How ServiceNow Predictive Intelligence Enhances Service Delivery: Explained

ServiceNow has always been at the forefront of innovation. By understanding how artificial intelligence and its subsets can transform various business areas, ServiceNow added machine learning capabilities to the platform in the form of Predictive Intelligence. ServiceNow Predictive Intelligence leverages the power of AI in applications like Information Technology Service Management (ITSM), HR Service Delivery (HRSD), Customer Service Management, and Event Management to further enhance service delivery efficiencies.

What is ServiceNow Predictive Intelligence?

ServiceNow Predictive Intelligence (formerly known as Agent Intelligence) uses machine learning solutions. It provides a layer of artificial intelligence that makes predictions and recommends related records based on user inputs. As a subset of artificial intelligence, machine learning uses statistical techniques to enable the ServiceNow platform to learn from a massive amount of enterprise data.

With the help of predictive intelligence, ServiceNow can categorize, route, and assign work based on user input. The solutions are prepared by taking thousands of historical records and using them to create new solutions. Predictive intelligence improves process accuracy and decreases the resolution time. This allows businesses to gain ServiceNow Predictive Intelligence insights that improve decision-making across departments.

Benefits of ServiceNow Predictive Intelligence

Let us now check the top 4 benefits of ServiceNow Predictive Intelligence.

1. Enhances Business Efficiency

ServiceNow predictive intelligence enables businesses with the flexibility to train predictive models with machine learning solutions that are oriented to your business objectives. This way, businesses get reliable information which allows the organization to exceed expectations and increase customer satisfaction.

To further explore AI-driven efficiency in IT operations, check out our blog on empowering IT operations with ServiceNow AIOps.

2. Avoids Human Error

According to ServiceNow research, “43% of IT service desk respondents have more than 100 different assignment groups to choose from, and nearly a quarter face a choice from more than 300 groups.” This complexity with manual processes leads to errors and therefore it is time-consuming. Predictive intelligence automates service desk processes with more accuracy in less time while providing insights from the data it collects. This is impactful, particularly in ServiceNow Predictive intelligence for incident management.

3. Provides Simple and Insightful Visualizations

With interactive and insightful reports, dashboards, and SLAs, Predictive intelligence provides better insights into various IT processes and performance, all with easy-to-understand visuals that make it easy to make the right business decision.

4. Automates Routing and Categorizing

Automatically categorizes and routes work based on past data, handling high volumes of incoming requests at lower costs. This highlights the strength of ServiceNow Predictive intelligence classification models.

McKinsey reports AI can automate 60–70% of work activities, freeing employees for higher-value tasks.

Now, let us look at the ServiceNow Predictive Intelligence features.

ServiceNow Predictive Intelligence Features

Here is a brief overview of the key ServiceNow Predictive Intelligence features:

• Intelligent Categorization and Routing: It automatically allocates the tickets to the required teams by using trained machine learning models.
• Language Detection and Translation: Using ServiceNow Predictive Intelligence language processing, it can identify language and interpret content across different geographies.
• Clustering and Similarity Matching: Utilizes natural language understanding to group incidents that are exactly similar.
• Custom ML Model Training: Train models by considering your enterprise data and applying them to specific use cases.
• Performance Dashboards: Real-time insights and accurate reporting.

ServiceNow Predictive Intelligence Roles and Prerequisites

To use ServiceNow Predictive Intelligence effectively, a few roles and configurations to the platform are necessary.

Here are the required roles:

• ml_admin: It grants administrative access to manage ML models and training solutions.
• ml_report_user: Allows users to generate and view predictive intelligence reports and dashboards.

Prerequisites

To start using the Predictive Intelligence ServiceNow plugin on the Now Platform, you must follow these steps.

• Activate the predictive intelligence (glide.platform_ml) plugin and its dependent plugin (com.glide.platform_ml_pa) for reporting.
• Verify activation by confirming that the system has successfully created the sharedservice.worker user.
• Maintain a minimum of 10,000 records to train high-quality predictive models for accurate recommendations and reliable ServiceNow Predictive Intelligence classification.

