AI for Business Leaders Training Course

Module 1: What AI Actually Is (and Is Not)

• A short, honest history: from rules to machine learning to foundation models
• What a large language model actually does: prediction, not knowledge
• Terminology leaders must own: model, prompt, context window, token, fine-tuning, RAG, agent
• Why AI got cheap and fast, and what that changes about strategy
• The provider landscape and how to think about choosing
• What is not AI: automation, dashboards, and rules engines wearing a costume

Exercise 1, the Jargon Decoder: Teams are handed real sentences from vendor pitches, press releases, and internal memos. Which claims are meaningful, which are vague, and which are wrong? Each team rewrites one claim into something a technical team could actually be held to.

Module 2: Working With AI, Prompting as a Leadership Skill

• Clear and specific instructions: delimiters, structured output, examples
• Giving the model time to think: step-by-step reasoning and self-checking
• Iterative refinement: your first prompt is a draft, not a verdict
• Four workhorse patterns: summarize, infer, transform, expand
• Context engineering: what to hand the model, and in what order
• Where your data goes, and what never to paste

Exercise 2, the Prompting Workshop: A guided sequence rebuilt for a chat window and no code. Participants take a long document through summarize, infer, transform, and expand, then run a head-to-head: the naive prompt against the engineered prompt on the same task. The gap is the lesson.

Module 3: Capabilities, Limits, and Trust

• What today's AI is genuinely excellent at, and what it cannot do
• Hallucination: why it happens and why it will not fully go away
• Confidence is not accuracy
• Reducing risk: retrieval and grounding, citations, human review, narrow scope
• The reversibility test for deciding stakes
• Human-in-the-loop as a design choice, not a disclaimer

Exercise 3, Spot the Failure: Participants receive AI-generated outputs that look excellent and are subtly wrong. Find the errors, then classify: would our current process have caught this? Teams then set a trust threshold for three tasks in their own area.

Module 4: Finding and Prioritizing Business Value

• Where value comes from: cost, speed, quality, capacity, new capability
• Repeating patterns: document understanding, search, drafting, classification, extraction
• Use cases in regulated and safety-critical environments
• Sourcing candidates: the friction audit and the retyping test
• Scoring on value, feasibility, data readiness, and risk
• The portfolio view: quick wins, strategic bets, and things to refuse
• Anti-patterns: the solution looking for a problem, the demo that cannot scale

Exercise 4, the Use-Case Portfolio: Teams generate candidate use cases from their own organization, score them on the value and feasibility grid, and defend one quick win and one strategic bet to the room. The room votes. Output: a real prioritized portfolio, carried into Day 2.

Module 5: AI Strategy and the Stages of AI Success

• Strategy first: AI serves the business objective, not the reverse
• Stages from experimenting to transforming, and the failure mode at each
• Why organizations get stuck in pilot purgatory
• Foundations that set the ceiling: data availability, quality, access, infrastructure
• Organizing the capability: centralized, federated, or embedded
• Measurable objectives, and the vanity metrics to ignore
• Building the roadmap by sequencing capability

Exercise 5, Honest Maturity Assessment: Each participant scores their organization against the stage model and the foundational readiness checklist, then compares with the group. The gap between self-assessment and evidence is the discussion.

Module 6: Building an AI-Ready Culture

• Why adoption, not technology, is the binding constraint
• Four reactions to AI: enthusiasm, indifference, fear, quiet sabotage
• Talking honestly about jobs, and what leaders owe people
• Skills and roles: what to hire, train, and partner for
• Making experimentation safe: sanctioned tools and sandboxes
• Incentives and recognition for people who automate their own work
• What leaders must say, repeatedly, in their own words

Exercise 6, the Resistance Role Play: Participants take roles: the skeptical senior engineer, the anxious analyst, the enthusiastic overreacher, the compliance officer. One leader must carry an AI initiative through the conversation. Rotate, then debrief what actually worked.

Module 7: Responsible AI, Risk, and Governance

• Accountability, transparency, fairness, privacy, security, human oversight
• The real risks, ranked
• Data handling, retention, residency, and consumer versus enterprise accounts
• Security demonstrated live: an ordinary document hijacks a working assistant
• Which security instincts transfer and which do not
• Poisoned content, excessive agency, and why the AI did it is not a defense
• Policy, approval paths, inventory, logging, and audit
• The regulatory landscape at a leader's altitude
• Why it cannot be patched: the model cannot separate your instruction from text it was given to read
• The governing principle for agents: a system that only writes text is a smaller problem than one that can act
• Assigning accountability: who owns the output of a machine?

Exercise 7, the Incident Tabletop: A realistic scenario unfolds in stages: an AI-assisted document goes out with a fabricated reference in it. Teams work the response: detect, contain, communicate, remediate, prevent. Output: three governance controls each team would put in place on Monday.

Module 8: Planning, Investing, and No-Code Enablement

• From use case to project: scope, owner, success criteria, kill switch
• Buy, configure, build, or no-code
• What no-code AI does today: a grounded assistant, workflow automation, drafting, analysis
• The limits of no-code and the signals you now need engineering
• What AI actually costs: licensing, usage, integration, data work, change management
• The business case: value hypothesis, cost, risk, time to value, measurement plan
• Vendor evaluation questions that separate substance from a demo
• Pilot design: small, time-boxed, measurable, with a defined decision

Exercise 8b, Build Your Own Grounded Assistant: Every participant builds a custom assistant grounded on their own documents, in minutes, with no programming, after the instructor demonstrates it live.

Exercise 8, the Business Case: Teams take their Day 1 quick win and build the one-page case: problem, approach, value, cost, risk, controls, and the 90-day pilot.

Closing: Pitch and Challenge

• Five-minute initiative pitch per team
• Investment committee challenge checklist across all eight modules
• Personal 30/60/90-day action plan

Pitch and Challenge: Each team pitches its AI initiative in five minutes. The room acts as the investment committee, using a challenge checklist drawn from all eight modules: Is this a real use case? What happens when it is wrong? Who owns it? What does it cost? How will you know it worked? What would make you stop?