Konkrete Ausgangslage
Typisch sind Teams, die täglich dieselben Status-, Termin-, Produkt- oder Servicefragen beantworten und dadurch kaum Zeit für komplexe Anliegen haben.
ConsultingServices.aiAI Consulting for SMEsUse Case in Detail
Customer Service & Virtual Assistant.
High call center volume, 80% repeat inquiries, and overload during peak times lead to frustration for both customers and your team. A multilingual virtual assistant (voice & chat) handles first-level inquiries 24/7 and integrates seamlessly into your existing systems.
When is this Use Case Worth It?
The Typical Bottleneck (Problem)
- Customers repeatedly ask the same questions via email, chat, and phone ("Where is my order?").
- During peak times (seasonal business), customers are on hold for extremely long periods.
- Satisfaction (NPS score) noticeably declines due to poor accessibility.
The Ideal AI Solution
An omnichannel AI assistant (web, WhatsApp, voice) captures first-level tickets, verifies identities, and accesses the backend via API to provide personalized responses (e.g., delivery status) in over 50 languages.
Less suitable if: You provide highly exclusive B2B support for very few accounts, where personal connection makes up 100% of the value.
Business Impact
Measurable Results in Support
20-40%Call Reduction
Routine questions do not reach human agents at all (Deflection Rate).
24/7Service Worldwide
Answers for customers in over 50 languages – without night shifts.
150%ROI (p.a.)
Cost savings with external service providers and call centers immediately offset the project costs.
The Solution in Practice
Omnichannel: From Customer to Agent
It doesn't matter which channel the customer inquires through. The process is always harmonized.
1
Omnichannel Access (Web, WhatsApp, Phone)
The virtual assistant is available as a web chat, a channel in the messaging service (WhatsApp), or as a voice agent in the phone system.
2
Intent Recognition (NL)
The language model recognizes the true intent behind the question, even with spelling mistakes or vague phrasing.
3
API Query or Handover
If the solution requires, for example, an order status, the AI queries this in the background (e.g., via Zendesk/Salesforce/Shopify API). If the bot does not know how to proceed, it escalates with context to a human agent.
Find Out What's Possible
Frequently Asked Questions
Typical Objections & Answers
"Doesn't that scare off customers?"
Poorly programmed "click-tree bots" scare off customers (Press 1 for...). Modern agents interact fluently, empathetically, and if in doubt, they transfer the conversation to a human within seconds. The customer will have their problem solved extremely quickly – and that makes them happy.
"What happens with errors and hallucinations?"
The bot operates not in "creative" but in RAG mode. We can strictly prohibit it from "making up" answers. If it does not know the information reliably from a validated system document, it always (!) transfers to a human agent. Compliance and brand image are safe.
Consulting for Customer Service
Do you have specific systems and complex data silos? Let's talk about secure integrations.
Book Potential ConsultationVertiefung
Ausgangslage, Wirtschaftlichkeit und Umsetzung.
Damit ein Use Case nicht nur interessant klingt, muss er in Prozessvolumen, Datenlage, Risiko und messbarer Wirkung übersetzt werden.
ROI-Logik
Eine realistische Rechnung startet mit Ticketvolumen, durchschnittlicher Bearbeitungszeit und Deflection-Rate. Wenn 30 Prozent der Standardanfragen sicher automatisiert werden, entsteht schnell ein spürbarer Zeit- und Servicehebel.
Umsetzung in 4 Schritten
- Top-50 Fragen und Eskalationsfälle sammeln.
- Wissensbasis, CRM und Ticketsystem anbinden.
- Antwortgrenzen, Tonalität und Übergabe an Menschen definieren.
- Pilot mit echten Tickets testen und Deflection, CSAT und Eskalationen messen.
Entscheidungsfragen vor dem Pilot
- Welche 20 Fragen kommen jede Woche wieder?
- Welche Antworten dürfen automatisiert erfolgen?
- Wann muss der Bot zwingend an einen Menschen übergeben?
ROI-Beispiel
Beispielrechnung: Standardtickets
Der manuelle Aufwand liegt rechnerisch bei ca. 108.000 EUR pro Jahr. Bei 35 Prozent Entlastung ergibt sich ein Potenzial von ca. 37.800 EUR pro Jahr.
Gezählt werden nur wiederkehrende Standardfälle. Komplexe Reklamationen, Eskalationen und sensible Fälle bleiben beim Team.Annahmen
- 1.500 Support-Anfragen pro Monat
- 8 Minuten Bearbeitungszeit pro Anfrage
- 45 EUR interne Vollkosten pro Stunde
- 35 Prozent sichere Automatisierung oder Antwortvorbereitung