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Module 6 Overview: Artificial Intelligence (AI) in Business

Manager’s Briefing: What Is Artificial Intelligence?

Types of Artificial Intelligence for Business Applications

Machine Learning Essentials for Managers

Applying AI and ML in Business: A Practical Playbook

Case Studies and Metrics: From Pilot to Scaled Impact

Knowledge Check: Module 6 Quizzes

Quiz 1: Foundations of AI in Business

Quiz 2: Types of AI and When to Use Them

Quiz 3: Machine Learning Concepts and Metrics

Quiz 4: Applying AI and ML—Use Cases and ROI

Quiz 5: Risk, Governance, and Responsible AI

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Module 6 Survey: Learning Effectiveness and Relevance

AI and Machine Learning for Business Transformation is a practical, manager-focused course designed to help you turn data and AI into measurable business value. In clear, non-technical language, you’ll learn what AI is (and isn’t), how it differs from traditional software, and where it delivers the greatest impact across revenue growth, cost efficiency, risk management, and customer experience. You’ll explore essential AI capabilities—natural language processing, computer vision, forecasting, optimization, and autonomous decision support—and understand their strengths, limitations, and risks, including data quality, bias, explainability, and regulatory considerations. The course demystifies machine learning fundamentals and MLOps so you can assess feasibility, choose the right approach, and govern models responsibly at scale. Through a step-by-step playbook and concise case studies from retail, manufacturing, and financial services, you’ll learn how to identify high-value use cases, build a minimal viable dataset, decide between build versus buy, and move from pilot to enterprise-grade deployment. By the end, you’ll be ready to prioritize AI initiatives, align stakeholders, define success metrics, and accelerate adoption while managing compliance and ethics—turning AI into a sustainable competitive advantage for your organization.

Module 6: Artificial Intelligence (AI) in Business

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            <p>Artificial intelligence and machine learning have moved from experimental pilots to core levers of business transformation. This course equips managers with the clarity and practical tools to translate AI into measurable outcomes—revenue growth, cost efficiency, risk reduction, and superior customer experiences. You will learn what AI is in business terms, how it differs from traditional software, and which capabilities—such as natural language processing, computer vision, forecasting, optimization, and autonomous decision support—map to your strategic priorities. The curriculum addresses both value and vigilance: how to harness personalization, automation, and predictive insights, while managing limitations around data quality, bias, explainability, integration complexity, and regulatory obligations.</p>
<p>The journey begins with a manager’s briefing on AI fundamentals and types of AI relevant to commercial applications, from supervised and reinforcement learning to generative and conversational systems. You will then build fluency in machine learning essentials: framing business problems, assembling and labeling data, selecting and evaluating models with metrics that align to outcomes (e.g., accuracy, precision/recall, ROC-AUC, MAE/MAPE), and instituting MLOps for governance, monitoring, and responsible AI. A practical playbook guides you through identifying and prioritizing high-value use cases—such as demand forecasting, churn prediction, dynamic pricing, preventive maintenance, underwriting, fraud detection, and customer support automation—while making sound build-versus-buy decisions and structuring cross-functional delivery from pilot to scale. Real-world case snapshots from retail, manufacturing, and financial services translate concepts into operational impact and value metrics, including revenue lift, cost-to-serve reduction, cycle-time improvement, risk loss avoided, and CSAT/NPS. Quizzes reinforce comprehension across foundations, AI types, ML concepts, application and ROI, and risk and governance, and a closing survey ensures continuous improvement of the learning experience.</p>
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