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ServiceNow Generative AI Use Cases 2024

ServiceNow Generative AI is in demand, and so are its developers and administrators. I am presenting use cases related to ServiceNow Generative AI use cases which are important from an interview point of view and to get real-time understanding.

Please review these ServiceNow Generative AI Use Cases and give your best in the interview. Happy Learning.

Extras:- ServiceNow CMDB Use Cases, ITSM Scenarios

Case Summarization

  • Example: An IT technician receives a user ticket describing a slow computer with multiple error messages. Generative AI analyzes the description and system logs, then creates a concise summary: “Critical system process (svchost.exe) consuming excessive CPU resources. Potential causes: malware infection or corrupted system files.” This summary helps the technician diagnose the issue quickly and efficiently.


  • Example: A business analyst needs a workflow to reset user passwords after failed login attempts automatically. Instead of writing complex scripts, they describe the desired functionality in natural language: “Trigger workflow on failed login attempt exceeding 3 tries. Reset the user password and send a notification to IT security team.” Generative AI translates this into the necessary ServiceNow workflow code.

Incident Resolution Assistance

  • Example: A network engineer is troubleshooting a network outage. Generative AI analyzes the incident details (symptoms, affected users) and suggests relevant knowledge base articles that helped resolve similar outages in the past, along with configuration items (switches, routers) that might be causing the issue.

Root Cause Analysis

  • Example: The IT service desk experiences a spike in printer-related incidents every Monday morning. Generative AI analyzes historical data and identifies a correlation between these incidents and scheduled server maintenance on Monday mornings. This helps pinpoint the root cause (printer drivers getting overwritten during maintenance) and implement a fix.

Automated Workflows

  • Example: A server overheating issue typically requires manual intervention to reboot it. Generative AI can create a self-healing workflow that automatically monitors server temperature. If it exceeds a threshold, the workflow triggers a server reboot, minimizing downtime and potential data loss.

Agent Chat Assistance

  • Example: A customer service agent chats with a customer frustrated with a slow internet connection. Generative AI suggests a knowledge base article on troubleshooting internet connectivity issues and a canned response offering to escalate the issue to a technical support specialist if the self-help steps don’t resolve it.

Ticket Classification and Routing

  • Example: A customer submits a ticket with the subject line “Can’t access my account.” Generative AI analyzes the content and identifies keywords suggesting a login issue. It then automatically categorizes the ticket as a “Login Problem” and routes it to the appropriate queue for faster resolution.

Personalized Self-Service Portal

  • Example: A customer recently purchased a new laptop. When they log in to the self-service portal, Generative AI recommends articles and FAQs related to setting up their new device, downloading software, and troubleshooting common laptop issues.

Automated Customer Satisfaction Surveys

  • Example: After a customer interacts with a live chat agent, Generative AI automatically sends a brief survey asking about their satisfaction with the interaction. Analyzing this feedback helps identify areas where the customer service team can improve.

Sentiment Analysis for Customer Feedback

  • Example: A customer emails customer service expressing frustration with a recent product purchase. Generative AI analyzes the email content and identifies negative sentiment. It flags the email for priority attention, allowing the customer service team to quickly address the customer’s concerns.

Onboarding Automation

  • Example: A new employee joins the marketing department. Generative AI creates a personalized onboarding checklist that includes tasks like setting up their computer account, attending mandatory HR training sessions, and familiarizing themselves with marketing department tools and workflows.

Employee Self-Service Knowledge Base

  • Example: An employee has a question about their health insurance plan. They access the HR knowledge base, where Generative AI has populated and maintains articles on various benefit plans, enrollment procedures, and claim submission processes.

Leave Request Analysis

  • Example: The HR department notices a surge in leave requests during the upcoming holiday season. Generative AI analyzes historical leave data and predicts potential staffing shortages in certain departments. This allows HR to proactively take steps like hiring temporary staff or encouraging employees to stagger their leave requests.

Automated Performance Review Generation

  • Example: A manager needs to complete performance reviews for their team members. Generative AI analyzes employee data like goals achieved, project contributions, and peer feedback. Based on this information, it generates draft performance reviews that the manager can then tailor with specific examples.

Skills Gap Analysis

  • Example: A company is planning to implement a new marketing automation platform. Generative AI analyzes employee skills against the required skills for using the platform and identifies a gap in marketing automation expertise. This helps HR recommend training programs to bridge the gap.

