AI and automation in accounting and document work: where to start so it pays off and stays safe
Accounting and document work involves a great many repetitive steps, such as keying in data from invoices, matching incoming payments to receivables, or sending documents to management for approval. This work is well suited to AI and automation, but starting in the wrong place can leave you with a more complicated system that saves little time. This article sets out a way to start that is worthwhile and safe.
What kind of work suits automation
- It is repeated often and in high volume
- It follows clear rules, or there is enough sample data for the system to learn from
- It is easy to check whether the results are right or wrong
- It currently takes up so much staff time that it crowds out more valuable work
Examples in accounting and document work
- Reading data from documents: the system reads invoices and receipts and creates draft entries with the document number, date, amount and tax, for staff to check before posting
- Bank reconciliation: statement lines are matched automatically with invoices or payments, leaving only the items that do not match for a person to check
- Approval workflows: documents are sent automatically to the approver for the amount concerned, with a record of who approved them and when
- Receivables follow-up: reminders when a payment is nearly due or overdue
- Report summaries: help with drafting explanations of changes in the figures in management reports, for the person responsible to check and edit before sending
How to start so that it pays off
- Pick one process first: start with the most painful task, where the results can be clearly measured.
- Measure where you are now: record the time taken, the number of documents and the error rate before you begin.
- Run a pilot: use it on part of the documents first and compare the results with the old way of working.
- Always have a person review at first: scale back the review only once accuracy has been shown to be at an acceptable level.
- Expand step by step: once the first process works, move on to the next one.
Risks to control
- Accuracy: AI output can be wrong, so there must be a review step and clear criteria for which items need a human decision
- Data leaks: do not put customer data, personal data or confidential financial data into public tools that have no data protection agreement
- Access rights: automated systems should have only the rights they need, for example able to create draft entries but not to approve payments themselves
- Audit trail: every item the system creates or changes should be logged so that it can be checked later
- Legal compliance: any processing of personal data must comply with the personal data protection law
How to measure results
- Time taken for the monthly close
- Number of documents processed per person per day
- Error rate found in reviews or audits
- Cost per document, including system costs and review time
Frequently asked questions
Will AI replace accountants?
AI can greatly reduce data entry and duplicated work, but professional judgement, the interpretation of accounting standards and tax law, and responsibility for accuracy remain human tasks. The accountant's role is therefore shifting towards analysis and control.
Can a small business start using it?
Yes. Many of these tools are already built into the online accounting software you use, such as reading receipts or matching bank transactions. Start with these features before investing in a dedicated system.
What data do we need to prepare before starting?
A clear chart of accounts, clean master data for customers and suppliers, and sample documents covering the formats you see most often. Automation can only be as accurate as the underlying data.
Let us help you bring in automation
Kerwin & Vance brings AI and automation into working processes only where they genuinely reduce duplicated work and improve accuracy. Find out more about our Technology and Accounting & Reporting services, or book a consultation with us.