AI & Future of Work

Why This Module?

“AI won’t replace you. A person using AI will replace you.” - Unknown

In 2026 and beyond, AI literacy is not optional. This module covers:

• How to work productively WITH AI
• What AI can and cannot do
• How work is changing
• Skills that remain human

Connection: You’ve built leadership, strategy, and sustainability skills. Now understand the technological context you’ll apply them in.

Learning Objectives

By the end of this module, you will:

• Use AI effectively for work and learning
• Understand AI capabilities and limitations
• Navigate AI’s impact on work
• Develop “human-in-the-loop” skills

Week 1-2: Working With AI

Co-Intelligence: Living and Working with AI - Ethan Mollick

Rating: Essential | Wharton Professor | 2024

The Core Idea:

AI is not a tool like previous technologies. It’s more like an alien co-worker - with alien strengths, weaknesses, and ways of thinking.

The 4 Rules for Working with AI:

Rule 1: Always Invite AI to the Table

• Use AI for everything (at first)
• Discover what works and what doesn’t

Rule 2: Be the Human in the Loop

• AI generates, you evaluate
• Check outputs critically
• You are responsible for the result

Rule 3: Treat AI Like an Unpredictable, Smart Intern

• Capable but inconsistent
• Great ideas mixed with nonsense
• Needs supervision and direction

Rule 4: Assume This Is the Worst AI You’ll Ever Use

• Today’s limitations are temporary
• Build habits that scale

What AI is Good At:

• Generating first drafts
• Brainstorming options
• Summarizing information
• Pattern recognition
• Code and technical tasks
• Explaining concepts

What AI is Bad At:

• Factual accuracy (hallucinations)
• Up-to-date information
• Understanding YOUR context
• Emotional intelligence
• Original creative vision
• Ethical judgment

The Jagged Frontier:

• AI is brilliant at some things, terrible at others
• The pattern isn’t obvious
• You must explore to find the edges

Free Alternative: Ethan Mollick’s Substack “One Useful Thing”

Week 2-3: The AI Revolution Context

The Thinking Machine - Stephen Witt

Rating: Recommended | Journalism | 2025

The inside story of Nvidia and Jensen Huang - understanding where AI came from and where it’s going.

Related: “Chip War” by Chris Miller (broader semiconductor context)

Week 3-4: Practical AI Skills

AI for Everyone - Andrew Ng (Free Course)

Rating: Essential | Free | 4-6 hours

What You’ll Learn:

• What AI can (and cannot) do
• How to spot AI opportunities
• How to work with AI teams
• Navigating ethical/societal issues

Link: https://www.coursera.org/learn/ai-for-everyone

Key AI Concepts for Leaders

Understanding AI Types

Prompting Skills

Basic Prompting:

• Be specific about what you want
• Provide context
• Give examples of good output
• Iterate and refine

Advanced Prompting:

• Role assignment (“You are an expert in…”)
• Chain-of-thought (“Think step by step”)
• Multi-turn conversations
• Structured outputs

AI Ethics for Leaders

Key Questions:

• Whose data trained this AI?
• Who benefits and who might be harmed?
• What biases might be present?
• What should humans always decide?

Guidelines:

• Always verify factual claims
• Don’t automate decisions requiring human judgment
• Disclose AI use when appropriate
• Consider impact on jobs and people
• Maintain human oversight

The Human Skills That Remain

Despite AI advances, these skills remain human:

The Meta-Skill: Learning how to learn (Phase 0!) remains crucial.

Practical AI Applications

For Your MBA Studies

• Summarize books (AFTER reading)
• Generate quiz questions
• Create flashcards
• Explain difficult concepts
• Role-play scenarios

For Work

• Draft first versions
• Analyze data
• Prepare for meetings
• Create presentations
• Brainstorm options

For Leadership

• Prepare for difficult conversations
• Draft communications
• Analyze feedback
• Create training materials

2026 Update: Working in Agentic Organizations

This section adds context from 2025–2026 practice that goes beyond Mollick’s 2024 book. The book’s 4 Rules remain the right foundation; this update layers on what’s emerged since.

From Co-Pilot to Agent: The Shift Since 2024

By mid-2026, “working with AI” has expanded from individual productivity to orchestrating agents — AI systems that plan, use tools, and act across multiple steps without a human in the loop for each one. Key patterns:

• Centaur/Cyborg (Mollick’s 2024 framing) → still the dominant personal workflow.
• Agentic workflows: Software agents handle multi-step tasks autonomously (research → draft → format → file). Human role shifts to task-setting, review, and approving irreversible actions.
• Human+agent teams: Routine monitoring, drafting, and analysis are increasingly agent-run; judgment, relationships, and strategy remain human.

The “Least Agency” principle (2026): Give an AI agent only the permissions needed for its specific task. Never grant write access to budgets, data, or communications without a human approval gate. This is the successor to “least privilege” in traditional security, now extended to AI agents.

Spec-Driven Development (SDD): A Leadership Skill

As AI agents become capable of writing code and executing complex tasks, the highest-leverage skill becomes specifying work clearly — not just prompting, but writing a structured spec:

1. Goals — what success looks like
2. Constraints — what the agent must not do
3. Acceptance criteria — how you’ll verify it’s done

This pattern emerged in 2025 as the antidote to “vibe-coding” (asking AI to build something without a spec and hoping for the best). By 2026, every major AI coding tool (GitHub Copilot Workspace, AWS Kiro, Cursor, Claude Code) ships with some version of SDD.

