Subject Overview

This subject examines how artificial intelligence creates business value through prediction, automation, personalization, optimization and decision support. It integrates AI foundations, data and models, enterprise applications, implementation, managerial decision-making, responsible AI, governance and emerging developments.

Learning Goal

Students will be able to identify valuable AI opportunities, explain and evaluate AI models, manage AI projects, interpret analytics, support evidence-based decisions, assess organizational impacts and implement AI responsibly in contemporary business environments.

Syllabus

1. Foundations of Artificial Intelligence in Business

Learning Objective: Explain AI terminology, history, symbolic and data-driven approaches, intelligent agents, machine learning categories, capabilities, limitations and the distinction between AI, analytics and automation.

Category: foundation

2. AI Strategy and Business Value

Learning Objective: Connect AI initiatives to business strategy using value chains, resource-based and capability perspectives, use-case portfolios, feasibility-impact analysis, business cases and competitive advantage.

Category: strategic

3. Data Foundations and the AI Project Lifecycle

Learning Objective: Examine data types, quality, labeling, pipelines, feature engineering and governance while applying CRISP-DM and iterative AI lifecycles from problem definition through monitoring.

Category: foundation

4. Supervised Learning and Predictive Business Analytics

Learning Objective: Understand regression, classification, decision trees, ensembles and neural networks; assess training, validation, overfitting, bias-variance trade-offs and business-oriented performance metrics.

Category: core

5. Unsupervised Learning, Segmentation and Recommendation

Learning Objective: Apply clustering, dimensionality reduction, anomaly detection, association analysis and recommender systems to customer, operational, fraud and market problems.

Category: core

6. Deep Learning and AI Perception

Learning Objective: Explain deep neural networks, computer vision, speech recognition and multimodal AI, including their enterprise applications, data requirements, costs and limitations.

Category: advanced

7. Natural Language Processing and Generative AI

Learning Objective: Evaluate NLP, foundation models, large language models, prompting, embeddings, retrieval-augmented generation, fine-tuning and AI agents for business communication and knowledge work.

Category: contemporary

8. Decision Intelligence, Optimization and Human–AI Collaboration

Learning Objective: Integrate predictive and prescriptive analytics, optimization, simulation, rules and causal reasoning while addressing bounded rationality, automation bias and effective human oversight.

Category: core

9. Enterprise AI Applications and Intelligent Automation

Learning Objective: Analyze AI applications across marketing, finance, accounting, operations, supply chains, human resources, customer service and risk management, including robotic process automation.

Category: application

10. AI Technology Architecture, Deployment and MLOps

Learning Objective: Explain cloud AI, APIs, data platforms, model serving, vector databases, build-versus-buy choices, MLOps, LLMOps, monitoring, scalability, cybersecurity and legacy-system integration.

Category: application

11. AI Evaluation, Experimentation and Value Measurement

Learning Objective: Use technical, financial and operational metrics; A/B testing, causal evaluation, cost-benefit analysis and KPI frameworks to assess model effectiveness, adoption and return on AI investment.

Category: application

12. Responsible and Explainable AI

Learning Objective: Evaluate fairness, transparency, explainability, privacy, accountability, safety, intellectual property, sustainability and social impacts using responsible-AI principles and assessment tools.

Category: core

13. AI Governance, Risk and Regulation

Learning Objective: Design governance structures, policies, audit controls, model inventories and risk-management processes aligned with data protection, emerging AI regulation and recognized governance frameworks.

Category: strategic

14. AI Transformation, Leadership and Future Developments

Learning Objective: Plan enterprise AI transformation through operating models, change management, workforce reskilling and innovation portfolios while critically evaluating autonomous agents, synthetic data, edge AI and future business models.

Category: contemporary