Security Incident Response Playbooks

  • Example: A security analyst discovers a phishing attempt targeting company employees. Generative AI automatically generates a response playbook based on pre-defined security policies. This playbook outlines steps for containing the attack, mitigating damage, and notifying relevant stakeholders.

Threat Detection and Prioritization

  • Example: A security information and event management (SIEM) system generates a large number of security alerts daily. Generative AI analyzes these alerts, identifying potential threats based on predefined rules and threat intelligence feeds. It then prioritizes the alerts based on severity and risk score, allowing security analysts to focus on the most critical threats first.

Security Incident Reporting

  • Example: After a security incident is contained, security analysts need to document the details for compliance purposes. Generative AI can automatically generate a comprehensive security incident report, including information about the attack type, affected systems, remediation steps taken, and lessons learned.

Vulnerability Management Automation

  • Example: Security scans regularly identify vulnerabilities in IT systems. Generative AI can automate the process of prioritizing these vulnerabilities based on risk and asset criticality. It can also recommend appropriate remediation actions, such as applying security patches or isolating vulnerable systems.

Security Awareness Training Content Generation

  • Example: An organization is rolling out a new security awareness training program for employees. Generative AI can personalize the training content based on employee roles and past training results. For example, employees who handle sensitive customer data might receive more in-depth training on phishing attacks and data security best practices.

Service Catalog Optimization

  • Example: The IT service catalogue contains hundreds of services, some of which are rarely used. Generative AI analyzes service catalogue usage data and identifies underutilized or redundant services. It can then recommend removing these services from the catalogue or consolidating them into more comprehensive offerings.

Workflow Efficiency Analysis

  • Example: A complex ServiceNow workflow is taking an unusually long time to complete. Generative AI analyzes the workflow execution data and identifies bottlenecks or inefficiencies. It can then suggest ways to streamline the workflow, such as eliminating unnecessary steps or automating manual tasks.

Predictive Resource Allocation

  • Example: An organization is expecting a surge in customer support tickets during the holiday season. Generative AI analyzes historical data and current trends to predict future resource needs, such as IT staff or storage capacity. This allows the organization to proactively allocate resources to ensure they can handle the increased demand.

Automated Report Generation

  • Example: Managers across different departments need regular reports on key performance indicators (KPIs) related to their areas of responsibility. Generative AI can automatically generate these reports from various ServiceNow modules, providing valuable insights into areas like incident resolution times, customer satisfaction levels, and employee onboarding efficiency.

Personalized User Interface

  • Example: A new IT service desk analyst logs in to ServiceNow for the first time. Generative AI personalizes the user interface by highlighting the features and functionalities most relevant to their role, such as incident management tools and knowledge base articles. This helps new users navigate the platform more easily and become productive quickly.

Dynamic Service Catalog Personalization

  • Leverage generative AI to personalize the service catalog for each user based on their role, past requests, and recent activity. For instance, a network engineer might see network-related services prominently displayed, while an HR representative might see employee onboarding workflows highlighted.
  • Example: John, a software developer, logs into the ServiceNow portal. Generative AI tailors the service catalog view to showcase frequently used development tools, deployment workflows, and access request options.

AI-powered Root Cause Analysis with Impact Prediction

  • Combine generative AI with machine learning to analyze historical incident data and not only identify root causes but also predict the potential impact of similar incidents on business operations. This allows for more strategic mitigation strategies.
  • Example: Generative AI analyzes a recent database server crash and identifies a software bug as the root cause. It further predicts potential downtime and data loss if the issue isn’t addressed promptly.

Automated Patch Management Optimization

  • Utilize generative AI to optimize patch management processes by analyzing system configurations, vulnerability reports, and dependencies. This can help prioritize patches based on risk and compatibility, reducing downtime and ensuring efficient deployment.
  • Example: Generative AI identifies a critical security patch for a specific operating system version. It analyzes the IT infrastructure and recommends the most suitable deployment strategy to minimize disruption and maximize security.

Incident Resolution Knowledge Base Curation

  • Train generative AI to curate and maintain the knowledge base by automatically updating articles with new solutions and insights gleaned from resolved incidents. This ensures the knowledge base remains accurate and up-to-date.
  • Example: After a network connectivity issue is resolved, generative AI automatically updates the relevant knowledge base article with the troubleshooting steps taken and the root cause identified. This helps future technicians resolve similar issues efficiently.

