Artificial intelligence (AI): The Digital Marketer’s Guide

Artificial intelligence (AI) refers to machine “thinking” that simulates human intelligence. To be precise, AI in the purest sense — technology that perfectly mimics the functioning of the human brain — is still just fantasy (or rather, Sci-fi): What we call AI today can only work with the information it is fed.

Even if the intelligence of current AI isn’t human, though, it does have the ability to make itself smarter, which is still inarguably impressive. Machines based on AI technology can process enormous amounts of data with tremendous speed, which is why they’re so adept at pattern recognition, language learning, and complex decision-making. They’ve already begun transforming many fields, from medicine to agriculture to education. In the performance marketing world, AI tools have already proven adept at identifying high-intent users, adapting in real-time to user behavior, and optimizing their journey through the conversion funnel.

How Does AI Work?

In the simplest terms, AI is a union of volume, speed, and algorithmic logic. It involves crunching tons of data, spotting patterns, and making decisions. Here’s how an AI system usually works:

• It takes in and metabolizes huge amounts of data. For example, in 2024, Meta fed hundreds of thousands of books into its AI models.
• It uses machine learning (ML) to pick out trends or connections. (More on that below).
• It uses neural networks — technology that mimics the human brain’s ability to recognize patterns — to make decisions like suggesting or automatically taking actions based on what it’s learned.
• It keeps getting smarter as it processes more data over time.

Tools like ChatGPT and DeepSeek are known as Generative AI tools, so named because they can generate new text, images, and videos. The “wow” factor — their ability to have normal, human-sounding conversations — comes from the Large Language Models (LLMs) on which they’re based.

Types of AI

Narrow AI (Weak AI)

Of the three types of AI discussed in this section, Narrow AI is the only one that exists in the real world. Narrow AI is built to perform a single job: These systems are tightly programmed for their task and can’t step outside those parameters. One can find numerous use cases for narrow AI in the fields of e-commerce and digital marketing:

• Amazon and Netflix product recommendations: When you shop on Amazon and see suggestions like, “People who bought this self-cleaning litter box also bought this cat litter,” that’s Narrow AI at work. The recommendation engine crunches your browsing history and purchase data, as well as the purchases of other users whose buying history resembles your own.
• Smart Bidding: Google Ads uses Narrow AI for Smart Bidding. It analyzes data such as who’s clicking your ads, when, and on what device, then automatically adjusts your bids to get the most conversions within your budget.

General AI (Strong AI)

Think of Ava, the main android character in the movie Ex Machina: that’s an example of General AI, bordering on Superintelligent AI. General AI, sometimes known as Strong AI, is hypothetical at this point, but it’s the holy grail of artificial intelligence. Machines driven by General AI don’t just follow narrow scripts, but think and adapt across all sorts of tasks, including writing fiction, problem solving, and offering advice in a way that surpasses its training. In theory, these would be as versatile as the human brain. Researchers are making progress, but we’re currently nowhere near machines that can match the full range of what the human brain can do.

Superintelligent AI

Dystopian depictions of sentient AI machines that want to be autonomous from humans are generally referring to Superintelligent AI machines. Examples would be the setting of William Gibson’s 1984 sci-fi novel Neuromancer. This technology is not real, but if it were, machines built on its principles would leave human intelligence in the dust. Ethical questions would arise about whether they are capable of good and evil. Superintelligent AI could lead to what is known as singularity — a hypothetical scenario where artificial intelligence can autonomously improve upon itself without human intervention and advance uncontrollably.

AI Models

Machine Learning Models

Machine learning models are trained to recognize patterns and make decisions without being explicitly programmed for every scenario. They can be supervised (trained on labeled data), unsupervised (finding hidden patterns in unlabeled data), or reinforced (learning through reward-based systems).

An example of machine learning is a spam email filter. It uses a learning algorithm, like a decision tree or neural network, trained on a dataset of emails labeled as “spam” or “not spam.” The model learns patterns, such as specific words or phrases, to classify new emails. For instance, if an email contains “Earn $$$$,” the model might flag it as spam based on the parameters of its programming. Over time, user feedback — marking emails as spam or not — refines the model’s accuracy.

Deep Learning Models

Deep learning, a subset of machine learning, uses neural networks with many layers (hence “deep”) to analyze various factors of data. It’s particularly effective in tasks like image recognition, language processing, and fraud detection.

