Certified Generative AI in ITSM Training Course

• Fundamentals of Generative AI
• Types of Generative AI models
• Use cases of Generative AI in various industries

• Key ITSM processes
• ITSM best practices and frameworks
• Challenges and opportunities in ITSM

Service Desk:

• Gen AI-powered chatbots for 24/7 support and self-service
• Intelligent ticket routing and prioritization
• Automated knowledge base article generation and Curation
• Automate routine tasks, such as ticket updates and notifications
• Analyze user sentiment in tickets and conversations to identify potential escalations
• Provide personalized support and recommendations to improve user satisfaction

Incident Management:

• Employ Gen AI to accurately classify and categorize incidents based on their descriptions
• Identify patterns in incident data to automatically prioritize incidents based on their impact and urgency
• Leverage Gen AI to automatically suggest relevant knowledge articles or solutions
• Deploy Gen AI-powered chatbots to provide self-service support to end-users
• Leverage Gen AI to predict potential system failures or performance issues based on historical data.
• Integrate with collaboration platforms to facilitate seamless communication and knowledge-sharing

Problem Management:

• Analyze historical incident and problem data to uncover recurring patterns and trends
• Generate potential root cause hypotheses based on analyzed data and knowledge
• Generate temporary workarounds to mitigate the impact of problems
• Automate the creation of change requests to address the root cause of problems

Change Management:

• Simulate the impact of a change on the IT environment to identify potential conflicts or issues before implementation
• Generate standardized change request templates based on the type of change
• Automatically assess the potential impact of a change on other systems and services
• Analyze failed changes to identify patterns and root causes
• Leverage machine learning models to predict the likelihood of a change being successful
• Provide recommendations to change implementers based on historical data and best practices

Service Request Management:

• Utilize NLP to understand and interpret user requests submitted in natural language
• Identify the underlying intent of user requests
• Automate routine request fulfillment tasks
• Optimize resource allocation based on predicted demand and workload
• Generate personalized responses and updates to users

Configuration Management:

• Gen AI-driven configuration item discovery, dependency mapping, and real-time updates
• Automated configuration baseline management, change tracking, and version control
• Predictive configuration drift analysis including drift detection, impact analysis, and proactive remediation
• Gen AI-powered CMDB querying and reporting

IT Asset Management:

• Gen AI-powered asset discovery, asset classification, and inventory updates
• Predictive asset maintenance and lifecycle optimization & management
• Automated asset compliance and security audits for license management, vulnerability assessment, and policy enforcement
• Gen AI-driven prediction for future asset needs based on historical usage patterns and business requirements

Release Management:

• Gen AI-assisted release planning and scheduling including dependency analysis, resource allocation, and risk assessment
• Automate the deployment of software releases across different environments
• Implement automated rollback procedures in case of issues or failures during deployment
• Integrate AI with CI/CD pipelines to enable faster and more frequent releases
• Predictive release risk assessment and impact analysis

Deployment Management:

• Leverage GenAI to automate deployment workflows, identify and manage dependencies, and integrate with configuration management
• Real-time deployment monitoring and troubleshooting
• Analyze failed deployments, risk evaluations, and deployment optimization

Service Level Management:

• Gen AI-driven service level agreement (SLA) monitoring, automated reporting, and data visualization
• Predictive SLA breach analysis and prevention
• AI-powered performance analysis, resource optimization, and SLA negotiation support

Capacity Management:

• Leverage Gen AI to analyze historical usage patterns, business trends, and other relevant factors
• Build and simulate capacity models to predict the impact of different scenarios
• Predictive capacity bottleneck analysis and remediation
• Gen AI-powered capacity optimization recommendations

Availability Management:

• Gen AI-driven availability monitoring for collecting and analyzing potential availability issues and reporting
• Utilizing ML models for anomaly detection and generating timely alerts and notifications to relevant stakeholders
• Gen AI-powered recommended adjustments to allocate resources and optimize infrastructure design for availability and performance

Demand Management:

• Gen AI-driven demand forecasting and analysis to accurately predict future demand for IT services
• Predictive demand pattern identification and management to prevent service disruptions, optimize resource utilization, and improve user satisfaction
• AI-powered demand optimization recommendations

Information Security Management:

• Gen AI-powered threat detection and incident response
• Automated security policy enforcement and compliance checks
• Predictive security risk assessment and vulnerability management
• Gen AI-driven security awareness and training

Service Continuity Management:

• Utilize Gen AI to analyze vast amounts of data from various sources to identify potential risks and their impact on business operations
• Create and simulate various disruption scenarios using Gen AI
• Automated disaster recovery planning and testing
• Automate communication with stakeholders during a crisis

