Proposal: AI Foundations in Marketing

AI Foundations

in Marketing

This foundational course develops students' understanding of how AI is transforming marketing practice across the complete marketing lifecycle. Students learn to analyze AI capabilities, evaluate collaboration patterns, understand ethical frameworks, and assess organizational readiness for AI-augmented marketing through case analysis and conceptual exploration.

www.quanthub.com

Course Overview

Learning Approach: Students examine how professional marketers work with AI through case analysis and conceptual exploration. The focus is on understanding AI's impact, evaluating frameworks, and building literacy—not hands-on implementation.

👥 Target Audience

Undergraduate Business Students

⏱️ Duration

16 hours (16 weeks, ~1 hour/week)

📚 Platform

QuantHub Upskill

📋 Co-requisite

Marketing 101

Course Outcomes & Content

Key Course Objectives

By the end of this course, learners will be able to:

Explain the seven core AI capabilities (Analyze, Generate, Reason, Remember, Learn, Plan, Use Tools) and how they are applied across marketing workflows
Describe the impact of AI on each stage of the marketing process (UNDERSTAND, DESIGN, CONSTRUCT, ENGAGE, CAPTURE) including benefits, risks, and human expertise requirements
Compare six human-AI collaboration patterns from basic assistance through autonomous execution and identify appropriate patterns for different marketing contexts
Evaluate context engineering strategies including the Four Pillars, Six Methods, and Four Context Layers that differentiate AI marketing performance
Distinguish among AI technology categories (general assistants, platform AI, AI-native tools) and describe strategic technology stack decisions
Analyze AI-specific ethical frameworks including bias mitigation, synthetic content disclosure, and regulatory compliance (EU AI Act, FTC guidelines, GDPR)
Describe how marketers apply critical thinking when working with AI, including quality assurance protocols and systematic evaluation methods
Identify organizational AI maturity levels and explain capability gaps preventing successful AI transformation

Topics Covered

Topic Area	Content
🎯 AI Capabilities	Seven core AI capabilities (Analyze, Generate, Reason, Remember, Learn, Plan, Use Tools), models vs. assistants vs. agents
📊 Marketing Process	AI impact across UNDERSTAND, DESIGN, CONSTRUCT, ENGAGE, and CAPTURE stages of marketing lifecycle
🤝 Collaboration Patterns	Six human-AI partnership patterns (Assistant, Conversational Partner, Co-Creator, Orchestrator, Agent, Executor)
🔧 Context Engineering	Four Context Layers, Four Pillars, Six Methods including RAG, Few-Shot Learning, and Dynamic APIs
🛠️ Technology Stack	General assistants, platform AI, AI-native tools, and Model Context Protocol (MCP) for orchestration
⚖️ Ethics & Compliance	Algorithmic bias, manipulation risks, synthetic content, privacy, accountability, EU AI Act, FTC guidelines, GDPR
🧠 Critical Thinking	Critical Thinking Paradox, collaboration models, quality assurance protocols, AI Sandwich method
🏢 Transformation	Four AI maturity levels, Adoption Paradox, organizational capability development, strategic roadmap

Delivery & Logistics

Aspect	Details
⏱️ Duration	16 hours total (16 weeks, approximately 1 hour per week)
📱 Platform	QuantHub Upskill with LMS integration capabilities
🎓 Prerequisites	Marketing 101 (co-requisite)
👥 Target Audience	Undergraduate business students

Course Structure

The course follows a progressive learning sequence across 16 chapters:

Chapter 1 - Foundations (8 modules): Comprehensive introduction to AI capabilities, marketing impact, collaboration patterns, context engineering, technology landscape, ethics, critical thinking, and organizational transformation
Chapters 2-16 (15 case-based modules): Application-focused case analysis examining AI's impact on specific marketing domains including strategy, consumer behavior, segmentation, product development, pricing, distribution, communications, and digital marketing
Learning Sequence: Foundational knowledge in Chapter 1 provides frameworks and concepts that students apply through case analysis in subsequent chapters
Pedagogical Approach: Conceptual learning focused on understanding AI's transformational impact rather than hands-on technical implementation

Course Modules

The course is organized into 16 chapters. Chapter 1 provides comprehensive foundational knowledge across 8 modules, establishing the conceptual frameworks students will apply throughout the course. Chapters 2-16 are application-focused, using case analysis to examine how AI transforms specific marketing domains.

