AI Change Manager

Successful AI implementation doesn’t start with choosing a tool. It starts with an organization being able to explain to people why it’s implementing AI, where it has real benefits, what rules apply, and what is expected of individual teams.
Many organizations today are solving a similar problem: AI tools are available, management expects quick results, employees try them out in different ways, but there is no unified framework, clear rules, and a clear adoption plan. The result is haphazard use, resistance from some employees, unclear success metrics, and a return to old ways of working after initial enthusiasm.
This three-day course helps to grasp these situations from a managerial and practical perspective. AI is not understood here as an isolated technology project, but as a change in the way of working, decision-making, collaboration, and responsibility across the organization. The course combines change management, user adoption, working with concerns, leadership engagement, skill development, communication, risk management, and measuring real impact into one practical procedure.
During the course, participants will create their own work package for managing AI adoption, which they can directly use in their organization.

No technical knowledge of AI is required. The course assumes experience in managing people, projects, or organizational change.

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

• managers responsible for implementing AI tools and digital change,
• team leaders who need to prepare their people for new ways of working,
• project and program managers driving AI initiatives,
• HR, L&D and internal communications supporting skills development and behavior change,
• AI ambassadors, internal champions and adoption coordinators,
• business owners looking for practical use of AI in their agendas,
• specialists from IT, security, compliance and legal teams,
• representatives of public sector and regulated organizations,
• transformation and innovation teams connecting strategy, people and operations.

Day 1 – AI as a change in work, not as a new tool

• AI as a change in work habits, decision-making and responsibility
• Typical reasons for low adoption: uncertainty, mistrust, fear of control or replacement
• Impacts of AI on roles, teams, processes and managerial functioning
• The difference between tool introduction, pilot, adoption and long-term use
• Mapping the groups that support, inhibit or ignore change
• The role of leadership, middle management and local ambassadors
• Creating a change story that is understandable to employees and management

Day 2 – Trust, rules and skills for using AI

• Translating AI strategy into everyday life teamwork
• Ground rules for using AI: data, sensitive information, output control, human responsibility
• Working with employee concerns: security, output quality, loss of expertise, role change
• A role-based AI skills development plan
• Involvement of HR, L&D, internal communications, IT, security and compliance
• Setting up a support structure: ambassadors, best practices, consultation, feedback link

Day 3 – Managing adoption, measuring benefits, and managing challenges

• Managing AI adoption after pilot: scaling, modifying, stopping, or scaling
• Adoption metrics: usage, quality, time savings, trust, process impact
• Ownership after deployment and preventing a return to old habits
• Escalation management: data concerns, AI backlash, manager resistance
• Long-term embedding of AI into processes, governance and education
• Final practical simulation: AI adoption under pressure, defending the adoption plan against the leadership model

• Formulate a clear case for implementing AI in a specific organization or team.
• Assess the impacts of AI on roles, processes, responsibilities, and the way of working.
• Identify stakeholders, their motivations, concerns, and level of influence.
• Prepare communication scenarios for different groups of employees.
• Design an AI skills development plan and ground rules for safe use of AI.
• Define metrics for adoption, quality of outputs, and impact.
• Prepare a long-term support model after the pilot or rollout.

Who is the course Managing AI Adoption in an Organization for?
For managers, team leaders, HR, L&D, AI ambassadors and specialists from IT, security or compliance who have an influence on whether AI really takes hold in an organization.

Is the course also suitable for non-technical roles?
Yes, the course does not focus on technical skills, but on managing change, communication and adopting AI in practice.

What will the participant take away from the course?
A custom work package for managing AI adoption – adoption plan, stakeholder map, communication scenarios, and success metrics for a selected scenario from your organization.

Why does AI adoption often fail?
Usually not because of the technology, but because of unclear purpose, poor communication, employee concerns, and weak ownership of the change.

How does the course help measure the benefits of AI?
It teaches you to distinguish user activity from actual behavioral change. and set adoption, quality, and impact metrics that are defensible to management.