Upon successful completion of this program, participants will be able to:

• Articulate the core capabilities and limitations of various Generative AI models (LLMs, image, code generation).
• Identify and prioritize high-value GenAI use cases across multiple business functions (e.g., marketing, code, content).
• Design and pilot workflows that fundamentally leverage GenAI for efficiency and creativity gains.
• Develop a technical strategy for integrating GenAI, including build vs. buy decisions and model selection.
• Establish clear governance and safety policies for GenAI usage, addressing data security and intellectual property.
• Measure the ROI and productivity uplift from GenAI implementations.
• Lead the cultural and change management required to adapt teams to GenAI-augmented work.
• Formulate a comprehensive Generative AI strategy aligned with enterprise digital goals.

• C-Suite Executives and Senior Strategists
• Heads of Digital Transformation and Innovation
• Product Managers and Owners focused on AI services
• IT and Enterprise Architects
• Marketing and Content Creation Directors
• Legal and Compliance Officers managing IP and data risk
• Business Process Optimization Specialists

• **Scenarios:** Developing a crisis communication plan following a GenAI system's "hallucination" in a client-facing document.
• **Case Studies:** Analyzing organizations that have successfully redesigned their content creation or software development workflows using GenAI.
• **Group Activities:** Conducting a risk assessment and IP analysis for a proposed internal GenAI legal drafting tool.
• **Individual Exercises:** Drafting a one-page "Build vs. Buy" decision matrix for implementing a customer service chatbot using GenAI.
• **Mini-Case Studies:** Reviewing and improving a set of initial prompts designed for generating marketing copy for consistency and brand voice.
• **Syndicate Discussions:** Debating the ethical boundaries of using GenAI to simulate customer interactions for training purposes.
• **Strategy Workshop:** Developing the first three high-priority GenAI pilots for a fictional company.

Unit 1: Understanding the Generative AI Landscape

• Overview of Large Language Models (LLMs), diffusion models, and foundation models.
• Differentiating between In-Context Learning, Fine-Tuning, and Retrieval-Augmented Generation (RAG).
• Analyzing GenAI's current and future impact on various industries and job functions.
• Case studies of early GenAI successes in code generation and creative content.
• Assessing the risk profiles and inherent biases of popular foundation models.
• The economics of GenAI: API costs vs. self-hosting.

Unit 2: Strategic Use Case Identification

• Frameworks for systematically identifying and prioritizing GenAI applications (e.g., high volume, repetitive tasks).
• Analyzing core business processes for GenAI-driven transformation potential.
• Developing a GenAI "Idea Bank" based on productivity and creativity uplift.
• Defining clear metrics (KPIs) for GenAI pilots and measuring ROI.
• The art of prompt engineering for business-critical tasks.
• Identifying "GenAI-native" business models and services.

Unit 3: Workflow Redesign and Integration

• Applying business process mapping to redesign workflows with GenAI checkpoints.
• Integrating GenAI into existing enterprise systems (e.g., CRM, ERP, Slack).
• Designing the human-in-the-loop oversight model for critical GenAI outputs.
• Developing a technical integration strategy (e.g., API gateway, vector databases).
• Managing the data privacy and security of internal prompts and generated content.
• Techniques for rapid piloting and iterative deployment.

Unit 4: Governance, Risk, and Compliance

• Establishing clear policies for employee use of GenAI (Acceptable Use Policy).
• Navigating the complex Intellectual Property (IP) landscape regarding generated content.
• Developing safety guardrails and moderation layers for internal-facing GenAI tools.
• Data governance strategies for RAG-based systems and private data.
• Implementing techniques to prevent data leakage and malicious prompt injection.
• Auditing GenAI outputs for factual accuracy (hallucination mitigation).

Unit 5: Leading the GenAI Transformation

• Communicating the vision of GenAI as an "augmentation" tool, not a replacement.
• Strategies for upskilling and reskilling teams in prompt engineering and model oversight.
• Managing anxiety and resistance related to GenAI adoption.
• Fostering a culture of "AI Literacy" and responsible experimentation.
• Structuring the GenAI Center of Excellence (CoE) and internal talent development.
• Developing a long-term GenAI strategy and investment roadmap.