• Learning Objectives: Define AI and Robotics in simple terms; Recognize AI and robots in everyday life; Understand their relationship; Understand why automation is relevant to their job and the company; Bust common myths.
• Topics:
  • What is AI? (Simple definitions: AI as prediction machines, software brains, pattern finders). Examples include Netflix, Siri, and spam filters.
  • What is Robotics? (Simple definitions: Machines that SENSE, THINK/PLAN, and ACT in the physical world). Examples include Roomba vacuums, factory arms, and drones.
  • AI + Robotics = Intelligent Automation: How AI equips robots with their intelligence (e.g., navigation, object recognition, decision-making). Not all robots use advanced AI, but many modern ones do.
  • AI vs. Machine Learning vs. Deep Learning: (Simplified visual explanation remains).
  • Why is Automation Booming Now? (Ingredients: Big Data, Powerful Computers, Smart Algorithms, Better Sensors, Advanced Mechanics).
  • Myth vs. Reality: Addressing common fears/misconceptions about AI and robots (job loss, sentient robots, etc.).
  • The Big Picture: How AI and Robotics Are Transforming Industries Together.
• Activities:
  • Icebreaker: Share an example of an AI or a Robot you encountered today.
  • Group discussion: Initial thoughts or concerns about AI and robotics in the workplace.

• Learning Objectives: Understand that AI learns from data; Recognize data sources for AI and sensors for robots; Grasp the concept of “Garbage In, Garbage Out” for both.
• Topics:
  • Data: The Fuel for AI Brains: Why Data Is Essential (Volume, Variety, Quality). Examples include user clicks, sales figures, text, and images.
  • Sensors: The Senses for Robots – How robots perceive the world (Cameras, LiDAR, microphones, touch sensors). Relate sensor data back to AI processing.
  • “Garbage In, Garbage Out”: Importance of good data for AI and accurate sensors/calibration for robots.
  • How AI Learns (Simplified):
    • Learning from Examples (Supervised Learning): Teaching AI using labeled data (relevant for robot perception, such as identifying objects).
    • Finding Hidden Patterns (Unsupervised Learning): Discovering structure (relevant for anomaly detection in robot performance).
    • Reinforcement Learning: Learning through Trial and Error; Often used to train robots for complex tasks, such as walking and grasping.
  • Think Input -> AI/Robot System -> Output/Action: Focusing on what goes in and what comes out.
• Activities:
  • Analogy exercise: Match AI learning types and robot actions to tasks.

Brainstorm: Identify data sources and potential sensor inputs relevant to your department or industry.

• Learning Objectives: Identify specific AI (software) and Robotics (physical automation) applications relevant to different business functions; Analyze real-world case studies.
• Topics: (Tailor examples based on audience mix, explicitly distinguishing between software AI and physical robots where appropriate)
  • Marketing & Sales: AI for personalization, lead scoring, and sentiment analysis. Potential Robotics: Interactive kiosks (limited).
  • Finance & Operations: AI for fraud detection and process automation (RPA). Robotics: Warehouse automation (AMRs picking/sorting goods), robotic process automation for physical tasks, and quality inspection robots on production lines.
  • Logistics & Supply Chain: Utilizing AI for Route Optimization. Robotics: Delivery drones and robots, automated warehouse systems.
  • Human Resources: AI for resume screening and employee analytics. Potential Robotics: Telepresence robots for remote work integration (limited).
  • Product & Customer Service: AI chatbots, AI analytics. Robotics: Robotic assistants in retail/hospitality (often basic AI) and automated checkouts.
  • Healthcare: AI for diagnostics. Robotics: Surgical assistant robots, robotic exoskeletons, lab automation robots.
  • Cross-functional case Studies: Walk through 1-2 examples involving both AI software and robotic hardware.
• Activities:
  • Breakout groups: Brainstorm potential AI and Robotics use cases in their functional area.

Case study discussion: Analyze the benefits/challenges of a real-world automation project (AI or Robotics).

• Learning Objectives: Understand AI bias; Recognize the importance of fairness/transparency; Discuss ethical considerations specific to robots (safety, job displacement); Understand the human role.
• Topics:
  • When AI Goes Wrong: Understanding Bias (Remains crucial). How biased AI can unfairly control robots.
  • Robots in the World: Safety & Interaction: Physical safety protocols for human-robot collaboration, ensuring predictable behavior.
  • The Job Question: Discussing the impact of automation (both AI and Robotics) on tasks and roles – augmentation vs. replacement.
  • Ethical Dilemmas: Decision-making for autonomous systems (e.g., autonomous vehicles – briefly), data privacy from robot sensors (cameras, microphones).
  • Principles of Responsible Automation: Fairness, Transparency, Accountability, Privacy, Security, and Safety.
  • The Human Element: The Importance of Human Oversight, Control, and Collaboration with Automated Systems.
• Activities:
  • Scenario discussion: Analyze ethical dilemmas involving AI and robots in the workplace.

Debate: Discuss the pros and cons of introducing specific robotic automation into a hypothetical workplace.

• Learning Objectives: Feel empowered to identify AI and Robotics opportunities; Know how to collaborate with relevant teams; Understand where to learn more.
• Topics:
  • How to Spot Automation Opportunities: Look for repetitive tasks (cognitive or physical), prediction needs, pattern recognition, personalization, and physical interaction needs.
  • Speaking the Language: Key Questions to Ask Technical Teams (Including Hardware and Robotics Engineers).
  • Being an Automation Advocate: Championing the responsible use of AI and robotics.
  • The Future is Collaborative: Humans + AI + Robots working together.
  • Staying Curious: Resources for Non-Technical AI and Robotics News and Insights.
• Activities:
  • Action Plan: Write down one potential AI or Robotics idea or one question to ask.
  • Course wrap-up, final Q&A, and feedback.

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