Subsidized AI Training

Module 1

Introduction to AI

A common language and a realistic picture: what AI can do, what it can't – and how to tell.

Predictive and generative AI, how language models work and why coherence is not plausibility. Six AI goal directions (faster, cheaper, better, safer, new, more revenue) and the four roles of AI in a business process. First hands-on tasks with agents and prompting – and the inventory: which AI use cases already exist in your company?

Module 2

Governance, ethics & compliance

GDPR, EU AI Act and internal guidelines – before anything is built.

Data protection, copyright, liability and the EU AI Act through case studies. Responsible AI: accountability, transparency, fairness, reliability. Concrete tools along the AI lifecycle: maturity assessment, impact assessment, data and model management – applied directly to the first own use case.

Module 3

Identify & evaluate use cases

Not every idea is worth it. Find, describe, evaluate and prioritise use cases.

The focus of the first half of the course: lean process analysis, stakeholder workshops and interviews, trend analyses. Complete use-case descriptions with template and Value Proposition Canvas, evaluation and risk matrix – and finally a prioritised AI project portfolio with elevator pitch.

Module 4

Technological implementation

Prompting, agents, StepGPT and n8n: the tools with which solutions are actually built.

Map the AI market – proprietary vs. open source, cloud vs. on-premise. From business requirements to solution selection with evaluation matrix and specifications. Define success metrics, identify critical assumptions, plan proof of concept and business case.

Module 5

Develop & test prototypes

Start small, measure, learn – check on the prototype whether AI really delivers.

Plan test environment, sampling and evaluation metrics. Prompt engineering on your own case in sandbox and no-code platform, structured with design thinking and Scrum elements. Honest evaluation: areas of use, limits, data requirements – and a solid recommendation: roll out, improve or deliberately discard.

Module 6

Integration into IT infrastructure

A solution that doesn't fit the system landscape remains a toy. This is where the prototype becomes daily practice.

Integration strategies: interfaces and APIs, process redesign, low-code automation with n8n as the connecting tool. The AI architecture puzzle of data sources, models, workflows and output channels. Operations and scaling with AIOps: monitoring, results analysis, human feedback loops. Large share of guided implementation on your own case.

Module 7

Organisational development & change management

Technology alone changes nothing. Bring colleagues along and anchor the change in the organisation.

Roles and responsibilities with stakeholder map and 7S model. Plan the rollout as a change initiative: participation, training, resources – with change canvas, 8-step model and hopes-and-fears template. Institutionalise AI competence: skill profiles, cross-departmental processes, operational excellence in teams.