Why it matters for leaders: SDD is a general management skill, not just a technical one. A leader who can write a clear spec for an agent is a leader who can think rigorously about any project. Source: Martin Fowler, Exploring Gen AI — SDD (2026); Microsoft Developer Blog, Spec-Driven Development.

Agent Security: What Leaders Need to Know (2026)

The OWASP organization published the official Top 10 for Agentic Applications 2026 — the governing standard for AI agent security. The risks that matter most to leaders:

Source: OWASP Top 10 for Agentic Applications 2026.

Context Engineering: The New Prompting

In 2026, the conversation has moved from “prompt engineering” (individual prompts) to context engineering — the art of deciding what information an AI agent has access to, in what structure, at what time.

Practical principle (from 2026 consensus): Curated context beats information dumps. An agent with 10 relevant paragraphs outperforms one with 10,000 random ones. Leaders who design AI workflows should think: “What does this agent need to know, and what should it NOT have access to?”

Source: Sourcegraph, Context Engineering (2026).

TED Talks & Videos

Interactive Tools

AI Assistants to Practice With

AI Learning Tools

AI Creation Tools

Documentaries & Video Content

YouTube Deep Dives

Netflix / Streaming

Educational Series

Newsletters

Recommended Podcasts

AI Learning Integration

For AI Practice

"Help me practice using you effectively.
Give me 5 different tasks to try with AI.
After I attempt each, evaluate my prompting technique
and suggest improvements."

For Creating AI Guidelines

"Help me create personal AI ethics guidelines.
Ask me questions about:
- What I'll use AI for
- What I won't delegate to AI
- How I'll verify AI outputs
Then help me write my guidelines."

Phase 7 Checklist

Week 1

• Read “Co-Intelligence” (first half)
• Applied the 4 rules to your AI use
• Experimented with AI for 5 different tasks

Week 2

• Finished “Co-Intelligence”
• Identified 3 ways AI could help your work
• Tried advanced prompting techniques

Week 3

• Completed AI for Everyone course
• Developed personal AI use guidelines
• Watched TED Talks

Week 4

• Practiced AI for MBA review (flashcards, quizzes)
• Identified human skills to double down on
• Created personal AI ethics guidelines
• Completed reflection questions

Reflection Questions

1. Which of the 4 rules (Mollick) is hardest for you to follow? Why?

2. Experiment: Use AI for 5 different tasks this week. What surprised you?

3. What tasks in your work could AI help with? What should stay human?

4. Your AI ethics: What will you never delegate to AI?

5. As AI improves, which of your skills become MORE valuable, not less?

Your MBA Journey: Complete

Congratulations! You’ve completed the core curriculum:

PHASE 0: Learned How to Learn
PHASE 1: Understood Yourself
PHASE 2: Understood Others
PHASE 3: Learned to Lead
PHASE 4: Navigated Conflict
PHASE 5: Built & Created
PHASE 6: Sustained & Thrived
PHASE 7: Prepared for AI Future

What’s Next?

• Extensions: Explore Tier 1-4 for specialized depth
• Practice: Apply what you’ve learned
• Review: Use spaced repetition to maintain knowledge
• Teach: Share with others to deepen understanding

Final Reflection

1. What were your top 5 insights from this entire journey?

2. How has your thinking changed about leadership?

3. What will you DO differently as a result?

4. What do you want to learn next?

5. How will you maintain and grow what you’ve learned?

Vela stastia, Zuzka! You’re ready for whatever comes next.

I'm studying AI & Future of Work (Phase 07 of my MBA program).

Act as a Socratic tutor - don't give me direct answers. Instead, ask me questions to help me discover insights about these concepts: AI Collaboration, AI Literacy, AI Ethics, Future-Proof Skills.

Start by asking what I already know about one of these topics, then guide me deeper with follow-up questions. Challenge my assumptions when appropriate.

After each of my responses, either:
1. Ask a deeper follow-up question
2. Point out a gap in my reasoning
3. Connect my answer to another concept

Let's begin.

Quiz me on AI & Future of Work. Ask 10 questions covering: AI Collaboration, AI Literacy, AI Ethics, Future-Proof Skills.

Rules:
- Mix question types (multiple choice, short answer, scenario-based)
- Start easier, get progressively harder
- After each answer, tell me if I'm right or wrong and explain why
- Keep a running score
- At the end, summarize what I know well vs. need to review

Ask the first question now.

Help me apply Mollick's 4 Rules for AI, Jagged Frontier Concept, Human-in-the-Loop, AI Ethics Guidelines, Prompt Engineering Techniques to a real situation in my life or work.

First, ask me to describe a recent challenge or decision I faced.

Then guide me through analyzing it using these frameworks:
- Which framework applies best?
- What would each framework reveal about the situation?
- What would I do differently knowing this?

Don't lecture - ask questions that help me discover the insights myself.

I want to practice case analysis for AI & Future of Work.

Give me a short business scenario (2-3 paragraphs) involving AI Collaboration, AI Literacy, AI Ethics, Future-Proof Skills.

Then ask me:
1. What's the core problem?
2. Which frameworks from AI & Future of Work apply?
3. What biases might cloud judgment here?
4. What would you recommend?

After each answer, push back on my reasoning before moving to the next question.

I'm studying AI & Future of Work and need to understand these concepts deeply: AI Collaboration, AI Literacy, AI Ethics, Future-Proof Skills.

For each concept, ask me to explain it in simple terms (as if to a child).

If my explanation is unclear or wrong, don't correct me directly. Instead:
1. Ask clarifying questions
2. Give me a scenario that tests my understanding
3. Help me refine my explanation

The Feynman technique says if you can't explain it simply, you don't understand it well enough.