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Module 1 Business Basics*

Q1 How does AI enhance traditional management practices?

Q2 Can AI replace human managers?

Q3 Why are SWOT and PESTLE analyses still relevant?

Q4 How can small businesses implement AI effectively?

Q5 What skills are essential for managers in an AI-integrated environment?

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Module 2: What is AI and Why It Matters in Business*

Q1 What’s the key difference between AI, ML, and DL?

Q2 How can startups use AI affordably?

Q3 Is AI only useful for tech firms?

Q4 Is data privacy a concern with AI?

Q5 What are the top tools for AI in business?

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Module 3 Top AI Tools Every Business Manager Should Know *

Q1 How do AI tools improve productivity in business management?

Q2 What’s the cost of using AI tools in business management?

Q3 Can AI replace human decision-making?

Q4 What are the security concerns when using AI tools?

Q5 Can AI tools help with customer service?

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Module 4 Strategic Decision-Making with AI Insights *

Q1 : Can AI replace human strategic thinking in SWOT?

Q2 : How often should AI update SWOT analysis?

Q3 : How can AI detect hidden risks?

Q4 Can startups use AI for SWOT effectively?

Q5 How can I measure ROI?

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Module 5 Smart Ads, Smarter Results: How AI Is Revolutionizing Marketing *

Q1 Do I need coding skills to use AI in marketing?

Q2 How can AI improve my existing marketing campaigns?

Q3 Is AI effective for small businesses?

Q4 What kind of data does AI need?

Q5 Can AI replace human marketers?

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Module 6 Operations & Supply Chain with AI*

Q1 How does AI improve inventory forecasting accuracy compared to traditional methods?

Q2 Is AI cost-effective and accessible for small and medium-sized businesses, or is it only for large enterprises?

Q3 What is the typical ROI and payback period for implementing AI in logistics and supply chain management?

Q4 How long does it typically take to implement AI in operations and supply chain management?

Q5 Will AI replace human jobs in supply chains, or will it augment human capabilities?

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Module 7 Financial Intelligence AI in Budgeting and Forecasting *

Q1 Explain how AI algorithms analyze historical financial data and identify patterns to create more accurate budgets.

Q2 Describe how AI can generate more dynamic and responsive financial forecasts by considering a wider range of variables.

Q3 Explain the role of AI in algorithmic trading and how it analyzes market data to make investment decisions.

Q4 Explain how AI can automate the collection, processing, and reporting of financial data, improving efficiency and accuracy.

Q5 Discuss the importance of understanding the "black box" nature of some AI algorithms in financial decision-making and the need for transparency.

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Module 8 AI in HR – Smarter Hiring & Employee Management *

Q1 What is resume parsing

Q2 How do resume parsing tools work?

Q3 Can resume parsers reduce bias in hiring?

Q4 How accurate are AI predictions of employee churn?

Q5 How does AI reduce bias in performance appraisals?

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Module 9 - AI Ethics, Data Privacy Legal Risks in Business *

Q1 Discuss the fundamental ethical principles that should guide the development and deployment of AI in business.

Q2 Explain how data privacy regulations like GDPR and CCPA impact the way businesses can use AI and the data it relies on.

Q3 Describe specific legal risks associated with the use of AI, such as discriminatory outcomes resulting from biased algorithms.

Q4 Discuss the importance of explainability and transparency in AI systems, particularly those used for decision-making that affects individuals.

Q5 Discuss the challenges in regulating AI effectively and the implications for businesses operating in this evolving legal landscap

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Module 10 Build a Business Strategy with AI*

Q1 Describe a framework that businesses can use to identify strategic opportunities where AI can provide a significant competitive advantage.

Q2 Explain the key steps involved in integrating AI initiatives into a company's overarching business strategy.

Q3 Discuss the importance of defining clear, measurable objectives and key performance indicators (KPIs) for AI-driven strategic initiatives.

Q4 Explain how to assess the technical feasibility, potential return on investment (ROI), and alignment with strategic goals for different AI projects.

Q5 Discuss strategies for fostering a data-driven culture within an organization and ensuring employees have the skills needed to work with AI.

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