A contact center, system, and method of operating a contact center are provided. In one example, the system includes a quantum computing resource and a server. The server is configured to receive a contact from a customer communication device, provide information about the contact to the quantum computing resource, receive a probabilistic output from the quantum computing resource based on the quantum computing resource processing the information about the contact, and make a work assignment decision for the contact based on the probabilistic output.

A device that is configured to assign users to an issue cluster based on issue types for the users. The device is further configured to identify available agents and to assign each available agent to one or more knowledge area clusters based on knowledge scores. A knowledge score indicates an expertise level for an agent in a knowledge area. The device is further configured to identify an issue cluster that is associated with an issue type and to identify a user from the issue cluster. The device is further configured to identify a knowledge area cluster that is associated with the issue type and to identify an agent from the knowledge area cluster. The device is further configured to establish a network connection between a user device associated with the user and a user device associated with the agent.

Described herein is a system and method for capturing data associated with actions attempted by an automated agent. The system described herein captures data associated with the actions attempted by an automated agent during the messaging session between an automated agent and the user and present a summary of the actions in a messaging platform. In an embodiment, the automated agent uploads data associated with actions attempted during the messaging session to a server. The server captures the data associated with the actions and generates a description of each action that was attempted. The server generates a summary including the description of each action. The summary of the actions are rendered in the messaging platform.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for automating actions based on ranked work events. A sequence of events are tracked which occur in software services accessed by a user, tracking events from each case handled by the user. Focus events are determined which identify which case is being worked on by the user at points in time. The determination is made using information extracted from user interactions with at least one service, where each focus event has a focus event duration. Each focus event is assigned to a particular case. A total period of time spent by the user on the particular case is determined. Work actions of the users are ranked. The ranking includes receiving an indication of reviewer intent for ranking the work actions, generating a set of work actions, and prioritizing the set of work actions.

This document discusses, among other things, limiting contact to a networked contact center that is a host to multiple tenants. In an example embodiment, a communication is received by a networked contact center. In determining whether to allow the communication to reach contact center resources, a contact rate value may be examined. The contact rate value may represent an allowable quantity of contact within a time interval. For some example embodiments, a determination of whether the network contact center is to accept or reject the communication is made based on the contact rate value.

In the present disclosure, analytics are applied to work items while the work items are waiting in a work queue in order to optimize the routing and allocation of work items to agents in the most efficient manner possible, while optimizing agents being assigned to work items they are most qualified to handle. By performing a look ahead at more than the initial work item, the system assesses the agent skills required by imminent work items in the work queue. This is then compared to a skillset of each available and/or soon to be available agent in order to achieve the optimal allocation of the work items to maximize the work item being assigned the best qualified agent. The work items are then routed to the agents accordingly.

Disclosed embodiments may include a system that may receive an indication that a user is accessing an ATM, receive, from the ATM, average session duration data over a predetermined period, generate, using a machine learning model, a busyness score for the ATM based on the average session duration data over the predetermined period, and determine whether the busyness score for the ATM exceeds a busyness score threshold. When the busyness score for the ATM does not exceed the busyness score threshold, the system may cause the ATM to present, via a first graphical user interface, a default ATM experience. When the busyness score for the ATM exceeds the busyness score threshold, the system may cause the ATM to present via, a second graphical user interface, a busy ATM experience.

A method of routing interactions to contact center agents according to an embodiment includes identifying an interaction to be routed to a contact center agent, identifying a group of contact center agents as candidates for routing of the interaction, retrieving agent performance data for each candidate agent of the group of contact center agents identified as candidates for routing of the interaction, determining a predicted score for a key performance indicator for each candidate agent based on the agent performance data, determining an occupancy rate of each candidate agent based on the agent performance data, generating a ranking of the candidate agents for routing prioritization based on the predicted score for the key performance indicator for each candidate agent and the occupancy rate of each candidate agent, and signaling a routing device to route the interaction to a selected candidate agent based on the ranking of the candidate agents.

Contact centers often simultaneously utilize two or more entities for making routing decisions to match a caller with an agent. The entities utilize discrete methodologies, such as algorithmic versus artificial intelligence (AI) based decision making. Algorithmic decision-making components provide robustness and ensuring the very best match, of all possible matches, is made. AI-based decision making provides a decision based on learning/training and is often faster. The decisions reached are expected to be identical. However, the management and data reporting from two very different systems can produce errors and increase overhead. Accordingly, a management component is provided to harmonize the inputs and outputs of these disparate systems to comport to a single standard.

Methods to reduce agent effort and improve customer experience quality through artificial intelligence. The Agent Assist tool provides contact centers with an innovative tool designed to reduce agent effort, improve quality and reduce costs by minimizing search and data entry tasks The Agent Assist tool is natively built and fully unified within the agent interface while keeping all data internally protected from third-party sharing.