Artificial Intelligence - Zia
Zia is a continuously learning AI assistant whom you can train to help automate various service desk tasks. Zia continuously learns from the ServiceDesk Plus data, and you can also track her learning. She inspects each incoming request and applies her learning to understand and respond to the user's needs. Currently, Zia can perform the following actions:
The following document will explain in detail how to configure, train, and employ Zia to automate your business processes. The following links take you to specific topics:
Supported OS: Zia is fully functional on Windows machines, Ubuntu 16, and CentOS 7 and higher versions.
Zia predictions are enabled by default under Admin > Zia Configurations > Configuration.
Validate Request Reopening - Zia evaluates end-user responses to completed requests and reopens only the requests that need further processing. This rule applies to requests in any Completed status, including default and custom statuses. Zia's intervention will help you avoid swamping your service desk with thank you notes, acknowledgment notifications, automated emails, out-of-office messages, and the like from end-users. Consequently, your technicians can focus on resolving more pressing requests within allocated service level agreements.
Assign/Suggest Category Prediction - Zia suggests an appropriate category for a request based on the content in the subject and description. This will help your end-users select the right category when they are creating new requests as well as reduce the workload of technicians who assign categories manually. For requests created using via API and Email, the suggested category will be automatically applied to the requests. Please note that Zia requires a minimum of 500 requests to get started with the training for this prediction. Zia will not suggest or override categories for requests under Business Rules, Field and Form Rules, and Request Life Cycle configuration.
Suggest Template Prediction - Zia suggests an appropriate template for a request using Zia's prediction abilities. This will help the end-users pick the right template for the request they are creating as well as reduce the workload of technicians who assign templates manually. Please note that Zia requires a minimum of 500 requests to get started with the training for this prediction. Zia will not suggest templates for requests under Business Rules, Filed and Form Rules, and Request Life Cycle configuration.
A sample Category and Template suggestion for a request by Zia is shown below
Request
Zia's Suggestion on Webform
If the end-user ignores Zia's suggestion during request creation, then Zia will recommend it again on the request details page for all the other users except the one who ignores it. If the request owner rejects the suggestion, then the suggestion will not be shown any longer.
Zia's Suggestion on the Details page
Zia requires a minimum of 500 to a maximum of 50000 request records to train herself for the prediction. If there are more records, Zia will take the latest 50000 records and then start the training. If the request count is below 500, then the prediction training will not start. Zia will wait for the request count to reach the 500 mark and then start the training. Once the training starts, the same will be notified to SDAdmins/technicians via in-product notification. Every night Zia will undergo training with the latest set of records.
Execute User's Approval Action - Zia applies users' approval decisions on requests, changes, purchase orders, and purchase requests. Zia scans the approver's response in the approval email and deduces the approver's decision.
Apart from the user's approval action in the Zia configurations page, you can set approval configurations from General Settings > Advanced Portal Settings > Approval.
Enrich the training data by manually adding your real-time data and help expand Zia's learning.
To add custom training data, click Customize Data displayed under the respective Zia prediction option.
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You can test Zia with keywords, phrases, or complete sentences that are related to request reopening. If Zia prediction is inaccurate or unavailable, you can manually add relevant information to the training data set. Locate the cursor in the data set panel of the Reopen and Retain Closure actions and type in contextual data. Press Enter after typing in each record.
You can also modify or delete the entries in the default training data set of Reopen and Retain Closure actions.
Similarly, you can test Zia with approval-related keywords or contextual statements to predict approval, reject, and need clarification actions. Need clarification prediction is applicable only to the Requests module. To test Zia, click Test Zia to Predict Results. Type the keyword and press Enter or click Test Zia.
If Zia's prediction is incorrect or unavailable, you can add relevant data to the training data set and train her. For accurate prediction, select the required language from the drop-down and add the custom data. After saving the data, Zia will immediately train itself with the new data.
You cannot modify or delete the default training data set of approval actions.
To revert the training data set to its original state, click Restore Default on the top right of the page.
Go to Admin > Zia > Zia Configurations.
Click Customize Data under Execute User's Approval Action.
Click Add New Data across the Approve, Reject, or Need Clarification columns.
Type the keyword or contextual data and click Submit.
Alternatively, you can train Zia from user feedback.
Click Zia on the footer and go to Approvals, or click Zia on the request details page.
Click Dislike 
.
Select the required option.
Click Train Zia.
Technicians can evaluate (upvote/downvote) each of Zia's predictions and mark whether it is correct in Zia Notifications 
displayed at the right corner of the footer. Request reopening and approval notifications will be listed in different tabs as shown below:
Besides tracking and evaluating Zia's predictions under Zia Notifications, you can do so from the respective request, change, purchase request, or purchase order details page.
This feedback will be used to re-equip Zia's training data and enable Zia to make informed decisions in the future. Zia trains herself with the data submitted through user feedback once every 15 minutes.
User evaluation of Zia's action will be recorded in the corresponding entity's history.
Technicians with general scope, such as Site and Edition criteria and edit permission for the corresponding module (Requests/Purchase) can evaluate Zia's actions. However, users can evaluate Change-related Zia's actions only if they have edit permission for the corresponding change.
Besides re-equipping Zia's training data, user feedback helps in determining Zia's accuracy rate, which in turn allows you to track her learning. Zia's accuracy is computed every day at 11:55 PM and updated on the Zia Configurations page under Accuracy Rate.
You can track all configurations related to Zia under History on the Zia Configurations page.
