AI invoice coding works in three steps. First the machine reads structured data from the document: supplier, lines, amounts, VAT. Then it proposes a coding based on your own history – this supplier’s invoices have gone to this account before, similar line texts to this cost centre. Third, a confidence level decides what happens next: a high-confidence entry can post automatically, a low-confidence proposal comes to you with options. This is not magic but pattern: the system does what an experienced accountant who knows your chart of accounts does – tirelessly, on every invoice.
How accurate is it, really?
The honest answer: it depends on your history, and anyone promising a specific percentage without seeing your data is quoting an average, not you. The mechanics, though, are predictable. Recurring suppliers (rent, telecom, software licences) get matched quickly because the pattern is clear. New suppliers and atypical purchases go to a human at first, and every correction you make teaches the model. In practice accuracy is not a purchase-day feature but a growth curve: you review a lot in month one, less by month three, and with a stable purchasing pattern review becomes sampling. With about half of accountants already using AI, that curve has been walked in thousands of firms.
How are errors caught?
By three mechanisms that matter more than any accuracy percentage. Confidence thresholds: entries the system is unsure about do not post quietly; they come to you. Exception flagging: an amount that deviates from a supplier’s norm, or an account never used with that supplier, gets marked. And correction feedback: when you change a proposed coding you are not fixing one entry, you are teaching the rule. Ask any solution three things: can you set the confidence threshold yourself, do exceptions live in their own view, and do corrections train the model; a “no” anywhere means you are watching a demo, not a tool.
When should you trust it?
When the numbers say so: track for a couple of months what share of proposals goes through unedited. Once recurring suppliers run correct and untouched for weeks, raise their threshold to automatic and keep manual review on new suppliers and large amounts. Coding is the first step of a larger picture: how it composes with bank reconciliation and the rest of the routine is in our automation roadmap.
FAQ
Kuidas AI konteerimine töötab?
Masin loeb arvest struktuurandmed, pakub konteeringu sinu ajaloo mustrite põhjal (tarnija, reatekstid, summad) ja kindluse aste otsustab, kas kanne sünnib automaatselt või tuleb sinu ette.
Kui täpne AI konteerimine on?
Sõltub ajaloost: korduvad tarnijad tabatakse kiiresti, uued lähevad inimese ette. Täpsus on kasvukõver — iga parandus õpetab mudelit.
Kuidas AI konteerimise vead kinni püütakse?
Kindluse lävi (ebakindel kanne tuleb inimesele), erandite esiletõst (ebatavaline summa või konto saab märgise) ja paranduste tagasiside mudelisse.
Millal võib konteeringu automaatseks lülitada?
Kui jälgitud periood näitab, et korduvate tarnijate pakkumised lähevad läbi muutmata. Uutel ja suurtel summadel hoia inimese kinnitus.