Certified Generative AI IN ITSM

Why Choose Garranto Academy for Your Generative AI in ITSM Training?

Garranto Academy offers expert-led training, hands-on learning, and industry-recognized certification, empowering IT professionals to harness AI-driven ITSM solutions for enhanced service operations.

Course Overview:

The Certified Generative AI in ITSM course is a focused two-day training program that empowers IT professionals to harness the power of generative AI in IT Service Management. Designed for service managers, consultants, and IT practitioners, the course covers how to automate service desk operations, improve incident response, and streamline change management using AI-driven tools. Participants will explore AI-powered ticket resolution, predictive analytics for proactive incident handling, and intelligent service automation techniques. Through practical case studies and real-world examples, learners will gain hands-on experience in enhancing service delivery and operational efficiency. This certification affirms your ability to innovate with AI in ITSM, preparing you for leadership in today’s rapidly evolving, AI-integrated IT environments.

What You'll Learn in Our Certified Generative AI in ITSM Course?

Course Objectives:

• Validate practical skills in applying Generative AI to IT Service management

processes.

• Test the ability to use Generative AI for automated response generation,

and predictive problem-solving.

• Validate your skills to use generative AI to redefine SLAs with intelligent

escalations.

• Enhance career prospects and earning potential by specializing in the

integration of generative AI in IT service management.

• Provide a globally recognized certification that validates expertise in

modernizing ITSM with modern generative AI capabilities.

Prerequisites

• Basic understanding of artificial intelligence concepts

• Prior experience in IT service management

• Willingness to test one's practical skills using Generative AI

Course Outlines:

Module 1: Introduction to Generative AI

• Fundamentals of Generative AI

• Types of Generative AI models (e.g., GPT, GANs)

• Use cases of Generative AI in various industries

Module 2: Overview of IT Service Management (ITSM)

• Key ITSM processes (e.g., Incident, Problem, Change, Knowledge

Management)

• ITSM best practices and frameworks (e.g., ITIL)

• Challenges and opportunities in ITSM

Module 3: Generative AI in Core ITSM Processes Service Desk:

• GenAI-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 GenAI 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 GenAI to automatically suggest relevant knowledge articles or

solutions

• Deploy GenAI-powered chatbots to provide self-service support to endusers

• Leverage GenAI 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:

• GenAI-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

• GenAI-powered CMDB querying and reporting

IT Asset Management:

• GenAI-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

• GenAI-driven prediction for future asset needs based on historical usage

patterns and business requirements

Release Management:

• GenAI-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

Module 4: Generative AI in Supporting ITSM Processes Service Level Management:

• GenAI-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 GenAI 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

• GenAI-powered capacity optimization recommendations

Availability Management:

• GenAI-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

• GenAI-powered recommended adjustments to allocate resources and

optimize infrastructure design for availability and performance

Demand Management:

• GenAI-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:

• GenAI-powered threat detection and incident response

• Automated security policy enforcement and compliance checks

• Predictive security risk assessment and vulnerability management

• GenAI-driven security awareness and training

Service Continuity Management:

• Utilize GenAI 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 GenAI

• Automated disaster recovery planning and testing

• Automate communication with stakeholders during a crisis

Organizational Change Management:

• GenAI-powered change impact analysis to assess potential reactions and

concerns

• Generate personalized communication materials tailored to specific

stakeholder groups

• Develop GenAI-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 GenAIpowered gamification and simulations

Stakeholder Management (Relationship & Suppliers):

• GenAI-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

Module 5: Agile, DevOps, and SRE in ITSM with Generative AI Agile:

• Applying Agile principles to ITSM processes

• Leveraging GenAI for process backlog prioritization and sprint planning

thereby facilitating continual improvement

DevOps:

• Integrating development, operations, and security with AI

• GenAI-powered continuous integration and continuous delivery (CI/CD)

pipelines

SRE:

• Implementing SRE principles for service reliability and resilience

• Leveraging GenAI for error budgeting and incident response automation

Module 6: Implementing Generative AI in ITSM

• Identifying suitable Generative AI use cases and defining business

objectives

• Data preparation and model training strategies

• Integrating GenAI solutions with existing ITSM tools and platforms

• Measuring the impact and ROI of Generative AI implementations

Module 7: Challenges and Ethical Considerations

• Data privacy and security concerns

• Bias in Generative AI models and the need for transparency

• Impact on IT jobs and the need for upskilling

• Ethical considerations in using Generative AI

Module 8: Future Trends and Advancements

• Emerging trends and innovations in Generative AI

• The evolving role of ITSM professionals in an Generative AI-powered

environment

• The potential impact of Generative AI on ITSM job roles and

responsibilities

• Preparing for the future of ITSM with Generative AI

Course Outcomes:

Upon successful completion of this "Certified Generative AI IN ITSM" course, participants will:

• Participants will learn to leverage generative AI tools to automate repetitive

ITSM tasks, such as ticket classification, incident resolution, and change

management workflows.

• Attendees will gain expertise in using AI-generated insights for proactive

decision-making, enabling efficient resource allocation and improved

service delivery.

• Participants will acquire skills to develop and implement generative AIbased knowledge bases that improve self-service capabilities and reduce

response times.

• Attendees will learn to align generative AI solutions with ITIL and other ITSM

frameworks, driving innovation while maintaining compliance and best

practices.

• Participants will develop the ability to identify and mitigate potential risks,

such as data privacy issues and AI bias, while ensuring ethical deployment

of generative AI in ITSM.

Key Benefits of Becoming Certified in Generative AI in ITSM:

Gain expertise in leveraging Generative AI for IT Service Management (ITSM), enhancing automation, decision-making, and operational efficiency to drive digital transformation in IT services.

How Generative AI Can Transform IT Service Management?

Generative AI revolutionizes ITSM by automating repetitive tasks, providing real-time insights, and enabling proactive problem resolution, leading to faster response times and improved service quality.