AI Transformation Manager

Artificial intelligence today opens up many possibilities for organizations, but it also brings new demands on decision-making, coordination and responsibility. It is not enough to choose a tool, launch a pilot or train users. The real benefit arises only when an organization can connect business goals, technological capabilities, data, security, legal requirements, working with people and measuring results.

This four-day course prepares participants for the practical management of AI initiatives from the first idea through the selection of suitable use cases to the pilot, adoption, governance and impact assessment. Without clear management, AI activities can easily fragment into individual experiments that are not connected to the strategy, have no owner, are not measured or encounter user distrust.

The course helps create a practical framework for an individual or team that can distinguish where AI really makes sense, connect management, business, IT, security, legal, HR and end users, set rules and responsibilities, prepare a pilot, manage adoption and measure whether the initiative brings real value.

During the course, participants will create a practical working set - templates and frameworks that they can directly use or modify for their organization.

No technical knowledge of AI is required. The course assumes experience with project, process or organizational change management.

For hands-on exercises with generative AI, access to Copilot with your own license is required - the license is not included in the course, the participant must have their own.

• executives responsible for digitalization, innovation or operational improvement,
• project, program and transformation managers,
• business owners, product owners and business analysts,
• HR, L&D and internal communications staff who prepare people for change,
• governance, risk management, security and compliance specialists,
• team managers who want to implement AI in a controlled and practical way,
• representatives of the public sector and regulated organizations,
• everyone who decides on priorities, risks and investments in AI.

Day 1 – Choosing the Right AI Opportunities

• AI as a tool for organizational change, not just a new technology
• Where AI typically brings value and where dead ends arise
• The difference between an idea, a use case, a pilot, and a transformational initiative
• Mapping the processes and agendas suitable for AI
• Working with the expectations of management, expert teams and users
• Creating an initial portfolio of AI opportunities

Day 2 – Designing AI solutions and deciding on feasibility

• Current AI, generative AI, language models, agents and automation
• What AI does well, where it has limits and where it requires human control
• Prompting as a core skill for working with generative AI
• Deciding between a ready-made tool, platform, agent, automation, and self-development
• Data readiness: availability, quality, responsiveness, and authorization to data
• When is a quick pilot appropriate and when is more in-depth preparation needed

Day 3 – Pilot, adoption, and management changes

• Pilot design: goal, scope, users, owners, risks and success criteria
• Roles of business, IT, security, legal, HR and management
• AI adoption in teams and changing work habits
• Communicating benefits, limits and responsible use
• Working with fears, resistance and exaggerated expectations
• Measurement adoption, quality, productivity and real-world impact

Day 4 – Governance, security and responsible use of AI

• Basic governance for AI in the organization
• Rules for the use of AI tools by employees
• Legal and regulatory context: AI Act, AI Pact, GDPR and NIS2
• Working with personal, sensitive and confidential data
• Authors rights, inputs, outputs and responsibility for content
• Security risks: identities, accesses, data, suppliers, incidents
• Specifics of the public sector and regulated environments

• Identify appropriate opportunities for AI use in processes, teams, and services.
• Sort and prioritize AI use cases by value, risk, and feasibility.
• Assess the organization’s readiness across people, data, processes, and technology.
• Prepare a pilot scenario including goal, scope, risks, owners, and metrics.
• Set ground rules for AI use in the organization.
• Communicate AI clearly to different stakeholder groups.
• Measure adoption, quality of deliverables, and practical benefit of the AI ​​initiative.

Who is the Managing AI Initiatives in an Organization course for?
For managers, project and program leaders, business owners, HR and governance specialists who need to prepare, coordinate and evaluate AI initiatives.

Is technical knowledge of AI necessary?
No, technological topics are explained in a managerial way and in relation to decision-making and impact on the organization.

How does the course differ from the AI Change Manager: Managing AI Adoption in an Organization course?
This course covers the entire life cycle of AI. cycle of an AI initiative including opportunity selection, feasibility and governance, while the adoption course focuses more deeply on changing work habits and people's trust.

What will the participant take away from the course?
A practical work kit - AI opportunity map, prioritization matrix, pilot proposal, governance checklist and metrics for evaluating the impact in their own organization.

Does the course also discuss legislative contexts?
Yes, the fourth The day is dedicated to governance and contexts such as the AI ​​Act, GDPR and NIS2, always on a practical, managerial level without unnecessary legalese.