ServiceNow extends “smart automation” through machine learning

With the help of an intelligent automation engine, ServiceNow not only hopes to expand the field of IT service management through machine learning, but also hopes to provide new employees with customer support and entry procedures.

With the help of an intelligent automation engine, ServiceNow is further expanding its service strategy. This machine learning-based solution is mainly used to automate processes and problems in IT organizations.

“In the IT field, we usually find a high degree of automation,” Georg Goller, Vice President of ServiceNow Germany, said at a news conference in Munich last week. SaaS providers originated in IT service management, but as Goller explained, this is actually not the main goal. On the contrary, other areas such as human resource management or customer service should also be automated with the help of Now Platform.

With the help of the ServiceNow intelligent automation engine, users should be able to prevent IT failures and automatically classify and distribute incidents. Then, the task is automatically transferred to the responsible person. Performance predictions can also be made by comparing benchmark performance with similar organizations.

However, machine learning also aims to optimize other ServiceNow cloud services. Such as customer service, security and human resources. To this end, ServiceNow evaluates a large amount of context-sensitive operational data.

The algorithm can recognize patterns so that it can identify events before the failure. The solution also helps to create a workflow on how to avoid future problems.

ServiceNow realizes the intelligent classification and assignment of tasks by acquiring DxContinuum. The intelligent automation engine uses the old model for training. This is how to identify risks and responsibilities. First, this predictive intelligence is used in IT Service Management (ITSM) solutions to classify and route IT requests with high accuracy.

Determine the category of the request through the learned model, and then assign the task to the appropriate team. The solution also calculates the risks associated with team activity or negligence.

The new algorithm is also used for performance analysis. This allows users to define performance goals, and Performance Performance Analytics then determines the point in time at which the goal may be achieved based on the data profile.

For routine tasks such as resetting passwords or onboarding, multiple departments are usually involved. This creates friction losses. As shown in the ServiceNow research report “Today’s Work Status: At a Breaking Point”, 86% of companies believe that in order to be able to complete all tasks, automation will be needed in the next three years.

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