that other systems can use to read or write directly to its database. In practice, this means any automation is built on three components: scheduled exports and imports (the native files SAGA accepts), RPA-type automation for repetitive interface steps that can’t be replaced by a file, and a document-AI layer (optical recognition plus a language model) that reads scanned invoices, statements or other unstructured documents and turns them into the format SAGA can import.
Studios like The Niche Society build workflows like this on top of SAGA’s native exports, precisely because there’s no more direct route — any “instant” integration promised for SAGA, without this export/import step, is worth checking carefully before you trust it.
Controls, audit trail and the limits set by context
Every automated step that writes something to the accounting records should leave a verifiable trail: what was changed, based on which source document, at what time and with what result. Without this log, an error discovered late — a duplicated invoice, a misclassification repeated for five months — becomes almost impossible to trace back to its origin. This also covers useful automatic reminders, not just for filings: an alert when a bank line stays unmatched for too many days, or when a reporting deadline is approaching, stops exceptions from piling up unseen.
Just as important are the approval checkpoints: nothing should ever reach ANAF filed directly from an automated flow, without an explicit human confirmation step before submission. And because the data being processed is the company's financial data — sometimes personal data too, in payroll or CNPs on returns — the same confidentiality concern the ICAS study flags (52% of respondents mentioned concerns about the confidentiality of customer data) applies directly: where the model runs and who has access to the data sent to it matter just as much as the accuracy of the result. What this looks like in SAGA, without switching software, we describe on the page about accounting automation.
A good accounting automation workflow isn’t the one that looks the most autonomous, it’s the one where you can always reconstruct, step by step, why a figure ended up where it did.
Sources and further reading.
Frequently asked questions
Does SAGA have an API for automation?
No, SAGA C doesn’t expose a public API. Automation is built on top of file exports and imports, RPA automation for interface steps and, where unstructured documents need to be read, a document-AI layer that prepares the data in the format SAGA accepts.
Can AI file the SAF-T (D406) declaration with ANAF on its own?
It shouldn’t. AI can run format pre-checks and flag errors before filing, but the final decision to correct and the actual submission to ANAF must remain a step manually confirmed by the accountant responsible.
Is it safe, from a confidentiality standpoint, to process accounting data through AI?
It depends on where the model runs and what happens to the data sent to it. It’s a real concern among professionals in the field too — an ICAS study found that over half of respondents cited client data confidentiality as a risk. Access control and a clear audit trail are minimum requirements, not optional extras.
How much time can realistically be saved through accounting automation?
There’s no universal percentage that applies to every business. Globally, McKinsey estimates that almost half of a finance function’s activities can be automated fully, and nearly another quarter partially, with technologies that already exist — but the real time saved, at a small business in Romania, depends on the volume of documents and how clean the data you start from is.
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