This unit takes students function by function through a modern organization, showing how AI is already embedded in day-to-day business operations. In marketing, students explore AI-driven content creation, social media automation, and customer segmentation. In finance, the focus shifts to fraud detection, financial forecasting, and automated expense analysis. The human resource management segment covers resume screening, employee engagement analytics, and recruitment chatbots, while the operations segment addresses inventory management, demand forecasting, and supply chain optimization. The unit closes with customer service applications — chatbots, virtual assistants, and personalized customer experience design. Topics: • Marketing — Content creation, social media automation, customer segmentation • Finance — Fraud detection, financial forecasting, expense analysis • Human Resources — Resume screening, employee engagement, recruitment chatbots • Operations — Inventory management, demand forecasting, supply chain optimization • Customer Service — Chatbots, virtual assistants, personalized customer experience

This unit builds the technical backbone of the course, introducing students to Python and R Studio as foundational tools for working with data and AI systems. Students begin with Python fundamentals — syntax, variables, data types, loops, and functions — before moving into essential libraries for data handling and basic analysis, such as Pandas and NumPy. The R Studio component introduces the R programming environment, basic statistical operations, and simple data visualization, giving students a comparative view of the two most widely used languages in data science and business analytics. The unit is designed to be accessible to non-programmers, including BBA and MBA students with no prior coding background, while giving BTech students a practical on-ramp to applying their programming skills in an AI and business analytics context. Topics: • Python — Installation & setup, syntax, variables, data types, loops, conditionals, functions • Python — Introduction to Pandas & NumPy; reading and cleaning simple datasets • R Studio — R environment and interface, basic data structures • R Studio — Simple statistical functions, basic data visualization (plots and charts)

This unit combines two closely related themes: practical fluency with generative AI tools, and the responsible mindset required to use them well. Students are introduced to a working toolkit of popular platforms and learn the fundamentals of prompt engineering, including role prompting, conceptual chain-of-thought prompting, prompt refinement, and reusable prompt templates. These skills are immediately applied to business tasks such as drafting emails, generating reports, building SWOT analyses, creating presentations, summarizing meetings, and building resumes. The unit then turns to the responsibilities that come with this capability: AI bias, hallucinations, copyright and intellectual property concerns, data privacy, responsible AI principles, emerging AI regulations, academic integrity, and the continued importance of human oversight. Topics: • GenAI Tools — ChatGPT, Microsoft Copilot, Google Gemini, Canva AI, Gamma AI, Perplexity AI, Grammarly AI, Notion AI • Prompt Engineering—Writing effective prompts, role prompting, chain-of-thought prompting (conceptual), refining prompts, prompt templates • Business Applications — Business emails, report generation, SWOT analysis, presentation creation, meeting summaries, resume building • AI-assisted structuring of assignments, theses, and reports • Ethics & Responsible Use — AI bias, hallucinations, copyright, data privacy, responsible AI, AI regulations, academic integrity, human oversight

The course culminates in a hands-on, project-based unit in which students apply everything learned to build a working digital product using AI-assisted development tools. Working individually or in small teams, students choose a live project — such as a simple mobile or web application, a browser-based game, or a functional website — and use AI tools to accelerate ideation, design, coding, content generation, and testing. The unit guides students through the practical workflow: defining the concept, using AI to generate or assist with code and design assets, iterating based on AI-suggested improvements, and troubleshooting with AI support. The unit concludes with a live presentation and viva, where students demonstrate their project, explain their AI-assisted workflow, and answer questions on the design and business or technical rationale behind their choices. Topics: • No-code / low-code track (BBA/MBA) — AI-generated business plan, AI-assisted market research, AI-powered marketing campaign concept, AI chatbot concept, AI-generated dashboard • Build track (BTech / technically confident)—Simple app using AI-assisted coding, browser-based game with AI code assistance, functional website with AI design & development tools