Course · Training · Workshop

AI Lab for Management in Tech

Hands-on lab for management in tech: try out agentic systems, AI-assisted workflows, and agentic engineering – build literacy and see concretely what is possible today.

This lab is aimed at management in tech who want to not just understand AI, but actively apply it themselves – regardless of whether you see yourself as technically savvy or not. The focus is on doing: participants work with agentic systems, solve real tasks with AI-assisted workflows, and experience hands-on how agentic engineering can reshape products, processes, and decisions. Together we build AI literacy – with a clear view of possibilities, limits, risks, and costs – and translate the experience into implications for the own organisation. For in-house formats the course can be adapted to the specific industry, tool landscape, and concrete use cases. The aim is that leaders return to their organisation with their own experience rather than only slide knowledge.

In-house course

Tailored for your team.

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Trainers

Jonas Felix

Instructor, Co-Founder

Marc Herren

Instructor

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Content

The course consists of the following topics and may be extended or adapted based on the audience. The lab is designed for 1-2 days and deliberately alternates between short inputs and longer hands-on blocks in which participants work with AI tools themselves. For in-house formats, tool selection, examples, and use cases can be tailored to the own organisation.

– Introduction to the AI landscape for management:

Terms: AI, generative AI, large language models, agentic systems
What is practically possible today – and what is not
Hype vs. substance: positioning current developments – Hands-On: First steps with AI assistants:
Using chat-based AI tools effectively
Prompting basics for management tasks
Hands-On: Process own typical work tasks with AI – AI-assisted workflows in daily work:
Research, summarisation, analysis, decision preparation
Working with documents, data, and unstructured information
Hands-On: Implement an own workflow with AI support – Understanding and experiencing agentic systems:
From chat to agent: what is an agent, what can it do?
Agents with tools and data access – examples from practice
Hands-On: Solve a multi-step task with an agentic system – Agentic engineering from a management perspective:
How teams today develop and operate software with AI agents
Impact on roles, skills, team setup, and delivery capability
Hands-On: Walk through a small engineering task with an agentic coding tool – AI in product and business model:
Adding AI features vs. thinking AI-native products
Build vs. buy: in-house development, API usage, platforms
Use case evaluation: where AI adds real value, where not – Data, context, and integration:
Why context is decisive – retrieval, knowledge bases, connectors
Model Context Protocol (MCP) and similar integration approaches
Hands-On: Let AI work with own documents or data – Risks, governance, and compliance:
Hallucinations, bias, reproducibility, auditability
Data protection, confidentiality, and regulatory topics (e.g. EU AI Act)
Secure working: secrets, access, approvals in agentic setups – Economics: cost, benefit, risk:
Cost models (token, API, infrastructure, personnel time)
ROI considerations for AI initiatives
Realistic expectation management in the own environment – Adoption in the own organisation:
Building AI literacy in team and company
Identifying and prioritising pilot use cases
From pilot to scaling: platform, guardrails, responsibilities – Outlook and next steps:
Market developments, models, tools – what to watch
Hands-On: Sketch a personal roadmap and own next experiments

The focus is on well-established, vendor- and model-independent tools so that what is learned remains transferable. Concepts, workflows, and evaluation criteria can be applied to other tools and platforms.

The actual course content may differ from the above depending on the trainer, delivery, duration and the composition of participants.

Goal

After completing this lab you have own hands-on experience with agentic systems, AI-assisted workflows, and agentic engineering. You can judge which AI approaches fit which tasks, know the central risks, regulatory topics, and cost factors, and can evaluate and prioritise use cases in your own environment. You leave with a first personal roadmap to deliberately build AI literacy in your team and organisation.

Duration

1-2 Days

Format

The lab combines compact explanations with extended hands-on blocks in which participants work with AI assistants and agentic tools themselves. Real examples, live demos, guided exercises, and discussions on implications for the own organisation alternate. The trainer accompanies participants closely and translates the experience directly into management-relevant conclusions.

Target group

This lab is aimed at leaders and decision makers in tech organisations: CTOs, CIOs, engineering managers, product managers, heads of engineering/data/platform, technical program and area leads, and managing directors in technology-driven companies. Also suitable for leaders who do not see themselves as technical but want to understand and try out AI themselves rather than only delegate it.

Prerequisites

Prior AI knowledge is not required. Basic familiarity with modern software or product organisations is helpful, as is the willingness to actively work with AI tools yourself. Technical experience as a developer is beneficial but not mandatory.

Preparation

Before the lab participants receive a short questionnaire on experience level, interests, and typical tasks so that examples and hands-on exercises can be tailored. A laptop is needed for the exercises; required access to AI tools and API keys are provided for the lab. Participants may optionally bring own anonymised documents or task descriptions they would like to work on hands-on.

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More about the AI Lab for Management in Tech

AI is currently transforming how knowledge is built, software is developed, and business models are designed. For management in tech this creates a new requirement: not only to talk about it or position buzzwords, but to have enough own experience to make well-founded decisions. This lab addresses exactly this – with concrete doing instead of only slides, and with a clear view of opportunities, limits, and risks.

Further resources:

History and development

Generative AI moved within a short time from a research topic to a central management topic with the public breakthrough of large language models around 2022/2023. Since 2024 the focus has been shifting from chat assistants to agentic systems that autonomously execute multi-step tasks, use tools, and work with data.

For leaders this means: AI decisions are becoming increasingly strategic – they touch product strategy, organisation, skills, governance, and delivery capability. Labs like this one emerge from the observation that theoretical knowledge alone is not enough; own hands-on experience is the most reliable basis for good decisions.