What you'll learn:
- Explain core GenAI concepts (LLMs, prompts, training, fine-tuning) in plain English for executive decision-making.
- Identify and prioritize high-value GenAI use cases across functions using impact/effort and risk-based thinking.
- Apply See-Plan-Act and PAD to define problems, required AI abilities, and data readiness for pilots.
- Evaluate GenAI tool choices (buy/boost/build) and when to use RAG for grounded, enterprise-ready outputs.
- Design and run GenAI pilots with measurable KPIs, quality checks, and a clear path to scale.
- Create governance guardrails for GenAI: privacy, security, bias, IP, human oversight, and monitoring.
Generative AI is moving from “nice to have” to a board-level imperative. Consider what leading research and enterprise data suggest:
• McKinsey estimates GenAI could add up to $4.4T in annual economic value.
• Gartner predicts that by 2027, over 80% of companies will be using GenAI in some form.
• Enterprise investment in GenAI has surged dramatically—meaning the competitive gap between adopters and laggards is widening.
If your competitors use GenAI to ship faster, cut costs, and personalize customer experiences at scale, they don’t just get better—they pull ahead. And for CEOs and senior leaders, the real risk isn’t “getting AI wrong.” It’s waiting too long, running scattered pilots, or deploying tools without governance and control.
This course is designed to help you lead GenAI strategically and responsibly, without needing a technical background. You’ll learn how GenAI works in plain English, where it creates value across the organization, and how to move from curiosity to confident execution.
In this course, you’ll learn how to:
• Understand GenAI capabilities and limitations (including hallucinations and bias)
• Spot high-impact use cases across marketing, sales, service, product, operations, and HR
• Apply practical frameworks (like See-Plan-Act and PAD) to prioritize the right initiatives
• Choose the right approach: buy vs. boost vs. build—and when RAG makes sense
• Launch pilots with clear success metrics, quality controls, and human-in-the-loop review
• Scale safely with governance: privacy, security, IP, compliance, and ethical guardrails
• Build an AI-ready organization with the right data foundation, skills, culture, and ownership
By the end, you’ll have a clear CEO-level playbook to evaluate opportunities, make informed investment decisions, align stakeholders, and deploy GenAI in a way that drives measurable business outcomes—without losing control of risk.
