AI from ChatGPT to Custom LLMs                                                              

• Quick introduction to the current AI Landscape
• Why and how AI is transforming enterprises now.
• Poll/icebreaker: “Where is your company currently with Generative AI? Exploring, Piloting, Scaling, or Not Started?”

Generative AI in the Enterprise

• What is Generative AI (LLMs and Transformers simplified).
• Evolution: From ChatGPT to enterprise-ready Custom LLMs.
• Differences between general-purpose AI (ChatGPT) and domain-specific fine-tuned models.

Business Use Cases Across Functions

• Marketing & Sales– Content generation, personalization.
• Customer Support– AI copilots, knowledge retrieval (RAG).
• HR & Talent– Resume screening, policy summarization.
• Healthcare & Finance– Clinical documentation, fraud detection.
• Case spotlight: How US companies are adopting Generative AI (e.g., Morgan Stanley’s advisor GPT, Mayo Clinic pilots etc.)

Live Demo: Prompt Engineering and Retrieval Augmented Generation

• Showcase: same question → different outputs using zero-shot, few-shot, chain-of-thought prompts.
• Highlight: How structured prompting changes business outcomes.
• Highlight: Benefits (accuracy, compliance, brand alignment).

Responsible & Ethical AI – Balancing Innovation with Governance

• Risks: hallucination, bias, IP leakage, over-reliance.
• Bias & Fairness: Case study – biased recruitment models.
• Explainability: Why “black box” AI cannot be blindly trusted.
• US Regulatory Landscape: AI Bill of Rights, NIST AI Risk Framework.
• EU AI Act implications for US firms: compliance readiness, vendor accountability.

Outlook: Where Generative AI is Headed

• The rise of Agentic AI (autonomous workflows).
• Skills & workforce implications: reskilling for AI adoption.

Q&A and Wrap-Up

• Audience open questions.
• Closing takeaway: “Generative AI is powerful, but only responsible adoption creates long-term enterprise value.”