Trigger
Chatbot: a person asks a question. Integrated: an event in the system — new email, new receipt, changed status.
Chatbot or integrated AI — the difference
Chat assistants are a good start: they make AI tangible and help with individual tasks. Their benefit, however, depends on someone asking the right question, checking the result and transferring it into the target system by hand. At high volume, the bottleneck remains the person at the window.
Integrated AI turns this around. The process calls the AI — event-driven, with data from the system, with results flowing straight back into the system. People only see the cases that need a decision.
Data
Chatbot: whatever is typed or uploaded. Integrated: the data already present in the process — complete and with context.
Result
Chatbot: text that has to be processed further. Integrated: structured data, field values, status changes, tasks.
Control
Chatbot: every answer checked individually. Integrated: rules, thresholds and spot checks — with a log.
Where integrated AI works in the company
The most effective places are handover points between systems and people — where today things are typed in, sorted, searched for or asked about.
- Inbox and receipt processing: Invoices, delivery notes and orders are read, checked and pre-entered in the ERP.
- Sales and quoting: Enquiries are qualified, draft quotes generated from master data and history, the CRM is kept up to date.
- Service and support: Tickets are classified, prioritised and enriched with solution proposals from the knowledge base.
- Production and maintenance: Fault reports are assigned to assets, maintenance orders prepared, documentation retrieved.
- Reporting: Key figures are consolidated, commented and distributed on schedule.
How integrated AI is built technically
Integrated AI is integration work: events from ERP, CRM, DMS or mail system are intercepted, enriched with context data, handed to the appropriate model and the result written back via interfaces. In between are rules that define what runs through automatically, what is presented for review and what is logged.
The model itself is interchangeable. What matters are data flows, interfaces and guardrails — and that the solution is monitored in operation.
- Connection via existing APIs, connectors or middleware; no isolated solutions
- Model choice per step: frontier providers, European providers or your own models on premise
- Guardrails: thresholds, four-eyes rules, blocklists, logging
- Monitoring of quality, cost and runtimes in regular operation
People stay in the process — in the right place
Integrated AI does not take responsibility away from employees, only routine. Cases with low confidence, high value or legal weight are presented for review; all others run through. Where the line is drawn is defined per process with the business units and adjusted via KPIs. The process stays traceable — for employees, auditors and data protection.
Benefits that can be measured
Because integrated AI works in defined processes with known volume, its benefit can be calculated in advance and verified afterwards: handling time per case, lead time, error rate, share of automatically completed cases. With the Sellium TCO model we set these effects against the running costs for models, operation and maintenance — per use case, before investing.