Where does AI create value in ERP?
We do not replace verifiable finance, inventory and costing rules with AI. We use it where uncertainty can be governed: document classification, description extraction, forecasting, anomaly detection, natural-language reporting, service prioritisation and user assistance.
Use cases
- Classify invoices, orders, service forms and emails
- Extract structured fields from bank descriptions and free text
- Detect inventory, purchasing, price and payment anomalies
- Support demand, maintenance and capacity forecasts
- Answer reporting questions with explainable summaries
- Recommend the next step in a process
Governance and security
Every scenario defines data sources, access, personal data, model access, output validation and retention. If an agent executes a transaction, threshold, approval, reversal and audit trail are mandatory. AI must not expose data the user cannot see in ERP.
Measurement and acceptance
A demo is not production proof. Accuracy, false positives, missed records, latency, cost and human intervention are measured on real data. Below the acceptance threshold, the system falls back to suggestion mode or stops safely.
Frequently asked questions
Can AI post accounting entries automatically?
It may be technically possible, but we do not recommend autonomous financial execution without human approval, limits, reversal and an audit trail.
Is our data used for model training?
That depends on the selected service and contract. Processing, retention, region and training policy must be verified in writing.
How is success measured?
Each scenario has a baseline and acceptance threshold; we track operational time, error cost and human intervention alongside accuracy.