Agent intelligence is an artificial intelligence solution built on the ServiceNow platform. ServiceNow Agent intelligence will help your business to improve productivity and eliminates bottlenecks, which in-turn leads to high customer satisfaction. Explore in detail how ServiceNow agent intelligence works and saves your business costs and time.
Agent Intelligence Overview
Almost three-quarters of CIOs surveyed (72 percent) are leading digitization efforts, and more than half (53 percent) say machine learning is a focus."
AI solutions use machine learning algorithms to deliver predictions based on user input. With the help of machine learning algorithms, AI will understand patterns and predict intelligent outcomes without human intervention.
With the help of agent intelligence ServiceNow will categorize, route, and assign work based on the unique characteristics of each customer. The AI solutions will be prepared by looking at thousands of historical records (i.e., incidents, cases, requests), based on the newly prepared solution the correct categorization, priority, and routing information is applied, decreasing the time it takes to resolve a task. This automation helps to optimize resources and processes, improve customer satisfaction, and ensure energies are focused on innovation and strategic high-value activities.
Benefits of Agent Intelligence
- While working with the manual process, there will be so many errors and bottlenecks will get created. With the help of AI, we can eliminate the bottlenecks and can decrease the error rates and improves productivity results in less downtime.
- With fewer error rates, the productivity of IT teams will improve gradually. With the automation process end, users will get results in less downtime.
- We can achieve great end user/customer satisfaction with AI.
- Automating manual tasks will greatly save time and resources.
No specific roles are required to work with agent intelligence application. Users with the admin role can perform/work with this application.
- Customer needs to purchase this application from ServiceNow to enjoy the agent intelligence application services.
- ServiceNow system administrator needs to clone the sub production instance to implement Agent intelligence to test whether this application is running fine or not.
- Need to maintain minimum 30 thousand records in sub production instance in which you want to implement the application to prepare/predict accurate solution.
AI Benchmark Against Peers
- Data Quality: Gather and make sure the data which will be used for AI is as clean as possible.
- KPIs: KPI’s are major in ServiceNow. With the help of AI, we can improve our KPIs, average resolution time, customer satisfaction factors-open/re-assignment counts and other KPIs, prior to beginning implementation.
- Inputs and Outputs: Agent Intelligence is by default categorizes and assign requests. If you want to provide more information to determine the correct category and assignment group (i.e. Location), make sure to add respective additional inputs to the solution definitions.
- Forecast Organizational Change: It is important to estimate the organizational change. Before implementing agent intelligence, we need to create and implement an organizational change plan so as to check if these changes work with the decision makers.
- Need to select the table to implement AI predictions.
- Need to gather min 30 thousand records of the respective table to train the solution.
- Can give a maximum of 5 input fields to make predictions.
- Only one output field can be selected. The output field is predicted, such as category, priority, or assignment group.
- Frequency, which specifies how often to re-train the solution i.e. (daily, weekly, bi-weekly, monthly, etc.)
- Once training is completed, we can implement the solutions in the respective instance to make it available for end users.
Figure: ServiceNow agent intelligent process. Source: ServiceNow Saba Cloud
Solution statistics dashboard will help us determine if a solution has enough precision and coverage for each class. If the class doesn’t have enough precision and coverage, then identify the class that requires configuration or retraining with a new solution definition filter. The solution statistics dashboard lists the coverage and distribution for every category of active solutions.
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