Generative AI-powered Self-Healing Workflows

  • Develop self-healing workflows using generative AI that can automatically diagnose and remediate common IT issues. This reduces reliance on manual intervention and ensures faster resolution times.
  • Example: A server monitoring workflow powered by generative AI detects a CPU overload. It automatically triggers actions such as scaling resources or restarting services to restore optimal performance.

Omnichannel Customer Journey Orchestration

  • Leverage generative AI to orchestrate a seamless customer journey across various channels (phone, email, chat) by understanding customer intent and routing them to the most appropriate resources.
  • Example: A customer frustrated with a delayed order contacts support via live chat. Generative AI analyzes the chat transcript and identifies the issue. It then routes the customer to a specialized agent equipped to handle order tracking inquiries and potentially offers a discount coupon to apologize for the inconvenience.

Sentiment-Aware Chatbot Escalation

  • Implement chatbots powered by generative AI that not only provide basic support but also escalate complex issues or highly negative customer sentiment to human agents for more personalized intervention.
  • Example: During a chat interaction, a customer expresses extreme dissatisfaction with a product. The chatbot recognizes the negative sentiment and offers to connect the customer with a live support representative for a more in-depth resolution.

Predictive Customer Churn Analysis

  • Analyze customer data with generative AI to predict churn risk and suggest proactive measures to retain valuable customers. This could involve targeted communication, loyalty programs, or personalized offers.
  • Example: Generative AI identifies a customer who recently made fewer purchases and shows signs of potential churn. The system recommends a personalized email campaign offering exclusive discounts or loyalty points to incentivize continued engagement.

Automated Customer Satisfaction Surveys with Dynamic Questionnaires

  • Design surveys with generative AI that adapts to customer interactions and personalizes questions based on the specific issue or topic. This ensures more relevant feedback for continuous service improvement.
  • Example: After a customer utilizes the self-service portal to resolve a password reset issue, a generative AI-powered survey appears, asking targeted questions about the ease of use and effectiveness of the self-service experience.

Automated Knowledge Base Enrichment from Customer Interactions

  •  Train generative AI to analyze customer support interactions (emails, chat transcripts) and extract relevant information to automatically enrich the knowledge base with new solutions and FAQs.
  • Example: A customer raises a question about a new product feature. Generative AI analyzes the interaction and the provided answer from the support agent. It then incorporates this information into the knowledge base as a new FAQ entry for future reference.

Generative AI-driven Interview Candidate Matching

  • Utilize generative AI to analyze job descriptions, candidate resumes, and past interview data to recommend the most suitable candidates for open positions. This streamlines the recruitment process and improves hiring decisions.
  • Example: The HR department is hiring for a software developer role. Generative AI analyzes the job description, highlighting the required skills and experience. It then analyzes a pool of candidate resumes, identifying those with the most relevant qualifications and suggesting them for interviews.

Dynamic Onboarding Experience Personalization

  • Personalize the onboarding experience using generative AI to tailor tasks, resources, and communication based on the new employee’s role, department, and previous experience. This ensures a smoother and more efficient onboarding process.
  • Example: A new sales representative joins the company. Generative AI creates a personalized onboarding plan that includes video tutorials on using the company’s CRM system, introductions to key colleagues, and role-specific training modules.

Generative AI-powered Performance Feedback Optimization

  • Develop generative AI algorithms to analyze employee performance data and suggest specific, actionable feedback points for performance reviews. This can reduce manager bias and improve the quality of feedback.
  • Example: Generative AI analyzes a sales representative’s performance data, including quota attainment, customer satisfaction ratings, and activity logs. It suggests specific feedback points on areas for improvement, such as closing more deals or enhancing customer communication.

Automated Skills Gap Identification and Learning Path Recommendations

  • Utilize generative AI to analyze employee skills and experience against current and future job requirements. It can then recommend personalized learning paths to bridge identified skill gaps.
  • Example: Generative AI identifies that a group of marketing specialists needs to develop expertise in a new social media marketing platform. It recommends relevant online courses and training materials to help them acquire the necessary skills.

Adaptive Threat Detection and Response

  • Develop generative AI-powered systems that can learn and adapt to evolving cyber threats in real-time. This allows for more proactive detection and response capabilities to mitigate security risks.
  • Example: A generative AI system detects a new phishing campaign targeting company employees. It analyzes the email content and identifies previously unseen patterns. The system then dynamically updates security filters to block similar phishing attempts in the future.

