Generative AI for Businesses

Module 1: Introducing Generative AI in the Business Context

This opening module provides a conceptual and strategic introduction to generative AI. Participants examine what distinguishes GenAI from earlier forms of AI, understand how large language, vision, and multimodal models operate, and explore the technological principles behind their abilities and limitations. The module establishes a business-oriented understanding of GenAI as a tool that expands organizational capacities in areas such as ideation, problem-solving, customer engagement, and automation. Through real-world cases from multiple sectors, the module illustrates how generative models reshape workflows, reduce time-to-insight, and support new forms of value creation. Participants consider the risks associated with inaccurate outputs, hallucinations, and over-reliance, thus laying the foundations for responsible and informed use throughout the course.

Module 2: Generative AI Applications Across Business Functions

This module explores how generative AI transforms core organizational functions. Participants analyze sector-specific use cases in marketing, operations, HR, product development, finance, and customer service. Examples illustrate how GenAI automates document creation, enhances customer interaction through personalized content, accelerates due diligence, enables scenario modelling, and augments creativity in design processes. The module addresses the dynamics of augmentation rather than replacement, highlighting how GenAI supports human expertise while shifting the competencies required in knowledge-intensive roles. Attention centers on the balance between efficiency gains and the risk of diminishing employee ownership of decisions when generative systems execute tasks that traditionally required cognitive effort.

Module 3: Structured Prompting and the Preservation of Critical Thinking

This module investigates how interactions with GenAI shape cognitive processes, focusing on the risk of cognitive offloading when users rely on generative systems for complex reasoning or problem-solving. Participants examine the psychology of AI-assisted decision-making, understand mechanisms such as anchoring effects and automation bias, and reflect on the organizational risks associated with uncritical dependence. Building on this understanding, the module introduces structured prompting techniques that preserve analytical depth and intellectual ownership. Participants practice frameworks that encourage idea refinement, multi-step reasoning, controlled iteration, and critical evaluation of AI outputs. The module positions structured prompting as a core managerial competence that safeguards decision quality and enhances creative and strategic thinking in AI-rich environments.

Module 4: Integrating GenAI into Business Processes and Operating Models

This module analyses how GenAI reshapes business models, operational design, and process efficiency. Participants explore how workflows shift when generative systems support forecasting, planning, knowledge management, and customer-facing operations. Particular attention is paid to designing AI-enhanced processes that remain transparent, accountable, and aligned with organizational values. The module discusses the capabilities required to integrate GenAI at scale, including agile governance models, cross-functional collaboration, and adaptive organizational structures. Participants reflect on how GenAI expands the boundaries between human and machine contributions, blurs traditional job roles, and creates new forms of organizational interdependence.

Module 5: Data Foundations and Responsible AI Use for Generative Systems

This module considers the data foundations underlying generative AI applications. Participants examine the types of data needed for fine-tuning, customization, and domain adaptation, alongside the risks associated with data privacy, intellectual property, and regulatory compliance. The discussion highlights transparency, explainability, and accountability as essential pillars for responsible GenAI integration. Participants analyze practical governance mechanisms that ensure trustworthiness, mitigate bias, and maintain alignment with sectoral regulations. The module concludes with reflections on the emerging regulatory landscape for generative AI and how organizations can prepare proactively for evolving compliance requirements.

Module 6: People, Skills, and Organizational Readiness for Generative AI Adoption

The final module focuses on human and organizational readiness. Participants consider how GenAI alters workforce structures, skill profiles, and collaboration patterns. The module investigates the leadership responsibilities associated with guiding teams through technological transition, establishing psychological safety in AI-assisted workflows, and cultivating AI literacy across all organizational levels. Special attention is given to trust calibration in human-AI collaboration and the need for cultures that combine innovation enthusiasm with informed skepticism. Participants work through organizational readiness assessments that map capabilities, identify critical gaps, and inform strategic pathways for long-term GenAI integration.

Registration Fee: CHF 150 (part of the overall course fee). This fee is non-refundable if the participant cannot attend.

Refund Policy:

• The course fee is invoiced 3 months prior to the course start.
• 100% refund if canceled 4 weeks or more before the course start.
• 30% refund if canceled 2 weeks before the course start.
• No refund if canceled less than 2 weeks before the course start.