This title slide introduces AI, GenAI, and Agentic AI as the core topics. Its subtitle emphasizes how human judgment delivers real advantage, progressing from curiosity → thinking → execution → accountability.

AI, GenAI & Agentic AI

How Human Judgment Creates Real Advantage From curiosity → thinking → execution → accountability

Source: Presentation Title Slide

This "RESET THE ROOM" slide sets expectations by stating it's not a standard AI training session, with no demos, tool comparisons, hype, or fear. It emphasizes how AI changes thinking in our work and amplifies expertise rather than replacing it.

RESET THE ROOM

• Not a standard AI training session
• No demos, tool comparisons, hype or fear
• How AI changes thinking in our work
• AI amplifies expertise—doesn't replace it

AI is pattern learning at scale, deriving correlations from massive data to enable predictions, classifications, and recommendations. It excels in speed, scale, and consistency but lacks meaning, intent, judgment, or context.

WHAT IS AI (FOUNDATION)

• AI = Pattern Learning at Scale
• Learns correlations from massive data
• Powers predictions, classifications, recommendations
• Strengths: speed, scale, consistency
• Limits: no meaning, intent, judgment, context

The slide contrasts Traditional AI, which predicts outcomes, solves specific problems, and prioritizes accuracy, with Generative AI, which creates possibilities like language, code, and images while optimizing for plausibility. This two-column layout highlights their core differences under the title "TRADITIONAL AI vs GENERATIVE AI."

TRADITIONAL AI vs GENERATIVE AI

| Predicts outcomes Solves specific problems Optimized for accuracy | Generates possibilities (language/code/images) Optimized for plausibility |

Source: AI, GenAI & Agentic AI — How Human Judgment Creates Real Advantage

Behind the scenes, language models break text into subword tokens and predict the next token based on patterns from training data. They repeat learned correlations, optimizing for plausibility over truth.

WHAT’S HAPPENING BEHIND THE SCENES

• Breaks text into tokens (subword units)
• Predicts next token based on patterns
• Repeats correlations from training data
• Optimizes for plausibility, not truth

Hallucinations in GenAI are a deliberate design trade-off. They stem from missing context triggering pattern-filling, underspecified questions inviting assumptions, data limits for high precision, and prioritizing plausibility over facts.

WHY HALLUCINATIONS HAPPEN

• • Hallucinations: deliberate design trade-off
• • Missing context triggers pattern-filling
• • Underspecified questions invite assumptions
• • High-precision needs exceed data limits
• • GenAI prioritizes plausibility over facts

The slide presents a four-phase workflow for using GenAI intelligently: Start Simple by posing basic queries, Add Context with details and iteration, Challenge Itself via self-critique prompts, and Validate through fact-checking. Each phase details the AI's approach alongside the human's role, like clear framing, conversational refinement, reflection prompts, and judgment application.

USING GENAI INTELLIGENTLY

{ "headers": [ "Phase", "Approach", "Human Role" ], "rows": [ [ "1. Start Simple", "Pose a basic question or idea", "Frame the initial query clearly" ], [ "2. Add Context", "Provide relevant details and background", "Refine based on initial output; iterate conversationally" ], [ "3. Challenge Itself", "Ask AI to critique or improve its response", "Prompt self-reflection: 'What assumptions? Alternatives?'" ], [ "4. Validate", "Cross-check facts, logic, and relevance", "Apply judgment: verify sources, test applicability" ] ] }

Source: Treat AI as thinking partner: Start simple → add context → challenge itself → validate.

Prompt engineering structures prompts using role, context, and constraints. It's effective communication—like a conversation starter—rather than magic tricks or strict commands.

WHAT IS PROMPT ENGINEERING?

• Structure with role, context, constraints
• Communication > magic tricks
• Prompt = conversation starter
• Not a strict command

The slide titled "Why Prompt Engineering is Overrated" criticizes it for being excessive in one-shot prompts, replacing human thinking, and overemphasizing clever wording. It promotes modern LLMs' strength in conversation, where context, iteration, and good thinking outperform fancy prompts.

WHY PROMPT ENGINEERING IS OVERRATED

• Overrated for one-shot prompts
• Replaces human thinking
• Overemphasizes clever wording
• Modern LLMs thrive on conversation
• Context and iteration outperform
• Good thinking beats clever prompts

The slide "A BETTER MENTAL MODEL" promotes thinking in iterations rather than single instructions for effective AI use. It recommends gradually adding context, challenging assumptions, validating outputs, and improving AI through human engagement.

A BETTER MENTAL MODEL

• Think in iterations, not instructions
• Add context gradually
• Challenge assumptions
• Validate outputs
• AI improves with human engagement

The slide titled "Does Saying 'Please' Help AI?" quotes Dr. Alex Rivera stating that politeness doesn't flatter emotionless AI but enhances clarity, context, and nuance for superior responses. Dr. Rivera is Principal AI Strategist at Forrester Research.

Does Saying “Please” Help AI?

> AI feels no emotions, so 'please' doesn't flatter it. Yet politeness weaves in clarity, context, and nuance—patterns that sharpen communication and unlock superior responses.

— Dr. Alex Rivera, Principal AI Strategist, Forrester Research

Source: AI, GenAI & Agentic AI Presentation

The slide "HUMAN–AI COLLABORATION" contrasts AI strengths—speed in processing vast data, efficient synthesis, pattern detection at scale, and rapid option generation—with human strengths. Humans excel in framing problems clearly, wise judgment of nuances, ensuring ethical alignment, full accountability, and choosing wisely.

HUMAN–AI COLLABORATION

| Speed: Processes vast data instantly. Synthesis: Combines insights efficiently. Patterns: Detects correlations at scale.

Generates options rapidly. | Framing: Defines problems clearly. Judgment: Evaluates nuances wisely. Ethics: Ensures moral alignment. Accountability: Owns decisions fully.

Chooses wisely. |

This timeline traces the evolution from Generative AI in 2022, which emerged with idea generation and token prediction, to Agentic AI in 2024+ that autonomously orchestrates workflows under human guardrails. Intermediate milestones include tool-calling agents in early 2023 for API integration and basic actions, plus planning and reasoning in mid-2023 for goal decomposition and self-correction.

FROM GENAI TO AGENTIC AI

2022: GenAI: Thinking Emerges Generates ideas, predicts next tokens, excels at synthesis. Early 2023: Tool-Calling Agents Integrates APIs, performs basic actions beyond generation. Mid 2023: Planning & Reasoning Decomposes goals into steps, self-corrects errors. 2024+: Agentic AI Executes Orchestrates workflows/tools under human guardrails autonomously.

Agentic AI removes manual coordination and repetitive tasks, freeing humans for judgment, prioritization, and leadership. This shift enables more AI execution alongside increased human governance, preserving human value.

AGENTIC AI ≠ LOSS OF HUMAN VALUE

• Removes manual coordination & repetitive tasks
• Frees time for judgment, prioritization, leadership
• More AI execution → more human governance

Source: Presentation: AI, GenAI & Agentic AI — How Human Judgment Creates Real Advantage

The conclusion slide, titled "FINAL CLOSE," defines "Advantage Creators" as thinkers with curiosity, domain depth, judgment, and AI leverage. Its subtitle states that AI accelerates thinking, while humans own decisions.

FINAL CLOSE

Advantage Creators:

Thinkers with curiosity, domain depth, judgment, AI leverage.

AI accelerates thinking. Humans own decisions.

Source: AI, GenAI & Agentic AI — How Human Judgment Creates Real Advantage