Forecast the risk of a change request failing by using machine learning to generate a
score from 0 through 100. Use these data-driven scores to replace subjective assessments with a
consistent process that improves IT stability.
Required Editions
Available in: Lightning Experience
Available in: Enterprise, and Unlimited Editions with Einstein for IT
Services add-on, and AI Accelerator for IT Services add-on.
From the App Launcher, find and select Change Requests.
From the Change Requests page, open a record.
In a change request record, navigate to the Change Risk Score card in the side panel.
The Change Risk Score card displays the probability that a change request will
fail.
Review the predicted score to determine the risk level. For example, a score of 33.35
indicates a moderate risk of change request failure.
In the Top Predictors list, identify the factors influencing the score. Common predictors
include:
Subject and Description: Keywords like password or rejecting that correlate with
historical patterns.
Priority and Urgency: The impact of related incidents or current urgency levels on the
final score.
Historical Trends: Factors such as the percentage of past incidents breached by the
current owner.
Use the feedback icons to help refine the machine learning model:
Select the Thumbs Up icon if the prediction is accurate.
Select the Thumbs Down icon if the prediction is incorrect.
Example:
Example
Sarah, a Change Manager at Cumulus Bank, oversees critical updates to banking applications.
When a database schema migration for real-time transactions is scheduled, a change request is
created with these details.
Change Request Number: CHG-4055
Subject: Database Schema Migration for Real-Time Transactions
Category: Database
Sarah opens the record and sees the Change Risk Score card with a score of 72.45. This score
indicates a high probability of failure for the planned migration.
Sarah reviews the risk factors identified by machine learning. Predictive AI identifies that
the Subject and Description fields contain keywords such as Migration and Schema, which
historically correlate with higher failure rates. Additionally, the historical trends predictor
shows that an emergency change has higher likelihood of a failed change.
To maintain IT stability, Sarah postpones the migration by 24 hours for sandbox testing and
assigns an expert reviewer. Using data analysis from the Change Risk Score, Sarah follows a
consistent process that prevents a service outage.
Hjalp denne artikkelen med å løse problemet ditt?
La oss få vite det slik at vi kan forbedre!
Laster
Salesforce Help | Article
Cookie Consent Manager
Cookie Consent Manager
General Information
Required Cookies
Functional Cookies
Advertising Cookies
General Information
We use three kinds of cookies on our websites: required, functional, and advertising. You can choose whether functional and advertising cookies apply. Click on the different cookie categories to find out more about each category and to change the default settings.
Privacy Statement
Required Cookies
Always Active
Required cookies are necessary for basic website functionality. Some examples include: session cookies needed to transmit the website, authentication cookies, and security cookies.
Functional Cookies
Functional cookies enhance functions, performance, and services on the website. Some examples include: cookies used to analyze site traffic, cookies used for market research, and cookies used to display advertising that is not directed to a particular individual.
Advertising Cookies
Advertising cookies track activity across websites in order to understand a viewer’s interests, and direct them specific marketing. Some examples include: cookies used for remarketing, or interest-based advertising.