IT AI Management
Certificate in IT AI Management
The Certificate in IT AI Management (ITAIM) is for IT professionals and leaders of all levels. It’s designed to assist IT in the oversight and management of organizational AI-related activities and contains the following one-day classes:
- AI Governance from an IT Perspective
- Help Desk Support of AI Technologies
- IT Change Management for AI Adoption
- Managing AI-Driven Shadow IT
- Preparing IT Leadership for AI
Program Overview
The Certificate in IT AI Management is made up of five one-day classes. Each class can be taken individually, or participants can complete all five to earn the ITAIM certificate and digital badge.
The program is designed to help IT teams better manage organizational AI-related activities, including governance, internal client service, change management, shadow IT, and leadership readiness.
Participants will learn how to:
- Understand IT’s role in organizational AI adoption.
- Support responsible AI use across business teams.
- Build stronger AI governance practices.
- Clarify and reinforce AI policies.
- Manage AI-related change and user expectations.
- Identify and respond to shadow IT risks.
- Prepare IT leaders to guide AI projects and decisions.
- Balance innovation, control, support, and business value.
Quick Facts
Format:
Five one-day classes
Delivery:
Small-group corporate training, virtual, or public schedule options
Audience:
IT professionals and leaders at all levels
Credential:
Certificate in IT AI Management and digital badge after completing all five classes
Professional Development:
Each one-day class includes 7 PDUs
Includes:
Class materials, one-year access to the IT Leadership Growth Library, and monthly alumni continuous learning newsletters
IT AI Management Course
Day 1
AI Governance from an IT Perspective
AI governance is becoming a core responsibility for IT leaders. This session helps participants understand how AI governance connects to IT governance, data governance, risk management, compliance, ethics, lifecycle management, and organizational oversight.
Topics include:
- AI usage options
- Causes of IT-user conflict around AI
- The connection between IT, data, and AI governance
- AI governance scope and definitions
- IT’s role in AI governance
- AI governance activities for IT
- Risk management in AI systems
- AI governance design process
- Governance templates and deliverables
Day 2
Help Desk Support of AI Technologies
As AI tools spread across the organization, IT teams are often asked to support users, clarify policies, and help teams adopt new tools responsibly. This session focuses on internal client service in AI-related environments.
Topics include:
- Managing internal client expectations
- Supporting AI projects and user adoption
- Providing practical generative AI support
- Clarifying and reinforcing AI policies
- Tiered AI support models
- AI support implementation steps
- Using influence and change concepts to support AI adoption
- Documenting AI client types
- Saying “no” while maintaining trust and professionalism
Day 3
IT Change Management for AI Adoption
AI initiatives often require new behaviors, new processes, new communication strategies, and new levels of user adoption. This session helps IT leaders integrate AI-related activities into project planning and change management.
Topics include:
- Change management principles and methodologies
- AI-related activities in project planning
- Change adoption and acceptance
- Resistance to change
- Influence-based message design
- AI-assisted change communication
- Non-messaging AI uses in change management
- ERICA Change Framework
- Personal and departmental development planning
Day 4
Managing AI-Driven Shadow IT
AI has made it easier for non-IT users to create, adopt, and automate technology without IT involvement. This session helps leaders understand the risks, benefits, causes, and management strategies related to AI-driven shadow IT.
Topics include:
- What shadow IT means in an AI environment
- Why AI accelerates shadow IT
- Organizational, vendor, funding, and resource drivers
- Low-code, no-code, SaaS, and AI-powered tools
- Security, compliance, data, and support risks
- Costs, productivity gains, and productivity losses
- Business benefits of responsible user innovation
- IT strategies for managing shadow IT
- Collaboration between IT and business units
- Governance, guidelines, controls, metrics, and action planning
Day 5
Preparing IT Leadership for AI
AI leadership requires more than technical awareness. IT leaders must know how to assess readiness, weigh risks and benefits, make implementation decisions, and prepare their teams and organizations for AI-driven change.
Topics include:
- The history and impact of IT-intensive innovation
- Understanding AI’s implementation impact
- When and where to implement AI
- AI strategy based on organizational size
- Taking an IT-first AI approach
- Preparing yourself to lead an AI team
- Preparing IT to lead organizational AI
- Preparing the organization for AI
- AI risk readiness
- AI strategic thinking and planning
- Decision factors for AI implementation
Bring AI Leadership Training to Your IT Team
Prepare your IT team to lead AI adoption responsibly, support business users effectively, and make stronger decisions about governance, risk, change, and AI-enabled innovation.