Note: Below are the detailed specifications for Chapter 1: AI in Marketing Foundations. Subsequent chapters follow a case-based learning approach examining AI's impact across the marketing discipline.

Understanding AI Capabilities in Marketing Context

Module 1.1

This module introduces the seven core AI capabilities that power modern marketing applications. Students learn how AI systems Analyze data, Generate content, Reason through problems, Remember context, Learn from experience, Plan actions, and Use Tools. Understanding these fundamental capabilities provides the foundation for evaluating AI's role in marketing workflows.

Learning Objectives

Identify the seven core AI capabilities (Analyze, Generate, Reason, Remember, Learn, Plan, Use Tools) and describe their functions
Explain the differences between models, assistants, and agents based on their capability combinations
Recognize marketing examples that leverage different AI capabilities individually and in coordination

AI's Impact on the Marketing Process

Module 1.2

This module examines how AI transforms each stage of the marketing process lifecycle. Students explore AI's impact on UNDERSTAND (market analysis), DESIGN (strategy development), CONSTRUCT (content creation), ENGAGE (customer interaction), and CAPTURE (measurement) phases. The module emphasizes both the opportunities and limitations of AI augmentation in marketing workflows.

Learning Objectives

Describe how AI transforms tasks across each marketing process stage (UNDERSTAND, DESIGN, CONSTRUCT, ENGAGE, CAPTURE)
Explain the evolution of the marketer's role from tactical executor to strategic orchestrator
Compare the benefits and risks of AI adoption in marketing workflows
Identify limitations, failure modes, and scenarios requiring human expertise

Patterns of Human-AI Partnership

Module 1.3

This module explores the spectrum of human-AI collaboration patterns in marketing practice. Students examine six partnership models ranging from AI as basic Assistant through Conversational Partner, Co-Creator, Orchestrator, Agent, to fully autonomous Executor. Understanding these patterns helps marketers select appropriate collaboration approaches for different tasks and organizational contexts.

Learning Objectives

Describe the six patterns of human-AI partnership and their characteristics (Assistant, Conversational Partner, Co-Creator, Orchestrator, Agent, Executor)
Identify marketing interactions with AI at different levels of autonomy
Explain the relationship between interaction patterns and organizational AI maturity

Context Engineering Fundamentals

Module 1.4

This module introduces context engineering as the critical practice that differentiates high-performing AI marketing systems. Students learn the Four Context Layers (Brand, Customer, Market, Campaign) that provide AI with necessary marketing knowledge, the Four Pillars (Tools/APIs, System Prompts, Knowledge Bases, Memory) of infrastructure, and the Six Methods (RAG, AI Knowledge Bases, Few-Shot Learning, Dynamic APIs, Persona Crafting, Memory Curation) for implementation.

Learning Objectives

Explain the role of context engineering in improving AI marketing effectiveness
Identify the Four Context Layers (Brand, Customer, Market, Campaign) and their characteristics
Describe the Four Pillars (Tools/APIs, System Prompts, Knowledge Bases, Memory) and Six Methods (RAG, AI Knowledge Bases, Few-Shot Learning, Dynamic APIs, Persona Crafting, Memory Curation) of context engineering

The AI Marketing Technology Landscape

Module 1.5

This module provides a framework for understanding the three categories of AI marketing technologies. Students learn to distinguish between general AI assistants (ChatGPT, Claude), platform AI (built into marketing tools like HubSpot, Salesforce), and AI-native tools (purpose-built AI applications). The module also introduces the Model Context Protocol (MCP) and its role in orchestrating multi-tool AI workflows.

