📅 Day 1: Understanding Generative AI and Strategic Opportunities
- Introduction to Generative AI and its professional applications
- Role of Generative AI in research, academia, and industry
- Identifying high-impact AI use cases for productivity and innovation
- Understanding opportunities, risks, and limitations of Generative AI
🛠️ Hands-on Activity:
Use Google Colab/Jupyter Notebook to analyze sample researcher/publication data and identify possible Generative AI use cases for academic and professional workflows.
📅 Day 2: Designing Generative AI Workflows and Implementation Plans
- Building effective Generative AI workflows for content, research, and decision support
- Prompt engineering for structured and reliable outputs
- Selecting suitable AI tools and platforms for different professional needs
- Creating an AI adoption framework for teams and organizations
🛠️ Hands-on Activity:
Use Google Colab/Jupyter Notebook to design prompt-based workflows for summarizing researcher profiles, extracting key research areas, and generating structured professional insights.
📅 Day 3: Executing Generative AI Strategy Responsibly
- Developing a practical Generative AI execution roadmap
- Measuring success: productivity, quality, efficiency, and innovation impact
- Responsible AI practices: privacy, ethics, accuracy, bias, and compliance
- Final strategy discussion: moving from AI concept to real-world implementation
🛠️ Hands-on Activity:
Use Google Colab/Jupyter Notebook to create a basic Generative AI strategy template, including use case selection, workflow planning, risk assessment, and execution roadmap.