How ServiceNow Predictive Intelligence Works

ServiceNow Predictive Intelligence uses machine learning to assess the operational data and give predictions within the platform. It helps automate tasks, improve decision-making, and optimize workflows across different ServiceNow applications such as ITSM, HR Service Delivery, and Customer Service Management.

Here is how ServiceNow Predictive Intelligence works:

1. Data Collection: It begins with gathering of huge volume of structured and unstructured data from various sources within the ServiceNow platform. It can be from incident records, change requests, and historical tickets. Gathering this data gives a foundation for finding recurring patterns and meaningful correlations. After this, when unstructured data is cleaned, the resultant structured data is clean and significantly improves model performance.
2. Model Training: Using at least 10,000 records, the system trains a predictive model to spot patterns based on its past behavior. This learning process involves adjusting model weights and evaluating accuracy against real-world results.
3. Prediction Application: Once trained, the model can now categorize incidents and assign tickets automatically. This completely eliminates manual decision-making, which will ensure consistency and improve operational speed across Information Technology Service Management and other modules.
4. Monitoring & Evaluation: Ongoing monitoring will make sure predictions continue to remain precise with time as data evolves. Models can be trained again from time to time so that they adapt to new business trends.

With this seamless workflow, organizations can reduce resolution time and improve user experience, making the ServiceNow Predictive Intelligence cost a valuable investment for digital transformation.

ServiceNow Predictive Intelligence Frameworks

The following 3 main frameworks are required for predictive intelligence.

1. Predictive Intelligence Classification Framework

The Predictive Intelligence Classification Framework is supervised learning which uses the machine learning algorithm to set a field value during record creation, such as setting an incident category based on a short description. By training the models, it automatically categorizes and routes the work based on previous records. And, the minimum number of records needed in the classification framework is ten thousand.

This framework’s role is to reduce the resolution time and ensure consistent categorization. It is useful especially when implementing ServiceNow Predictive Intelligence for incident management.

2. Predictive Intelligence Similarity Framework

The Predictive Intelligence Similarity Framework provides a vocabulary that compares the new incident record with the previous records. To do this, it uses a “word corpus.” The word corpus is a collection of words and text that predictive intelligence uses to learn similarities to help the agent find similar records.

This framework helps agents by offering recommendations based on related historical incidents. This reduces the time spent on searching and increasing first-contact resolution rates. It enhances ServiceNow Predictive Intelligence insights by learning from context.

3. Predictive Intelligence Clustering Framework

The Predictive Intelligence Clustering Framework groups similar records into clusters by addressing them collectively. This helps to quickly detect and discover major incidents related to historical performance.

Clustering helps organizations monitor incident patterns, identify all the issues, and mitigate service disruptions proactively. This predictive model supports smarter root-cause analysis and preventive maintenance.

New Predictive Intelligence Features in ServiceNow Orlando

The latest ServiceNow Orlando release comes with some interesting updates for the predictive intelligence module.

• In the ServiceNow New York release, English was the only processing language. But the Orlando release has international languages such as Brazilian Portuguese, Dutch, English, French, German, Italian, Japanese, and Spanish.
• A customized stopwatch was added to improve the capability to categorize, compare, and create clusters.
• Configuring Team Frequency-Inverse Document Frequency (TF-IDF) has been encoded for classification and similar solutions.
• Configuring the target metrics with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) can be configured to improve the precision in the clustering solution.
• The updated API framework allows users to incorporate machine learning, which finds records faster based on similar words.

These enhancements contribute to both the efficiency and adaptability of ServiceNow Predictive Intelligence in global enterprise environments.

To explore how generative AI is shaping future innovations in ServiceNow, check out our blog on harnessing the power of generative AI and the latest trends in ServiceNow.

