Time Optimization with ServiceNow Agent Intelligence
ServiceNow agent intelligence is an artificial intelligence solution built on the ServiceNow platform which helps businesses in improving productivity and eliminating challenges. The solution not only saves business cost and time but also leads to high customer satisfaction.
Agent Intelligence (AI) solutions deliver predictions based on the input by using machine learning algorithms. With the help of these algorithms, various patterns and predictions can be made by AI without human intervention. Based on the unique characteristics of each customer, what is categorized, routed, and assigned by using ServiceNow agent intelligence. Since it is an automated process, resources and processes are optimized while improving customer satisfaction and ensuring focus on strategic high-value activities.
Benefits of Agent intelligence
- Agent intelligence eliminates all sorts of technical challenges along with human errors and improves productivity results in less downtime.
- As a result of a few errors, the overall productivity of the team increases gradually and the automation process results in less downtime.
- Automating manual tasks saves time and resources and also helps in achieving great end-customer satisfaction.
Agent Intelligence Preparation
Data Quality: You need to ensure that the data collected for Artificial Intelligence must be clean to achieve better outputs.
Key Performance Indicators: Key performance indicators also known as KPIs are an important part of ServiceNow. From improving KPIs and average resolution time to increasing customer satisfaction factors, agent Intelligence can also help in opening for re-assigning accounts and other KPIs prior to beginning implementation.
Inputs and Outputs: The categorization and request assignment is done by default with agent intelligence. Additional inputs to the solution definitions need to be added to determine the correct category and assignment group.
Forecast Organisational Change: Organizational change plan needs to be created and implemented before implementing agent intelligence to make sure that the changes worked with the decision-makers.
Agent Intelligence Process
The process includes the selection of tables to implement AI predictions and gathering a minimum of 30,000 records for that specific table in order to train the solution. A maximum of 5 input fields can be given at a time to make predictions. The prediction of a single output field like category, assignment group, or priority can be selected. Re-training of the solution can be specified as daily, weekly, bi-weekly, or monthly. The solutions in the respective instance can be implemented once the training is complete.
Solutions statistics dashboard determines Precision and coverage for each class and in case it does not, a new solution definition filter can be used to identify the class that requires configuration. The coverage and distribution for each category of active solutions are listed on the solution statistics dashboard.