Artificial Intelligence for Business Course

Differentiate between types of AI (narrow, general, and agentic), and explain how AI differs from automation and where it fits in modern business workflows.

What is AI?
"Definitions: AI vs automation
How AI mimics human cognition (classification, prediction, generation)
AI vs traditional programming (rules vs learning)
Professional examples (AI writing assistants, analytics bots, email sorting)"

Types of AI “Narrow AI (ANI), General AI (AGI), Agentic AI"

A Brief History of AI
"Symbolic AI (GOFAI), rule-based systems
Rise of ML: pattern recognition from data
Deep learning & transformers: unlocking scale
2023+ transition to agents and orchestration" 

Why AI Now?
"Exponential growth in model capabilities
Open APIs and cloud infrastructure
Foundation models as general-purpose interfaces
Business democratization (LLMs for non-developers)"

Business Context of AI
"Where AI creates value (efficiency, insights, automation, scale)
Use cases by domain
Limitations: trust, control, adaptability"

Analyse the role of data in AI performance, and evaluate how data quality, labelling, privacy, and bias influence model outcomes.

Data as the foundation of AI
The Shift Toward Data-Centric AI
Understanding Data Types for AI
Data Quality Principles 

Data Labeling and Annotation
Labeling & Annotation for ML
Feature Engineering for Traditional ML
Data Privacy and Governance
Bias in Datasets
From Data to Model Inputs

Evaluate AI use cases across business domains and assess the risks, limitations, and decision-support roles of AI systems in real-world environments. 

Domain-Specific AI Case Studies
Mapping AI Use Cases Across Industries
What Makes a Good AI Use Case?

Operational Limits of AI
The Role of AI in Decision-Making
Decision support vs automation

Barriers to Real-World AI Deployment
Organizational / Technical  / Human (Resistance to AI, overreliance, poor UX design)

Mapping AI Opportunities

"What is Machine Learning?
Key ML Concepts (Without the Math)
Supervised learning
Unsupervised learning
Reinforcement learning"

The ML Workflow
Tools for Building Models
Model Evaluation & Metrics
When Not to Build Your Own Model

Explain how foundation models are developed and accessed, and apply prompting and retrieval techniques to real-world business use cases involving text and image generation.

• What are foundation models?
• Capabilities and Use Cases
• Prompt Engineering
• Pretraining, Fine-tuning, and Adaptation
• RAG (and grounded RAG)
• Accessing Foundation Models via APIs 

Lesson 6: Agentic AI Systems
Differentiate between passive and agentic AI models, and construct simple agent workflows using foundation models with planning, tool use, and memory components.

• What is an Agent?
• Anatomy of an Agent
• Orchestration Patterns
• Key Frameworks for Building Agents
• Agent Tools and Integrations
• Risks, Limitations, and Governance

Design and implement orchestrated AI workflows that combine foundation models, APIs, tools, and low-code platforms to automate real-world business tasks.

• What is Workflow Orchestration?
• What Is AI Workflow Integration?
• Core Building Blocks of an AI Workflow
• Low-Code/No-Code Orchestration Tools
• Chaining & Memory with LLMs
• Workflow Design for Real Use Cases
• Deployment & Monitoring Considerations

Lesson 8 Ethics and Responsible AI
Design AI systems that incorporate user feedback, build trust, and support effective human-AI collaboration through transparent, adaptive interfaces and interaction loops.

Understanding Ethical Risks in AI
Fairness, Transparency & Accountability
Regulatory Landscape
Responsible AI in Practice
Organisational Governance & Risk Mitigation

Design AI systems that incorporate user feedback, build trust, and support effective human-AI collaboration through transparent, adaptive interfaces and interaction loops.

• What is Human-Centered AI?
• Human-AI Collaboration Patterns
• Interface and Interaction Design
• Building Feedback Loops
• Trust-Building Strategies in AI

Lesson 10: Deploying & Scaling AI Systems
Evaluate infrastructure options, deployment patterns, and cost-performance trade-offs involved in scaling AI systems into production.

• What Does “Deployment” Mean for AI?
• Hosting Options – Cloud, Local, Edge
• MLOps for Foundation Models
• Inference Optimization & Scaling
• Cost Control & Resource Management
• Cost Control Strategies

Capstone Overview & Expectations
Design and develop a domain-specific AI solution that demonstrates practical integration of foundation models or agentic systems using tools, workflows, and deployment strategies learned throughout the course.

• Group or Individual Project Planning
• Independent Build Time with Support
• Checkpoint Review & Peer Feedback

Lesson 12: Capstone Projects and Presentations
Communicate and reflect on AI solutions they developed, articulating key implementation choices, responsible AI considerations, and real-world applicability of the systems they designed.

Final Presentations
Each individual or team presents for 5 minutes, followed by 2 minutes Q&A

• Peer Review & Reflection
  Participants complete a Peer Review Form for at least two other presentations
• Course Review & Closing Discussion
  "Key takeaways from the full course
• -Foundation models
• -Agentic workflows
• -Integration and orchestration
• -Ethics and compliance
• Recap of real-world AI maturity: POC → pilot → production
• “What’s next?” discussion"
• Final Activity (Optional)
• Write a brief “AI Integration Plan” for your team or company