ServiceNow Predictive Intelligence Use Cases

ServiceNow Predictive Intelligence use cases span across IT, HR, and customer service functions.

1. IT Incident Management: Assign and prioritize tickets automatically. This results in reduced response times and timely issue resolution by the right teams.
2. Change Management: Predict impact based on past changes. Identifying trends in previous changes helps teams to reduce risk and plan effectively.
3. HR Case Management: Route the questions for the HR department to appropriate agents. This ensures a timely and accurate response to every employee, resulting in improved satisfaction.
4. Customer Service: Provide intelligent responses through similarity matching. This helps to improve agent efficiency by suggesting resolutions based on the issues resolved in the past.

It is clearly understood that, in each of the scenarios discussed above, using ServiceNow Predictive Intelligence insights has led to fast resolutions and better user experience.

For use cases related to conversational AI, you might also be interested in our guide on the ServiceNow Virtual Agent: how it works, benefits, and use cases.

ServiceNow Predictive Intelligence Best Practices

Make the most out of ServiceNow Predictive Intelligence by following these best practices:

• Use High-Quality Training Data: Make sure you utilize only clean, structured, and relevant historical data. The more organized the data, the more accurate and relevant the model predictions will be.

• Monitor Models Continuously: Evaluate the performance of the model constantly and provide more training when needed. This helps maintain accuracy because your business needs to evolve from time to time.
• Start Small and Scale Fast: Start with one use case initially. Once you see the results, you can expand quickly. Moreover, phased rollouts will significantly reduce the associated risks.
• Activate Necessary Plugins: Important plugins like the predictive intelligence ServiceNow plugin and its dependencies must be enabled always. This is necessary for accessing full functionality and integration across the platform.
• Collaborate Across Teams: Collaboration isessential to achieve maximum value. Therefore, involve your IT team, data science, and also process owners. Cross-functional collaboration ensures that models align with real-world workflows and user needs.

Conclusion

Overall, it is clear that ServiceNow Predictive Intelligence is transforming how businesses manage, route, and resolve service requests with speed and accuracy. By leveraging the predictive intelligence frameworks, organizations can streamline workflows easily and even reduce operational friction. Its powerful features are undeniably a strategic asset for IT, HR, and customer service teams.

Additionally, when the best practices are followed perfectly, businesses can unlock deeper insights and improve service outcomes. Are you ready to embrace predictive intelligence today? If yes, you are ready to set the stage for smarter, more agile service delivery tomorrow.

FAQs

1. How does Predictive Intelligence differ from traditional automation in ServiceNow?

   Traditional automation follows pre-defined rules while predictive intelligence uses machine learning to learn from past data and make predictions intelligently. This helps in making smarter decisions over time.

2. Is the predictive intelligence plugin free in ServiceNow?

   Depending on your ServiceNow plan, sometimes, the plugin may need a license.

3. Which ServiceNow modules support Predictive Intelligence?

   ServiceNow Predictive Intelligence is supported in modules like IT Service Management (ITSM), HR Service Delivery, Customer Service Management, and Event Management to enhance process efficiency.

4. Can ServiceNow Predictive Intelligence support multiple languages?

   Yes, language support is included for classification and similarity solutions but is limited to English for clustering Solutions at this time. Some of the languages that are supported for classification and similarity solutions include: Brazilian Portuguese, Dutch, English, French, German, Italian, Japanese, and Spanish.

5. How accurate are the predictions made by ServiceNow Predictive Intelligence?

   It depends on the volume and quality of the training data, model tuning, and ongoing monitoring. Dashboards provide confidence scores for each prediction.

6. How does ServiceNow Predictive Intelligence improve incident categorization and routing?

   By assessing all the past incident data, it automatically assigns incoming tickets to the right teams. This eliminates manual errors and also speeds up resolution times.

7. Are there compliance concerns with using predictive intelligence?

   While predictive intelligence adheres to ServiceNow’s platform security standards, organizations must ensure that their data governance and compliance policies are followed when using machine learning models.