AI doesn't transform organisations. People do.

AI training typically focuses on knowledge and skills – for example, how to use a tool or write better prompts. AI enablement goes further. It helps people apply AI safely and effectively in their actual work, develop relevant use cases, establish new ways of working and build the confidence, routines and shared standards needed to sustain them.

The goal is not simply to know more about AI, but to make AI work in the context of your organization.

Sustainable AI enablement requires time for application, reflection and iteration. That is why the program is structured in three consecutive sprints, with each sprint typically running for four to six weeks.

The exact duration depends on the organization’s starting point, internal dynamics and the complexity of the use cases. Some teams benefit from a faster four-week rhythm, while others need more time between sessions to test new approaches in their actual work, gather feedback and develop their solutions further.

This results in a program duration of 12 to 18 weeks – providing enough structure to maintain momentum while allowing the pace to adapt to the organization’s real working context.

An AI Operating Model defines how AI can be used effectively and responsibly within a team or organisation. It brings together the practical elements needed for sustainable AI-enabled work – including relevant use cases, workflows, roles and responsibilities, quality criteria, guardrails and ways of collaborating and learning.

Throughout the program, participants develop these elements based on their own work. The result is therefore not a generic framework, but a practical foundation for continuing AI adoption after the program ends.

The program is designed to integrate learning into real work rather than adding a separate training workload. Participants join the core sessions and work on selected use cases and practical assignments between them.

The most important commitment is not additional study time, but the willingness to experiment with AI in everyday work, share experiences and continuously develop the selected use cases.

Leadership involvement is equally important: leaders help provide direction, create space for experimentation and support the conditions needed for new ways of working to become sustainable.

We do not start with a predefined set of tools or generic exercises. The program is built around the organization’s actual context: existing AI maturity, available tools, workflows, data, responsibilities and relevant business challenges.

Participants work on real use cases from their own environment. The tools and methods used throughout the program help them assess where AI creates value, what conditions are required and where human oversight or organizational guardrails remain necessary.

You can start where it makes sense for your organization.

The AI Readiness Assessment creates transparency about your current starting point. AI Strategy helps establish direction, priorities and organizational focus. The AI Enablement Program turns this into practical application and sustainable ways of working.

These elements can form one end-to-end journey, but they can also be used individually depending on what is already in place and where the greatest need currently exists.

The AI Readiness Assessment creates a structured baseline of how prepared people are to work effectively with AI. It looks beyond technical knowledge and considers areas such as AI competence, change readiness and team impact, as well as specific dimensions of AI literacy.

Participants receive an individual view of their results and development areas. For the organization, results can be aggregated to identify patterns, strengths and development needs across teams or groups without turning individual scores into a performance evaluation.

How results are shared and used is defined transparently in advance and can be aligned with internal privacy, compliance and works council requirements.

The individual assessment reports already reflect this broader logic: besides AI competence, they include change readiness, team impact and six AI-literacy dimensions rather than assessing prompt skills alone.

The EU AI Act requires organizations to take measures to ensure an appropriate level of AI literacy among people working with AI systems.

What is appropriate, however, depends on the context — including roles, existing capabilities, the AI systems being used and the associated opportunities and risks. The AI Readiness Assessment provides a structured way to make the current starting point visible.

It is not a legal requirement to conduct an assessment. Instead, the assessment provides organizations with a baseline that can help them decide which AI literacy and enablement measures are appropriate for their specific context and where action is actually needed.

That logic also fits the broader rationale behind the assessment very well: the external research document you uploaded explicitly argues for establishing an objective baseline before designing interventions rather than treating the entire workforce as one homogeneous group.