- Understanding what AI is and isn't.

- Cover the evolution from rule-based systems to machine learning to generative AI.

- Discuss different AI paradigms (supervised learning, unsupervised learning, reinforcement learning, generative models) with real-world analogies.

- Examine current capabilities and fundamental limitations.

- Address common misconceptions and set realistic expectations for what AI can accomplish in professional contexts.

- Demystify the black box without heavy math.

- Explain neural networks using accessible metaphors (pattern recognition, weighted connections).

- Walk through how large language models process and generate text.

- Cover training data, fine-tuning, and why AI makes certain types of mistakes.

- Discuss embeddings and vector databases conceptually.

- Help participants develop intuition for when AI succeeds or fails at tasks.

- Move beyond ad-hoc AI to systematic integration.

- Teach frameworks for identifying high-value AI opportunities in their work.

- Cover workflow mapping and task decomposition.

- Discuss the "human-AI collaboration spectrum" from AI as an assistant to AI as an autonomous agent.

- Include change management considerations and teamadoption strategies.

hands-on: Each participantmaps ONE recurring workflow from their job in detail. Identify: repetitive
parts, decision points, creative parts, relationship parts.

- Go deeper than basic prompting into prompt engineering as a discipline.

- Cover chain-of-thought reasoning, few-shot learning, role-based prompting, and constraint specification.

- Teach techniques like iterative refinement, prompt chaining for complex tasks, and managing context windows effectively.

- Include structured output generation and working with different modalities (text, image, data).

- Survey the landscape beyond ChatGPT.

- Cover specialized AI tools for different professional functions (research, writing, analysis, design, and codingassistance).

- Discuss evaluation criteria for AI tools including accuracy, cost, privacy, and integration capabilities.

- Include hands-on tool comparisons where participants evaluate tools for their specific use cases.

- How to prepare, structure, and leverage their own data with AI.

- Cover document processing, data extraction and transformation, working with spreadsheets and databases through AI interfaces.

- Discuss RAG (Retrieval Augmented Generation) conceptually and practically.

- Include techniques for fact-checking and validation of AI outputs when working with data.

- Empower participants to create tailored AI applications.

- Introduce platforms like GPT custom instructions, OpenAI GPTs, Claude Projects, or tools like Zapier AI, Make.com for automation.

- Walk through building a simple custom solution from requirements to deployment.

- Cover API basics conceptually so participants understand integration possibilities even if they work with developers.

- Address critical considerations for professional AI deployment.

- Cover bias and fairness, privacy and data security, intellectual property concerns, transparency and explainability requirements.

- Discuss regulatory landscape (EU AI Act, industry-specific regulations). Develop organizational guidelines and decision frameworks
for responsible AI use.

- Participants design and prototype an AI solution for their actual work context.

- They define the problem, select appropriate AI approaches and tools, design the workflow, and present their solution. Includes peer feedback and iteration.