Overview
Generative AI is the rare technology that landed on every leader's desk at once: it writes, analyzes, summarizes, and converses well enough that every function in the business has a claim on it. That is exactly what makes it hard to lead. The capability is real, the hype is also real, and a leader's job is to tell them apart: to know where generative AI will actually move a business metric, what it costs to get there, and which operational questions must be answered before value shows up.
This is a hands-on, foundation course. It assumes no technical background and deliberately goes deep on a focused sequence rather than surveying the whole AI landscape: first an accurate, hands-on understanding of what generative AI does well and where it fails, then the patterns by which it creates business value, then the operational and risk questions every leader must be able to answer. Every module includes a hands-on lab and builds on the one before.
Who Should Attend
- Business leaders and senior managers deciding where generative AI fits in their organization
- Function heads (marketing, operations, service, finance, HR) evaluating generative AI for their teams
- Leaders who will sponsor, fund, or approve generative AI initiatives
Executives ready to build a full organizational plan should continue to AI Strategy for Executives.
Prerequisites
- None. No technical background is assumed
- Curiosity and a few real business problems from your own organization to work with
What You Will Learn
- Explain how generative AI works, in plain language sufficient to lead with confidence
- Judge what generative AI does well and where it fails, from direct hands-on experience
- Identify the value patterns that apply to your own part of the business
- Ask the operational questions that separate a good proposal from a risky one
- Screen generative AI use cases for risk and responsible use
- Build a credible 90-day plan to take one use case from idea to measured value
Course Outline
Day one: understanding the capability
- Generative AI in Plain Language
- How these models work, explained for decision-makers, not engineers
- The landscape: major models, assistants, and AI embedded in tools you already own
- Lab: work hands-on with a generative AI assistant on real leadership tasks
- What It Does Well and Where It Fails
- Genuine strengths: drafting, summarizing, analysis, and conversation at scale
- Honest limits: hallucination, inconsistency, and the gap between demo and production
- Lab: stress-test an assistant on business tasks and log its wins and failures
- Value Patterns Across the Business
- Where value is showing up: marketing, operations, customer service, software, and knowledge work
- Case studies of real gains, and of pilots that went nowhere
- Lab: identify the two value patterns most applicable to your own business area
Day two: leading with it
- The Operational Questions
- Data privacy and confidentiality: what happens to what your people type in
- Cost, tooling choices, and reading vendor claims critically
- Lab: build a question checklist for evaluating any generative AI proposal
- Risk and Responsible Use for Leaders
- The leader's view of bias, misuse, and reputational risk
- Where deeper treatment lives: Responsible AI and AI Ethics and AI Governance for Leaders
- Lab: screen a proposed use case against a simple risk framework and make a call
- From Pilot to Value
- Why most pilots stall, and what the successful ones do differently
- Measuring value, driving adoption, and knowing when to stop
- Lab: draft a 90-day plan for one high-value use case and present it to the group
Extended Version
The three-day version keeps the same gradient and adds depth and practice:
- A deeper hands-on day applying generative AI across each attendee's own function
- Fuller treatment of the people side: adoption, skills, and team readiness
- More case-study work on measuring and communicating value
- A capstone in which each leader presents a complete use-case business brief and defends it under questioning