Predictive Intelligence in ServiceNow: A low/no code approach to ML

[Luvv Aggarwal2]

Predictive Intelligence in ServiceNow is an integrated layer of AI that improves various aspects of the platform. It employs machine learning (ML) to enhance functionalities across applications, leading to better work experiences. The system consists of four frameworks for creating ML solutions within your ServiceNow instance, each catering to different types of predictions and recommendations. These frameworks can be used in multiple languages, including English, French, German, Japanese, Dutch, Spanish, Italian, and Brazilian Portuguese.

Machine Learning and Predictive Intelligence Solutions

Machine learning is a subset of AI that enables computers to learn from data and experiences, making predictions and decisions. Predictive Intelligence provides ML solutions through several frameworks:

1. Classification Framework: Automatically sets field values during record creation. For instance, it predicts and sets incident categories based on descriptions. This improves record handling efficiency.

2. Regression Framework: Predicts numeric outputs using historical data. For instance, it estimates the time needed to resolve incidents. By training the model with past data, it can predict future numeric values.

3. Similarity Framework: Identifies similar records based on textual content. This helps recommend actions or resolutions. For example, it suggests solutions for incidents with similar descriptions, even considering synonyms and context.

4. Clustering Framework: Groups similar records for analysis or pattern identification. It can identify trends or major incidents by clustering similar occurrences.

Architecture of Predictive Intelligence

1. Solution Definition: Create data records with training details, including input and output fields, retraining frequency, and more.

2. Solution: Result of solution definition, with precision and coverage metrics for predictions. Solution classes represent possible output values.

3. Predictive Intelligence API: Out-of-the-box business rules use the API to make predictions and update solution statistics.

Use Cases in ServiceNow

1. Classification and Routing: Automatically classify tasks, cases, and incidents, streamlining routing and assignment processes.

2. Smart Recommendations: Assist agents with suggestions for efficient issue resolution, improving productivity.

3. Major Incident Detection: Identify similarities across issues to prioritize critical incidents promptly.

4. International Language Support: Offer insights and predictions in multiple languages for a diverse user base.

5. Regression Analysis: Predict accurate time to resolution for different issues, setting informed expectations.

6. Dashboards and Reporting: Combine AI insights with analytics for real-time reports, assessing improvements.

7. AI Routing: Automatically route requests to appropriate teams by extracting relevant information.

8. Sentiment Analysis: Handle issues based on user sentiment, addressing high-priority issues driven by negative sentiment.

9. Auto-Population: Automate routing to correct teams for quicker response times.

Capabilities and Enhancements

Capabilities:

• Versatile support for on-premises and cloud environments.
• Data privacy assurance through centralized training servers.
• Localized predictions on a dedicated prediction server.
• Comprehensive learning frameworks for various solution types i.e. supervised and unsupervised.
• Enhanced data inputs through database views.
• Insightful dashboard for reviewing trained models.
• Automatic model versioning for better flexibility.

Potential Enhancements:

• Data manipulation interface for improved data preparation.

By leveraging Predictive Intelligence's advantages and potential enhancements, ServiceNow professionals can optimize its functionalities for better decision-making, process automation, and user experiences.

I am also attaching a pdf that I have created as a revision for my certification.

I hope it would be helpful