Element AI

Overview

Element AI, founded in 2016 and subsequently acquired by ServiceNow in 2020, functions as a set of artificial intelligence capabilities integrated within the ServiceNow platform. Its primary objective is to facilitate the adoption of AI within large enterprises, enabling the transformation of business operations through intelligent automation and data-driven insights. The platform is designed for organizations seeking to implement AI across various functions, including IT service management, customer service, human resources, and field operations, by embedding AI directly into existing workflows.

Element AI's approach centers on developing and deploying industry-specific AI solutions that address common enterprise challenges. This includes automating routine tasks, predicting potential issues, and personalizing user experiences. For instance, in IT service management, AI models can automatically categorize and route support tickets, recommend solutions, and identify patterns that indicate system failures before they occur. In customer service, AI agents can handle common inquiries, reducing response times and allowing human agents to focus on complex issues. The integration with ServiceNow's workflow engine aims to ensure that AI capabilities are not standalone tools but are interwoven into the fabric of daily business processes.

The platform is particularly suited for organizations with significant data volumes within the ServiceNow ecosystem that are looking to derive actionable intelligence from this data. It supports various AI paradigms, including machine learning for predictive analytics, natural language processing (NLP) for understanding unstructured text, and computer vision for image analysis, though its core strength lies in leveraging structured and semi-structured data from enterprise systems. Element AI aims to lower the barrier to AI implementation for enterprises by providing pre-built models and tools that can be customized and deployed without extensive data science expertise, focusing on practical business outcomes rather than solely on research and development.

Element AI's capabilities are leveraged within ServiceNow products like ServiceNow AI Search, which uses natural language processing to improve search relevance across enterprise data, and Virtual Agent, an AI-powered chatbot for automating interactions. The integration of Element AI's technology into the broader ServiceNow platform underscores a strategy to embed intelligence directly into operational workflows, aiming to enhance efficiency and decision-making across the enterprise. This approach aligns with industry trends emphasizing practical AI applications that deliver measurable business value, as noted by research from firms like Gartner on enterprise AI adoption, which highlights the importance of integrating AI into existing business processes.

Key features

• AI-powered workflow automation: Automates routine tasks and processes across IT, customer service, HR, and other business functions using machine learning and natural language processing.
• Predictive intelligence: Utilizes machine learning models to analyze historical data and predict future outcomes, such as potential system outages, customer churn, or service request volumes.
• Natural Language Processing (NLP): Processes and understands human language in text-based communications (e.g., emails, chat transcripts, support tickets) to extract intent, categorize information, and automate responses.
• Virtual Agent and chatbot capabilities: Provides conversational AI interfaces for users to interact with enterprise systems, resolve issues, and get information through self-service.
• Recommendation engines: Offers personalized recommendations for articles, solutions, or services based on user behavior and context.
• Anomaly detection: Identifies unusual patterns or deviations in data that may indicate critical issues, security threats, or performance degradation.
• Data ingestion and preparation: Tools for collecting, cleaning, and preparing enterprise data for use in AI model training and inference.
• Model management and monitoring: Capabilities for deploying, managing, and continuously monitoring the performance of AI models in production environments.
• Industry-specific solutions: Pre-built AI models and applications tailored for specific industries and use cases, such as IT Service Management (ITSM) optimization or customer experience enhancement.

Pricing

Element AI's capabilities are integrated into ServiceNow's broader product offerings. As such, direct standalone pricing for Element AI is not publicly available. Pricing for ServiceNow products that incorporate Element AI technology is typically structured as custom enterprise agreements, based on factors such as the specific modules deployed, the number of users, and the scope of implementation. Organizations interested in utilizing Element AI's features should engage directly with ServiceNow's sales team for a tailored quote.

For specific pricing inquiries, refer to the ServiceNow pricing page or contact their sales department.

Common integrations

As an integrated component of the ServiceNow platform, Element AI's capabilities are inherently linked with other ServiceNow modules and can extend to external systems through ServiceNow's extensive integration framework.

• ServiceNow IT Service Management (ITSM): Integrates to automate ticket routing, incident prediction, and provide self-service options via virtual agents. Learn more about ITSM capabilities on ServiceNow.
• ServiceNow Customer Service Management (CSM): Enhances customer experience through AI-powered case resolution, intelligent routing, and personalized support. See CSM documentation.
• ServiceNow HR Service Delivery (HRSD): Automates HR inquiries, provides self-service options, and streamlines HR workflows. Refer to HRSD documentation.
• ServiceNow Field Service Management (FSM): Optimizes scheduling, predicts equipment failures, and provides technicians with relevant information.
• Enterprise Data Warehouses/Lakes: Connects with systems like Snowflake or Databricks via ServiceNow's integration hub to ingest data for model training and analysis.
• Third-party communication platforms: Integrates with platforms like Microsoft Teams or Slack to deliver virtual agent interactions and notifications within common collaboration tools.
• CRM systems: Can be integrated with CRM platforms like Salesforce to unify customer data and enhance service interactions.