Generative AI-driven Security Incident Forensics

  • Leverage generative AI to analyze vast amounts of security data from different sources (network logs, endpoint logs, user activity) to reconstruct the timeline and scope of security incidents more efficiently.
  • Example: After a security breach, generative AI analyzes logs from various systems to determine the attack vector, affected systems, and the extent of data exfiltration. This information is crucial for containment, remediation, and incident response planning.

Generative Deception Technology

  • Implement generative AI to create dynamic lures that mimic real IT infrastructure and data. This can attract cyber attackers and provide valuable insights into their tactics and techniques, allowing for more effective defence strategies.
  • Example: Generative AI creates a decoy server that appears to be a critical database system. When attackers attempt to access the decoy server, their actions are monitored and analyzed by the security team, revealing their attack methodologies.

Automated Security Awareness Training Personalization

  • Personalize security awareness training using generative AI to cater to individual employee roles, past training results, and susceptibility factors (e.g., frequent clicks on suspicious links). This improves the effectiveness of security training programs.
  • Example: Generative AI identifies that an employee frequently opens emails from unknown senders. The system tailors security awareness training content for that employee, focusing on recognizing and avoiding phishing attacks.

Generative AI-powered Anomaly Detection in ServiceNow Logs

  • Utilize generative AI to analyze ServiceNow logs and identify unusual activity patterns that might indicate potential performance issues, configuration errors, or security threats. This allows for proactive troubleshooting and incident prevention.
  • Example: Generative AI identifies a surge in login attempts from a specific IP address. The system recognizes this as a potential brute-force attack and triggers an alert for the security team to investigate.

Generative AI-driven ServiceNow Data Governance

  • Leverage generative AI to ensure data accuracy, consistency, and compliance within ServiceNow by identifying and automatically correcting data inconsistencies.
  • Example: Generative AI scans ServiceNow data and identifies instances where the same asset has conflicting information in different fields. It then suggests potential corrections based on predefined data governance rules.

Intelligent Process Automation with Generative AI

  • Combine generative AI with robotic process automation (RPA) to automate complex workflows within ServiceNow. Generative AI can handle tasks like data extraction, decision-making based on context, and dynamic content generation, while RPA executes repetitive tasks.
  • Example: An approval process for purchase requests involves reviewing vendor quotes, checking budgets, and generating purchase orders. Generative AI analyzes quotes and automatically extracts key information. RPA then verifies budget availability and generates purchase orders based on approvals.

Generative AI-powered Chatbot Integration for Platform Support

  • Develop chatbots powered by generative AI that can answer user questions about using ServiceNow features, troubleshoot common issues, and escalate complex problems to human support representatives.
  • Example: A new IT technician encounters difficulty configuring a workflow in ServiceNow. They interact with a chatbot that provides step-by-step instructions and relevant documentation links. If the issue persists, the chatbot seamlessly connects the technician with a live support agent.

Predictive Resource Allocation with Generative AI and Machine Learning

  • Combine generative AI with machine learning algorithms to analyze historical data and predict future resource needs (storage, servers, software licenses) within ServiceNow. This enables proactive resource allocation and cost optimization.
  • Example: Generative AI analyzes historical trends in service desk ticket volume and predicts a surge in activity during the holiday season. It suggests scaling up IT resources to ensure efficient performance during this peak period.

Generative AI-driven ServiceNow User Interface Optimization

  • Utilize generative AI to optimize the ServiceNow user interface based on user roles, past activity, and preferences. This can involve highlighting relevant features, hiding unnecessary options, and personalizing dashboards for a more efficient user experience.
  • Example: A manager in the marketing department logs into ServiceNow. Generative AI tailors the interface to showcase marketing-specific tools and reports, such as campaign management dashboards and analytics tools.

Generative AI for ServiceNow Data Visualization and Insights

  • Leverage generative AI to analyze vast amounts of data within ServiceNow and create dynamic, interactive visualizations that provide actionable insights for decision-making. This could involve identifying trends, patterns, and correlations across different data sets.
  • Example: Generative AI creates a visual dashboard displaying key performance indicators (KPIs) for IT service desk performance. The dashboard dynamically updates to reflect changes in incident resolution times, customer satisfaction ratings, and agent workload. By analyzing these insights, management can identify areas for improvement and optimize service delivery.

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