AI Mindset Workshop
Developing an AI-First Perspective for Problem Solving and Innovation
Duration
1 Day
Level
Basic to Intermediate Level
Design and Tailor this course
As per your team needs
Overview
The AI Mindset Workshop is an 8-hour intensive session designed to shift participants’ perspective from viewing AI as a complex technical hurdle to seeing it as a strategic thought partner. This workshop bridges the gap between basic data science concepts and practical, daily application. Participants will learn a problem-solving framework that prioritizes human-AI collaboration, enabling them to identify automation opportunities and leverage no-code tools to drive efficiency and creativity in their professional roles.
Audience
- Business Leaders & Managers: To identify strategic AI opportunities within their teams.
- Non-Technical Professionals: Seeking to improve daily productivity using AI-powered tools.
- Innovation Leads: Looking to foster an “AI-First” culture within their organization.
- Problem Solvers: Individuals wanting a structured framework for data-driven decision-making.
Prerequisites
- Technical Level: No prior coding or data science experience is required.
- Equipment: Access to a laptop or mobile device with internet connectivity.
Curriculum
- Introduction to Data Science: Definition, scope, and the Data Science Lifecycle.
- Artificial Intelligence (AI): Evolution and types (Narrow, General, and Super AI).
- Machine Learning (ML): Understanding Supervised, Unsupervised, and Reinforcement Learning.
- Real-World Impact: Case studies across various industries.
- Moving from “Can AI do this?” to “How can AI help me do this better?”
- Recognizing AI as a tool for enhancing human creativity and judgment.
- Understanding the synergy between human intuition and machine processing.
- Recognizing repetitive tasks and data-heavy decisions.
- Evaluating tasks based on automation potential and efficiency gains.
- Analyzing patterns: Why AI excels at scale compared to human observation.
- Everyday Examples: AI in navigation, communication (autocomplete), and healthcare.
- Data: Why quality is the primary driver of AI success.
- Models: Understanding different AI types (chatbots, image recognition, predictors).
- Training vs. Inference: How AI learns from data vs. how it applies knowledge.
- Human-AI Collaboration: The necessity of human oversight and “Human-in-the-loop.”
- Step A: Define the Problem – Identifying the core challenge.
- Step B: Break it Down – Translating business hurdles into data-driven problems.
- Step C: Explore Solutions – Evaluating existing tools vs. custom AI needs.
- Step D: Ethics and Bias – Ensuring fairness, transparency, and responsibility.
- Step E: Test and Iterate – The experimental nature of AI solution improvement.
- Text-Based AI: Deep dive into ChatGPT and prompt experimentation.
- Visual/Audio AI: Exploring Teachable Machine for classification.
- Integrated AI: Utilizing Microsoft Copilot for office productivity.
- Experimentation: Identifying the unique strengths and weaknesses of each tool.
- Using AI for idea generation, brainstorming, and creative insights.
- Decision Support: Using AI to analyze market trends and risk profiles.
- Creativity Enhancement: AI as a catalyst for art, writing, and storytelling.
Duration
1 Day
Level
Basic to Intermediate Level
Design and Tailor this course
As per your team needs
