Configuring intelligent service prediction in incidents with BMC Helix classifier

Service desk managers can leverage AI to predict the services likely to be disrupted by incoming incidents for quicker resolution. AI analyzes historical, closed incidents to predict the service disruptions. The service prediction capability uses the Helix classifier model to predict the service disruption caused by the incoming incidents.

The benefits of using this capability are:

Increased efficiency of the service management team
Faster incident resolution
Lesser effort spent in identifying the service impact of incidents

Scenario

Carl, a Service Desk Agent at Apex Global, is handling a high volume of incoming incidents. He receives the LP1234 crashes incident with a vague description. He wants to know the service likely to be disrupted due to the incident so that he can assign it to the appropriate support team for quicker resolution.

He clicks the button under the Service field. Optionally, he clicks the button of the Assigned group, support group and the categorization fields as well. He then saves the incident.
BMC Helix ITSM analyzes the incident description and compares it with similar, closed incidents of the past to predict the services likely to be impacted by the LP1234 crashes incident.

Alternatively, Carl can save the incident without clicking the button for the Service and other fields. The relevant service, category details, and support group are assigned to the incident, and an activity note is added.

Workflow for service prediction

Before you begin

Make sure that you have the Cognitive Service Config permission. For information about this permission, see Permissions-to-configure-the-cognitive-service.
Make sure that you understand the types of training data and the guidelines to follow while generating the data.
Obtain the URL and credentials required to configure the cognitive service.

To configure intelligent service prediction in incidents with BMC Helix classifier

To configure the CCS parameters

Log in to Mid Tier.
Select AR System Administration > AR System Administration Console > System > General > Centralized Configuration.
From the Component Name list, select com.bmc.arsys.server.shared > *.
Set the value of the following CCS parameters:
- Classification-Service-Provider CCS parameter as HELIX.
  Use this parameter to select the provider of the classification service.
- Cognitive-Service-Confidence-Threshold CCS parameter as 0.0.
- Jdbc-Row-Limit CCS parameter as 200000.
  This ensures that there are sufficient closed incidents to train the model. Training the model on a high number of closed incidents may improve the resolution and category prediction.
Click Apply.

To configure the cognitive service connection settings

Log in to Mid Tier
Navigate to Application Administration Console > Custom Configuration > Foundation > Advanced Options > Cognitive Service Configuration.
On the Cognitive Data Setup form, click Cognitive Service Setup.
On the Cognitive Service Connection Settings form, enter the field values for User Name, Password, and Cognitive Service URL.
The URL and credentials are provided to the admin user.
In Enable Cognitive Service, select Yes.
The value that you select updates the Enable-Cognitive-Service CCS parameter.
After you save the settings, click Test Connection to check whether the cognitive service is configured on your system.
- If cognitive service is successfully configured, the following message is displayed:
  Connection Successful (ARNOTE 10000)
- If the system encounters problems while configuring the service, an error is displayed.
(Optional) If the system fails to configure the service due to an error, check the error details in the arextension.log file in the ARSysteminstalldirectory\Arserver\Db location and re-enter the correct values on the form.

To generate training data to train the algorithm on closed incidents of the past

Log in to Mid Tier.
Navigate to Application Administration Console > Custom Configuration > Foundation > Advanced Options > Cognitive Service Configuration.
From the Company list, select the company for which you want to enable the capability.
From the Application list, select Incident Management.
From the Template list, select Incident Service CI Training Data.
Click Add Selected Template.
Select the added template and click Generate Training Data.

The AI algorithm analyzes closed incidents from the selected company in BMC Helix ITSM, and considers them for model training.

If the training data generation fails, see Troubleshooting incident requests.

Recommendation

Each support group must have at least 100 incidents to train the model such that BMC Helix ITSM can predict service, categories, and support groups of new incidents.
We recommend setting the Jdbc-Row-Limit CCS parameter as 200000. This ensures that there are sufficient closed incidents to train the model. Training the model on a high number of closed incidents may improve the resolution and category prediction.
We recommend setting the Cognitive-Service-Confidence-Threshold CCS parameter as 0.0. This ensures that the Helix model can predict service, categories, and support groups of new incidents.
For improving the overall incident management, we recommend you train the models of the following cognitive service capabilities:
- Service prediction
- Categorization prediction
- Support group prediction

Where to go from here

Creating an incident request