Organizational Change Management:

• Gen AI-powered change impact analysis to assess potential reactions and concerns
• Generate personalized communication materials tailored to specific stakeholder groups
• Develop Gen AI-powered surveys and assessments to gauge readiness for change
• Generate personalized training content and recommendations based on learning styles and knowledge gaps
• Create interactive and engaging training experiences using GenAI-powered gamification and simulations

Stakeholder Management:

• Gen AI-driven stakeholder sentiment analysis to identify potential issues and concerns
• Generate personalized recommendations for stakeholder engagement
• Automated relationship and supplier management workflows and communication
• Predictive risk assessment and mitigation

Continual Service Improvement:

• Automatically discover and map ITSM processes based on logs, data, and user interactions
• Analyze process flows to identify bottlenecks, inefficiencies, and areas for improvement
• Track and analyze key performance indicators (KPIs) across various ITSM processes
• Simulate the potential impact of proposed improvements on service performance, user experience, and other key metrics
• Suggest improvement actions based on industry best practices, historical data, and AI-driven insights.

Agile:

• Applying Agile principles to ITSM processes
• Leveraging Gen AI for process backlog prioritization and sprint planning thereby facilitating continual improvement

DevOps:

• Integrating development, operations, and security with AI
• Gen AI-powered continuous integration and continuous delivery (CI/CD) pipelines

SRE:

• Implementing SRE principles for service reliability and resilience
• Leveraging Gen AI for error budgeting and incident response automation

• Identifying suitable Generative AI use cases and defining business objectives
• Data preparation and model training strategies
• Integrating Gen AI solutions with existing ITSM tools and platforms
• Measuring the impact and ROI of Generative AI implementations

• Data privacy and security concerns
• Bias in Generative AI models and the need for transparency
• Impact on IT jobs and the need for up-skilling
• Ethical considerations in using Generative AI

• Emerging trends and innovations in Generative AI
• The evolving role of ITSM professionals in a Generative AI-powered environment
• Integrating Gen AI solutions with existing ITSM tools and platforms
• Measuring the impact and ROI of Generative AI implementations

• Learn how AI can enable automated IT processes to be applied in ticket routing, resource allocation, and service desk management. .
• Learn how AI can be applied in real-time to predict and prevent failures before they happen.
• How to utilize AI models for detecting anomalies within traffic in networks and system behaviour to support improved security and operational resilience.
• Understand the process of deploying AI chatbots and virtual assistants to automate IT support and answer frequently asked questions instantly.

If you are any one of the following, then you are on the right track to pursue Generative AI in ITSM Certification:

• IT Professionals
• Service Managers
• AI Enthusiasts
• Business Analysts

There is no pre-requisite, but having relevant experience in the IT field can be beneficial

As part of this course, you will be engaged in a variety of interesting processes that will enable you to apply your theoretical knowledge to real-world problems. With our innovative approach, engagement and enthusiasm will increase.

• Training Sessions based on Storytelling.
• Concept-Based Examples for Understanding.
• Self-reflection with group conversation.
• Case studies as applied ideas given.
• Group and individual activities, and talks.
• Summarized readings to be taken home after the training.

Having the opportunity to learn from professionals with extensive experience is invaluable, and we all know that. The same learning experience will be offered to you. Our faculty has a strong industry A Generative AI Professional must have the experience to be successful. With over 7,000 professionals guided by them, you can be confident you're in good hands.

Certified Generative AI in ITSM Exam Format:

• Exam Time: 90 Minutes
• Exam Format - Objective Type, Multiple Choice
• Number of Questions: 40 (multiple-choice questions)
• Passing Marks: 26 out of 40 (65%)
• Certificate - Within 5 business days
• Result - Immediately after the exam
• Open book - Yes

• Enhanced Predictive Maintenance:The AI-based tool can monitor the system proactively, predict failures, and provide recommendations to the maintenance team.
• Better Resource Management:AI can optimize resource utilization by forecasting demand and simulating various scenarios for more efficient utilization.
• Improved speed and efficiency of the Service Desk Operation:AI-powered chatbots; and artificially intelligent responses enable quicker answers to customers' queries, keeping the customers satisfied.
• Data-Driven Decision Making:AI is a business enabler that allows an organization to gain deeper insights through the analysis of data, thus making the IT organization more informed.
• Anomaly Detection and Advanced Security:With the integration of AI, the system can predict anomalies in system performance or network traffic to its best, and IT teams can respond to potential security threats in time.