What We’ll Cover in Class: AI in Digital Advertising
Large Language Models (LLMs)
What it is:
We’ll explain what LLMs are and how they function as the interface layer on top of modern AI systems.
How it’s used:
We’ll cover how LLMs are used for planning, analysis, creative ideation, and workflow acceleration in advertising teams.
Who is using it (examples):
We’ll look at OpenAI and how ad platforms and companies are embedding LLMs into internal tools and copilots.
Why it matters:
We’ll show how LLMs lower the barrier to using AI across sales, marketing, and media roles.
Machine Learning (ML)
What it is:
We’ll define machine learning as predictive models trained on behavioral data to optimize decisions at scale.
How it’s used:
We’ll cover how ML powers targeting, bidding, pacing, and performance optimization in digital media.
Who is using it (examples):
We’ll examine how Meta, Google, and Amazon use ML to automate delivery and optimization.
Why it matters:
We’ll explain why ML controls most of the “who, when, and how much” in modern advertising.
Deep Learning
What it is:
We’ll explain deep learning as neural networks designed to understand complex inputs like video, audio, and images.
How it’s used:
We’ll cover how deep learning enables content recognition, brand suitability, fraud detection, and contextual understanding.
Who is using it (examples):
We’ll look at how Disney, Netflix, and YouTube apply deep learning to video, ads, and measurement.
Why it matters:
We’ll show how deep learning determines where ads appear and what content they run next to.
Generative AI
What it is:
We’ll define generative AI as systems that create new text, images, video, and audio.
How it’s used:
We’ll cover creative generation, personalization, localization, and self-service ad creation.
Who is using it (examples):
We’ll examine Amazon, Rembrandt, and Paramount Ads.
Why it matters:
We’ll explain how generative AI dramatically increases creative scale while changing how ads are produced.
Agentic AI
What it is:
We’ll define agentic AI as autonomous systems that plan, decide, and act toward defined goals.
How it’s used:
We’ll cover automated campaign execution, budget allocation, and continuous optimization.
Who is using it (examples):
We’ll look at AdCP, Swivel, and Scope3.
Why it matters:
We’ll show how agentic AI shifts advertising from human-managed to goal-driven automation.
Using AI in Your Day-to-Day Job
What it is:
We’ll translate AI concepts into practical workflows for non-technical roles.
How it’s used:
We’ll cover planning, creative development, performance analysis, sales prep, and reporting.
Who is using it (examples):
We’ll reference real-world usage across sales, marketing, media, and leadership teams.
Why it matters:
We’ll focus on how AI saves time, improves decisions, and increases individual productivity.
Wrap-Up
What it is:
We’ll connect all AI types into a single advertising ecosystem.
How it’s used:
We’ll discuss integration, governance, and human-in-the-loop control.
Who is using it (examples):
We’ll reference leading platforms and advertisers adopting AI responsibly.
Why it matters:
We’ll outline how AI will continue reshaping roles, workflows, and competitive advantage.