Foundations of Analytics and Artificial Intelligence

Introduction to Artificial Intelligence: meaning, evolution, scope, and types of AI; AI vs traditional computing; overview of machine learning, deep learning, and data science; role of AI in business decision-making; introduction to business analytics (descriptive, diagnostic, predictive, prescriptive); data-driven decision-making; data lifecycle in organisations; ethical, legal, and managerial challenges of AI adoption.

Types of business data: structured and unstructured; introduction to data analytics tools for managers; types of charts for data visualisation; introduction to dashboards, and KPIs; Excel-based analytics fundamentals; correlation, trend analysis, and forecasting basics; interpretation of data for managerial decision-making.

Introduction to Generative AI and Large Language Models (LLMs); applications of AI tools in business; principles of prompt engineering; structure of effective prompts (role, context, instruction, format); iterative prompting techniques; use cases in marketing, HR, finance, and reporting; limitations including bias, hallucination, and data privacy risks.

AI in marketing (customer segmentation, recommendation systems, sentiment analysis); AI in finance (fraud detection, credit scoring, risk assessment); AI in HR (recruitment analytics, attrition prediction, engagement analysis); AI in operations and supply chain (demand forecasting, inventory optimisation, logistics automation); AI in customer relationship management (chatbots, personalisation); overview of Industry 4.0 and emerging trends in intelligent enterprises.

• AI in strategic management and digital transformation; competitive advantage through AI adoption; AI governance frameworks; ethical issues in AI (bias, fairness, transparency); data privacy and compliance; impact of AI on future of work; organisational readiness for AI transformation; responsible AI practices; emerging trends such as AI agents and autonomous decision systems. Practical Activities 1. Use spreadsheet software to create charts and dashboards from given datasets and interpret the findings for managerial decision-making. 2. Develop prompts for marketing campaign creation, HR job description drafting, and financial summary generation, and compare outputs across different prompt styles. 3. Identify AI applications in a selected company across marketing, finance, HR, and operations, and prepare a brief analytical report.
• 4. Analyse a given business problem and propose suitable AI-based solutions (e.g., customer churn prediction, demand forecasting, recruitment challenges).