This project aims to examine how Generative AI can automate the complete research workflow, from data collection to reporting. It focuses on improving efficiency, reducing manual effort, and enhancing the accuracy of insights by integrating AI tools into research processes across business environments.
Study the traditional research workflow, including problem definition, data collection, analysis, and reporting.
Identify inefficiencies in manual research processes such as time delays and data inconsistencies.
Understand the fundamentals of Generative AI and its role in business analytics.
Explore AI tools for automating tasks like literature review, summarization, and report generation.
Map the existing research workflow in an organization or case study.
Identify stages where automation using GenAI can be implemented effectively.
Design an end-to-end automated workflow integrating AI tools.
Develop prompts for AI tools to extract insights and generate summaries.
Conduct a comparative study between manual and AI-driven workflows.
Measure performance metrics such as time saved, cost reduction, and accuracy improvement.
Use Excel or Power BI to visualize workflow efficiency and outcomes.
Evaluate risks such as data privacy, bias, and dependency on AI tools.
Create a prototype or demonstration of the automated research workflow.
Provide recommendations for implementing AI-driven research automation in organizations.
