How Agent Intelligence Automates and Optimizes the Issue Resolution Process

Intelligent automation is one of the most sought-after tech trends in 2020. Companies are relying on intelligent automation to eliminate roadblocks in service delivery and resolution processes in order to offer faster throughput and improved user satisfaction. One solution is Agent Intelligence from ServiceNow, which is powered by Artificial Intelligence and Machine Learning abilities.

Automation Challenges that Agent Intelligence Solves

Here are a few major challenges driving organizations to adopt agent intelligence:

  • Unstable and inadequate managed service outages
  • Error-prone categorization, routing, assignment, and tracking of issues due to manual processes
  • Inaccurate prediction of the performance of services causing missed KPI targets
  • Inability to precisely plot and keep up with the industry standards. Resulting in wrong solutions and direction
  • No Multi-platform support for customers makes it harder for customers to get help where they need it

Prerequisites to Agent Intelligence Adoption

  • Make sure the data that’s gathered is high quality
  • Understand business requirements and then define KPIs
  • Add continuously add relevant and valid inputs to the solution definitions to improve AI overtime
  • Assess the organizational change, prior to agent intelligence adoption to check if these changes work with decision-makers

How ServiceNow Agent Intelligence Fosters Intelligent Automation

ServiceNow agent intelligence is a part of an intelligent automation engine that brings AI-driven functional competencies within all ServiceNow applications to ensure more personalized experiences to customers. This application uses machine learning algorithms to create, customize and train agent intelligence applications as per the organizational priorities and requirements.  ServiceNow agent intelligence provides a set of predefined templates that are ready to use with minimal customizations.

Machine learning abilities study user interaction patterns to make predictions and provide tailored experiences. Agent intelligence implementation happens in two stages. The first stage is choosing the right category and assigning a respective human agent. The second stage is to identify the problem and recommend the right solution. The agent intelligence application makes use of machine learning algorithms to understand user intent and simplify task categorizing, routing, assignment, and prioritization of issues. This way the ServiceNow agent intelligence module intelligently automates the task resolution process by decreasing the number of user interactions.

ServiceNow Agent Intelligence Working Process

Agent Intelligence working processFigure: Agent Intelligence classification Framework

Agent intelligence is available for incident management and customer service management modules. Based on business needs, users can develop and train predictive models with user data. To train the agent intelligence module, a framework classifies current and past user records. This framework is used to develop a database that will be used to compare and relate records based on trained models. Here is the step by step process for configuring ServiceNow agent intelligence:

  1. Identify training data sets
  2. Identify input and output data fields
  3. Create a solution definition
  4. Submit a training request
  5. Process the training request
  6. Create a solution
  7. Review the solution
  8. If no changes are required, implement the solution
  9. If changes are required, modify the definition and submit training request again

Tags: , , , ,