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17.06.2024

Machine Learning in ServiceNow® for incident category automation

The landscape of ServiceNow® is rapidly transforming, thanks to the integration of artificial intelligence (AI) and machine learning (ML). ServiceNow® is at the forefront of this innovation with its predictive intelligence capabilities. These technologies are not just streamlining processes but also providing precision in incident categorization that was once unattainable.

Predictive intelligence in ServiceNow®

Predictive intelligence in ServiceNow® employs machine learning to categorize and route incidents automatically. By analyzing historical incident data, the system identifies patterns and correlations that would be impossible for a human to spot in a reasonable timeframe. This allows for the automated categorization of incidents into groups such as 'hardware,' 'software,' 'network,' and 'database,' enhancing the efficiency of the incident management process.

Visualizing use case success

The solution statistics and solution visualization components in ServiceNow® provide a clear representation of the efficacy of these machine learning solutions. As shown in this use case where incidents are categorized using short description field, the estimated solution precision and estimated solution recall metrics exhibit high levels of accuracy. For instance, the categorization precision for 'hardware' incidents stands at 99.46%, with a recall of 98.93%. This level of detail not only underscores the system's reliability but also the immense potential for continued optimization.

A compelling example of machine learning's practical application within ServiceNow® is its integration into UI actions, where predictive models are employed to anticipate user needs. For instance, consider a UI action that pre-populates fields based on historical data trends and user behaviour. When an IT professional begins logging a new incident, the ML model can predict and fill in the 'category' or 'subcategory' fields, based on the text entered in the 'short description'. This not only saves time but also ensures consistency in incident categorization. By minimizing manual input, this intelligent prediction facilitates a more efficient and user-friendly experience. It allows IT teams to respond more swiftly to incidents, ensuring that no time is lost in administrative procedures, which ultimately contributes to a faster resolution time and a better service level performance.

Solution Statistics in ServiceNow®

The figure showcases the performance of ServiceNow®’s machine learning model for incident categorization. On the x-axis, we have the estimated recall, which reflects the model’s ability to identify all relevant cases for each category. The y-axis shows the estimated precision, indicating the accuracy of the model’s categorizations.

The size of the bubbles represents the distribution of incidents in each category, with ‘inquiry’ having a significant presence at 62.64%. ServiceNow® advises prioritizing categories with over 50% distribution for automation, making ‘inquiry’ a key focus. For optimal categorization, ServiceNow® recommends aiming for precision above 70% and coverage above 80%, typically found in the upper right quadrant where precision and recall are both high. This quadrant indicates the most reliable performance for automating incident categorization with the model.

Performance of ServiceNow®’s machine learning model

Enhancing incident management

By automating incident categorization, organizations are seeing a decrease in resolution times and an increase in the satisfaction of both IT personnel and end-users. With machine learning, incidents are routed to the correct resolution team more quickly, reducing the backlog and allowing IT staff to focus on solving problems rather than on administrative categorization tasks.

Looking ahead

The application of machine learning in ServiceNow® is a dynamic and evolving field. As ServiceNow® continues to refine its predictive intelligence capabilities, we can expect even greater advancements in how incidents are handled. This includes not only categorization but also predictive analytics for incident prevention, personalized service delivery, and proactive problem management.

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