Marketing graduates understand strategic frameworks but lack practical ability to execute AI-augmented marketing work—they cannot conduct competitive intelligence at scale, generate and evaluate AI brand assets, orchestrate multi-channel campaigns with AI agents, or optimize performance using AI analytics. These are exactly the competencies employers now require, as 98% of marketers use AI (88% daily) while 66% of recent graduates lack workplace-ready practical experience.

Within 18 months of launch, undergraduate marketing students at partner business schools will complete this course with demonstrated AI collaboration competencies across market research, strategic planning, brand building, campaign execution, and performance analytics. Faculty integrate AI into their Marketing 101 courses using turnkey QuantHub modules that position AI as natural extensions of marketing concepts they already teach, meeting AACSB accreditation requirements for "current, relevant, forward-looking" curricula with "agility in emerging technologies."

By the end of this 16-week course, students will be able to:

• Orchestrate AI across the complete marketing process — Design AI-augmented workflows for market research, strategic planning, content creation, campaign execution, performance analysis, and customer engagement using conversational, multi-step, multi-agent, and semi-autonomous interaction patterns
• Select and deploy professional marketing AI tools — Choose appropriate AI tools across four tiers (general AI assistants, marketing platform AI, AI-native specialists, AI assistant extensions) based on marketing task requirements, data access needs, and strategic objectives
• Generate and critically evaluate AI-powered marketing outputs — Direct AI to produce strategic analysis, customer insights, brand content, campaign creative, and performance intelligence, then critically evaluate outputs for accuracy, brand alignment, strategic soundness, and ethical compliance
• Apply AI-specific ethical frameworks to marketing practice — Navigate ethical challenges unique to AI including algorithmic bias mitigation, synthetic content disclosure, manipulation boundaries in behavioral targeting, privacy in AI surveillance, and persuasion ethics in AI-optimized campaigns
• Engineer strategic context for professional AI outputs — Provide rich marketing context (brand guidelines, customer data, competitive intelligence, strategic objectives) enabling AI to generate brand-aligned, strategically relevant, professional-quality marketing work
• Optimize marketing performance using AI — Evaluate the use of AI for dynamic pricing, campaign budget allocation, performance optimization, demand forecasting, and multi-variable decision analysis across complex marketing scenarios

Marketing Functions

• Strategic Planning & Analysis (SWOT, competitive intelligence, PESTEL)
• Consumer & Market Research (sentiment analysis, behavioral prediction)
• Segmentation, Targeting & Positioning (customer clustering, AI personas)
• Product & Brand Strategy (visual asset generation, brand voice scaling)
• Services Marketing (conversational AI, multi-agent service)
• Pricing Strategy (dynamic pricing, elasticity modeling)
• Distribution & Supply Chain (demand forecasting, logistics optimization)
• Omnichannel & Retail (journey orchestration, real-time personalization)
• Integrated Marketing Communications (multi-agent campaigns, budget optimization)
• Advertising & Promotion (creative at scale, performance optimization)
• Digital Marketing Integration (MCP-enabled orchestration, ecosystem management)

AI Capabilities

• Conversational AI and iterative refinement
• Multi-step workflow orchestration
• Multi-agent coordination
• Semi-autonomous agents with agentic patterns
• MCP-enabled cross-platform orchestration
• Multimodal AI (text, images, video, audio, structured data)
• Seven AI task types: Research, Analysis, Synthesis, Generation, Optimization, Prediction, Automation

Ethics Integration

• Four AI-specific ethical principles (Transparency, Privacy, Fairness, Accountability)
• Domain-specific ethical applications across all marketing functions

This course is designed for undergraduate students concurrently enrolled in Marketing 101 courses at business schools. Students must have completed an AI Foundations course covering general AI concepts, capabilities, ethics, and basic prompting. No prior marketing or AI work experience is required.

Delivery Format

Online via QuantHub Upskill platform with:

• Interactive learning articles
• Scenario-based activities
• Formative assessment with gamification
• Task-based summative assessment
• Students work as strategic directors evaluating AI outputs simulated by QuantHub

Integration

Turnkey modules designed for easy integration with existing Marketing 101 courses. Faculty can adopt full course or select specific modules based on curriculum needs.

For Students

Graduates complete the course with demonstrated AI collaboration competencies that many of peers lack, positioning them for AI-augmented marketing roles where marketers now use AI daily. Students report immediate applicability to concurrent Marketing coursework, reinforcing both AI skills and marketing fundamentals.

For Institutions

The co-requisite model delivers improved student success rates compared to standalone courses. The curriculum directly addresses AACSB accreditation requirements for technology integration while providing a unique program differentiator for enrollment growth. Enhanced graduate placement rates result from students entering the job market with practical AI competencies employers actively seek.

For Faculty

AI is framed as a natural extension of marketing concepts you already teach—competitive analysis, consumer research, segmentation, content strategy. You continue teaching marketing while QuantHub modules provide AI-specific instruction. Complete instructor guides and comprehensive training are included, positioning you as a teaching innovator while improving student outcomes without requiring you to become an AI expert.