Learning Objectives

Identify the three categories of AI marketing technologies (general assistants, platform AI, AI-native tools) and their characteristics
Compare features and capabilities of general AI assistants, platform AI, and AI-native tools for marketing applications
Describe strategic technology stack decisions based on organizational needs and use cases
Explain the role of Model Context Protocol (MCP) in AI marketing orchestration

Ethical Frameworks for AI Marketing

Module 1.6

This module examines the ethical challenges and frameworks essential for responsible AI marketing practice. Students learn to identify five principal ethical risks (algorithmic bias, manipulation, synthetic content, privacy violations, accountability gaps) and apply three ethical frameworks (deontology, consequentialism, virtue ethics) to evaluate marketing decisions. The module covers governance systems including HITL protocols and regulatory compliance requirements under the EU AI Act, FTC guidelines, and GDPR.

Learning Objectives

Identify five principal ethical risks in AI marketing (algorithmic bias, manipulation, synthetic content, privacy violations, accountability gaps)
Explain three ethical frameworks (deontology, consequentialism, virtue ethics) used to evaluate AI marketing decisions
Describe governance systems to prevent harm including Human-in-the-Loop (HITL) protocols and bias audits
Identify compliance requirements under current regulations (EU AI Act, California ADMT, FTC guidelines, GDPR)

Critical Thinking for AI Collaboration

Module 1.7

This module addresses the Critical Thinking Paradox: as AI makes task execution easier, the quality of outputs becomes more dependent on human critical evaluation. Students explore three strategic collaboration models (Augmented Creativity, Hybrid Decision Systems, Oversight-Driven Automation) and learn quality assurance protocols including the "AI Sandwich" method and five-point editorial checklist. The module emphasizes competency development across four clusters: Strategic AI Integration, AI Technical Literacy, Core Marketing Fundamentals, and Human-Centric Skills.

Learning Objectives

Explain the Critical Thinking Paradox and its impact on AI marketing output quality
Describe three strategic collaboration models (Augmented Creativity, Hybrid Decision Systems, Oversight-Driven Automation) for different marketing tasks
Identify quality assurance protocols including the "AI Sandwich" method and five-point editorial checklist
Describe competency development across four clusters (Strategic AI Integration, AI Technical Literacy, Core Marketing Fundamentals, Human-Centric Skills)

Organizational AI Transformation

Module 1.8

This module examines organizational AI transformation and the Adoption Paradox: 80% of organizations achieve high AI adoption rates but see no EBIT impact. Students learn to assess organizational AI maturity across four levels (Nascent, Emerging, Advanced, Leading-Edge) and identify capability gaps that prevent transformation success. The module provides a three-phase strategic roadmap for building organizational AI capability and describes the evolution of marketing roles from tactical execution to strategic orchestration.

Learning Objectives

Explain the Adoption Paradox and why 80% of organizations achieve high adoption but no EBIT impact
Identify the four levels of organizational AI maturity (Nascent, Emerging, Advanced, Leading-Edge) and their characteristics
Describe the evolution of marketing roles from tactical executor to strategic orchestrator
Outline the three-phase strategic roadmap for building organizational AI capability

Subsequent Chapters: Case-Based Learning

Following the foundational knowledge established in Chapter 1, students apply these frameworks through case analysis across 15 marketing domains:

Chapter	Focus Area
Chapter 2	The Impact of AI on Marketing Strategy & Planning
Chapter 3	The Impact of AI on Marketing Environment Analysis
Chapter 4	AI Ethics & Social Responsibility in Marketing
Chapter 5	The Impact of AI on Consumer Behavior Analysis
Chapter 6	The Impact of AI on B2B Marketing & Market Research
Chapter 7	The Impact of AI on Segmentation, Targeting, & Positioning
Chapter 8	The Impact of AI on Product Strategy & Branding
Chapter 9	The Impact of AI on New Product Development
Chapter 10	The Impact of AI on Services Marketing
Chapter 11	The Impact of AI on Pricing Strategy
Chapter 12	The Impact of AI on Distribution & Supply Chain
Chapter 13	The Impact of AI on Retailing & Omnichannel
Chapter 14	The Impact of AI on Integrated Marketing Communications
Chapter 15	The Impact of AI on Advertising & Promotion
Chapter 16	Digital Marketing

Pedagogical Approach: Each chapter presents real-world case studies where students analyze how AI transforms specific marketing functions. Students apply the frameworks from Chapter 1 to evaluate AI implementation decisions, collaboration patterns, ethical considerations, and organizational capabilities in diverse marketing contexts.