Traditional Automation
Traditional automation follows rules: If A, then B; if C, then D. This makes automation reliable and predictable, as long as the situation behaves as originally anticipated.
AI automates work 
that requires human interpretation
Rule-based automation for predictable work, AI where interpretation is needed.
Automation with AI
If the outcome differs from what was anticipated, interpretation becomes necessary. AI automates that interpretation. We want to go to point D. Given the situation, which route is the most likely?
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AI is no substitute for process improvement
AI automates interpretation. But interpretation does not always require AI.
The automation paradox shows that automation does not necessarily lead to less work. Exceptions pile up and require human interpretation. AI can automate that interpretation, but it is often wiser to improve the underlying process.
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Document Conversion
AI recognizes vendor-specific layouts, fields, and invoice lines in digital and scanned PDFs. The extracted data is provided along with reliability scores and then converted to the desired XML format according to predefined mappings. Fields that are inconsistent or uncertain are flagged for review.
Validation
AI compares data from invoices and orders with supplier, customer, product, and contract master data. In addition to performing precise checks, the system can also detect spelling variations, incomplete descriptions, and suspicious discrepancies. This ensures that potential errors are flagged without valid documents being unnecessarily rejected due to minor differences.
Invoice Coding
AI learns from historically approved entries which general ledger account, cost center, and other dimensions correspond to a supplier, description, or expense type. For new invoices, it suggests the most likely coding, including a reliability score. Corrections serve as new input for further improvement.
Matching
AI helps match the correct invoice lines, order lines, and receipts, even when descriptions or line formats differ. Predefined rules and tolerances then determine whether price or quantity discrepancies are acceptable. Only cases of doubt and exceptions are submitted to an employee for review.
Messaging
AI analyzes the content of emails and messages from other channels (such as Microsoft Teams) and identifies the subject, intent, relevant invoices, sentiment, and urgency. Messages are automatically categorized, linked to the appropriate file, and prioritized based on factors such as payment risk, deadline, or escalation. As a result, employees start with the messages that require immediate attention.
Segmentation
AI combines payment history, outstanding balances, over-limit amounts, credit information, and recent interactions to segment debtors into risk profiles. For example, the system predicts the likelihood and expected timing of payment. This allows for more targeted follow-up, payment arrangements, and communication tailored to each segment.
Payment Processing
AI matches incoming payments to open items by evaluating amounts, invoice numbers, payment references, customer data, and payment notices together. The system also makes a recommendation for bundled payments, deductions, or incomplete descriptions. Only payments with insufficient certainty remain for manual reconciliation.
Reporting
AI analyzes reporting data, identifies trends, anomalies, and root causes, and translates these into a clear explanation. This automatically generates, in addition to figures and dashboards, an initial explanatory note containing observations, possible explanations, and points for attention. The responsible employee reviews and refines the conclusions.
Intelligent automation in finance operations
Webinar | 7 oktober
Webinar | October 7 – Practical AI applications for Accounts Payable and Accounts Receivable: from invoice processing to cash application.
Best-of-breed technology
AI is built into our own cloud solutions and is complemented by specialized intelligent automation platforms such as UiPath.
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