Alternatives

• IBM Watson: Offers a suite of AI services for enterprises, including natural language processing, speech-to-text, and machine learning, often used for custom AI application development.
• Microsoft Azure AI: Provides a comprehensive set of AI and machine learning services on the Azure cloud platform, catering to developers and data scientists building custom AI solutions.
• Google Cloud AI: Offers various AI and ML products, including Vertex AI for MLOps, pre-trained APIs, and custom model development tools on the Google Cloud infrastructure.
• DataRobot: Focuses on automated machine learning (AutoML) to accelerate the development and deployment of AI models for business users and data scientists.
• H2O.ai: Provides open-source and enterprise AI platforms for building, deploying, and managing machine learning models, with a strong emphasis on explainable AI.

Getting started

As Element AI is integrated into the ServiceNow platform, getting started typically involves configuring and utilizing its capabilities within existing ServiceNow instances. The primary method for interacting with Element AI's features is through the ServiceNow interface and its development tools.

A basic example of leveraging Element AI's predictive intelligence within ServiceNow involves configuring a classification model for IT incidents. This model can automatically assign categories and priority to incoming service tickets based on their description.

To begin, a ServiceNow administrator or developer would access the Predictive Intelligence workbench:

// This is a conceptual example demonstrating interaction with ServiceNow's Predictive Intelligence API
// In a real-world scenario, this would be configured via the ServiceNow UI or specific API calls.

// Step 1: Define a new Predictive Intelligence solution (e.g., for incident classification)
// This would typically involve selecting a table (e.g., 'incident'), a field to predict (e.g., 'category'),
// and input fields (e.g., 'short_description', 'description').

function createIncidentClassificationSolution(solutionName) {
  console.log(`Creating Predictive Intelligence solution: ${solutionName}`);
  // In ServiceNow, navigate to 'Predictive Intelligence' > 'Solutions' > 'New'
  // Configure: Table = 'incident', Predict = 'category', Input = 'short_description, description'
  // Set other parameters like language, training frequency.
  console.log("Solution definition complete in ServiceNow UI.");
}

// Step 2: Train the solution
// This process uses historical data from the 'incident' table to train the AI model.

function trainSolution(solutionName) {
  console.log(`Training solution: ${solutionName}`);
  // In ServiceNow, open the defined solution and click 'Train'.
  // The platform will process historical incident data to build the classification model.
  console.log("Training initiated. This may take time depending on data volume.");
}

// Step 3: Integrate the trained model into a business rule for incident creation
// This snippet demonstrates how a server-side business rule might call the trained model
// to classify new incidents automatically.

// Example of a server-side Business Rule script (on 'incident' table, 'before' insert)
// This code would run on the ServiceNow server.

// Pseudo-code for a ServiceNow Business Rule:
/*
(function executeRule(current, previous /*, gs, gr * /) {

  if (current.isNewRecord()) {
    var pi = new GlidePredictiveIntelligence();
    var solutionSysId = 'YOUR_CLASSIFICATION_SOLUTION_SYS_ID'; // Replace with actual solution ID

    // Prepare input for prediction
    var inputs = {};
    inputs.short_description = current.short_description.toString();
    inputs.description = current.description.toString();

    // Call the prediction service
    var prediction = pi.classify(solutionSysId, inputs);

    if (prediction && prediction.prediction) {
      // Apply the predicted category and confidence score
      current.category = prediction.prediction; // Set the predicted category
      current.setValue('u_predicted_category_confidence', prediction.confidence); // Store confidence
      gs.info('Incident ' + current.number + ' predicted category: ' + prediction.prediction + ' with confidence: ' + prediction.confidence);
    } else {
      gs.warn('Failed to get prediction for incident ' + current.number);
    }
  }

})(current, previous);
*/

// Main execution flow
const mySolutionName = "Incident Category Classifier";
createIncidentClassificationSolution(mySolutionName);
// After creating, manually train in UI.
// Then, deploy the business rule as shown in the pseudo-code above.

console.log("Configuration complete. New incidents will now be automatically classified.");

This example illustrates how Element AI's predictive capabilities, specifically for classification, can be integrated into a ServiceNow workflow. The actual implementation occurs within the ServiceNow platform's administrative and development interfaces, where solutions are defined, trained, and deployed via business rules or flow designer actions. For detailed steps, refer to the ServiceNow Predictive Intelligence documentation.