Exploring Issue Auto Resolution for HR

Issue Auto Resolution for HR enables employees to use request channels and response channels for communication and collects feedback from them to improve the quality of the recommendations.

Issue Auto Resolution for HR makes asking and receiving help easy for all employees. Office workers, with access to channels can ask help via a desktop or a laptop. Non-office workers, with limited access to computers, can ask help through a mobile phone. Issue Auto Resolution for HR provides a full self-service path toward resolving employee requests across a broad array of channels using AI and automation.

Request and response channels

Employees can request help or create a case using request channels like email or Employee Portal.

Employees receive communication from Issue Auto Resolution through response channels. If an HR case is evaluated as non-critical, and self-service content is available, Issue Auto Resolution sends a suggestion through the employee's preferred response channel. Employees can receive assistance through channels like email, SMS, Employee Portal, Microsoft Teams, or Virtual Agent (web). Providing diverse response channels enables employees to receive communication from Issue Auto Resolution through their preferred communication medium.

How Issue Auto Resolution works

After a general inquiry is created, Issue Auto Resolution uses Artificial Intelligence (AI), and Natural Language Understanding (NLU) to interpret the tone and intent of the case. AI analyzes the description of the HR case and identifies its criticality. Critical cases are flagged to a live agent for timely assistance. For non-critical cases, IAR responds with relevant knowledge articles and catalog items for reference. During the process of case resolution, employees can request agent assistance at any given point.

Understanding feedback

After reviewing the recommended knowledge articles or catalog items, an employee can choose to indicate whether the knowledge articles, and catalog items are helpful in resolving the inquiry. If the recommendations are helpful, the case is auto-resolved without any manual agent intervention. Otherwise, the case is moved to an agent's queue for further assistance.

Critical cases are brought to an agent’s attention and the assigned agent can give feedback on the prediction. The feedback can be used for training the machine-learning models so that the application gets smarter with time.