Nexora looks at this topic through the business process: what is happening now, where the loss occurs, how the solution is designed, and what result should be measured after implementation.
The topic
Customer service has become one of the main directions of AI automation. In State of Service, Salesforce reports that service teams estimate the share of cases already handled by AI at roughly 30% and expect 50% by 2027. The main risks remain the same: accuracy, security, and trust.
Microsoft cites the mobilezone case: after deploying agents built on Copilot Studio, the company handles more than 1,600 chats a month, and the resolution time for internal IT requests fell by 50%.
These figures show the potential, but they also confirm that AI works well when it is built into the process, has access to current data, and knows when to hand a request to a person.
How Nexora fits in
Nexora can combine the form, email, messengers, CRM, knowledge base, and AI into a single service workflow. But we do not treat AI as a replacement for all support.
For simple and safe requests the system can answer automatically. For complex, financial, conflict-prone, or low-confidence requests there must be an escalation path.
Nexora's task is to build the whole cycle: registering the request, classification, finding context, the reply, updating the CRM, SLA, and quality control.
How we see it
The main mistake is to automate only the front end: put in a chatbot and consider the project done. If the knowledge base is outdated, the CRM has no context, and the order status lives in another system, the AI will quickly give incomplete or wrong answers.
We treat service as a set of risk levels. FAQ, status, a standard instruction, and basic routing can be automatic. Returns, legal claims, large B2B clients, and low-confidence situations need a person.
The feedback loop matters too: every request should improve the knowledge base, the classification, and the understanding of why people get in touch.
How we solve it
- We register the request automatically and link it to the customer.
- We classify intent, priority, and language.
- We look for the answer only in approved sources.
- We allow an automatic reply for predefined categories.
- We hand cases with low confidence, risk, or negative sentiment to a person.
- We update the case in the CRM and control the SLA.
- We collect analytics on reasons for contact, resolution time, and handoff rate.
What improvement to expect
The main metrics after implementation are first response time, resolution time, the share of automatic resolutions without a repeat contact, handoff rate, the share of overdue SLAs, and the cost of handling one case.
Salesforce expects the share of AI-resolved cases to grow from 30% in 2025 to 50% in 2027. mobilezone has already reported a 50% cut in IT resolution time. These figures show the direction of the market, but the actual share of automation depends on the type of requests and the acceptable risk.
For Nexora a good result is when AI takes over the repetitive load and employees get more time for complex requests and important clients. If automation increased the number of repeat contacts or errors, it cannot be considered successful.