An example of deep learning is the facial recognition feature on your smartphone, which analyzes and identifies facial features and compares them against the countless other facial images in its database. Over time, with more input, the feature “learns” to recognize faces with increasing speed and accuracy.

Large Language Models (LLM)

Large language models (LLM) are a type of neural network, trained on vast amounts of text data to understand and generate human-like language.

An example of a large language model is GPT-4, developed by OpenAI. This technology serves as the basis for ChatGPT and similar generative AI tools. So, if you’re using an AI-driven performance marketing tool that takes advantage of LLM, you can enter prompts that generate natural-sounding copy for email campaigns, calls to action (CTAs), and social media.

Natural Language Processing Models (NLP)

NLP models allow machines to understand, interpret, and generate human language. Tools like chatbots, translation services, and voice-activated assistants rely heavily on NLP. In marketing, NLP can dynamically tailor messaging based on user sentiment or interaction history.

Benefits of AI

Enhanced Efficiency

AI dramatically reduces the time needed to analyze data, draw insights, and make decisions. For example, performance marketers using AI-enhanced platforms can automatically optimize campaigns at scale, saving resources while maximizing return on investment (ROI).

Personalization at Scale

AI enables highly personalized experiences for users. By analyzing behavior patterns and preferences, AI can serve custom content and product recommendations — a strategy exemplified by platforms that leverage predictive audience targeting based on first-party data.

Real-Time Optimization

Modern AI-based tools, such as those for marketers, enable real-time adjustments based on user actions. These tools make it possible to adapt to user behavior instantly, driving better outcomes.

Challenges and Risks

Bias and Fairness

AI systems can perpetuate or even amplify existing biases in the data they are trained on. This could lead to discriminatory outcomes in hiring, lending, or advertising. Developers must remain vigilant about training data and continuously monitor outputs for fairness.

In a 2023 experiment with GPT-3 API technology that came to be known as “AI Seinfeld,” Twitch ran a 24/7 channel devoted to a fake sitcom called “Nothing, Forever,” which ran AI-generated episodes of a show that appeared to be based on the 90s sitcom Seinfeld. The scripts for these visually clunky episodes were auto-generated by the bot, which did not contain sufficient content filters for offensive material. Unfortunately, the chatbot started to spew highly offensive content that was not culled from any real Seinfeld episode, nor was it based on malicious user input: It had processed “comedy” material from the internet at large. That’s why any AI-generated content you use needs to first undergo thorough human vetting.

Privacy and Security

As AI systems rely on data, ensuring user privacy and securing data from breaches is a major challenge. Companies working with proprietary first-party data must implement strong encryption, anonymization, and compliance practices.

Job Displacement

This aspect of AI scares people, but its scope has been exaggerated. Automation powered by AI might replace certain jobs, particularly in industries reliant on repetitive tasks. However, it also creates opportunities by enabling new roles focused on AI development, oversight, and integration. Mastering AI-based tools for your own industry is a great way to stay ahead of the pack.

Use Cases of AI

Marketing and Advertising

AI is revolutionizing how brands engage with consumers. From predictive targeting to real-time creative optimization, AI-driven tools help advertisers find high-intent users and drive them through the performance funnel. This new wave of AI goes beyond email blasts and social media strategies, offering scalable, measurable performance outcomes.

Healthcare

AI assists in diagnostics, personalized medicine, and treatment recommendations. Machine learning algorithms can predict disease outbreaks and aid in drug discovery at speeds previously unimaginable.

Agriculture

Machine learning models forecast crop yields based on historical data, weather patterns, and field conditions. Using drone images, they can identify crop diseases and pests, thereby allowing for early intervention.

Financial Services

Banks use AI for fraud detection, risk assessment, and personalized banking experiences. AI can rapidly sift through millions of transactions to identify suspicious patterns.

Autonomous Vehicles

Self-driving cars rely on AI to interpret sensory information, navigate roads, and avoid obstacles, representing one of the most advanced applications of real-time decision-making AI.

Key Takeaways

Artificial intelligence simulates human intelligence and can adapt based on new data. Modern AI thrives on data ingestion, pattern recognition, and predictive analytics. There are different types of AI, from narrow to theoretical superintelligent systems. AI brings efficiency, personalization, and real-time optimization — especially powerful in performance marketing. Challenges include bias, privacy concerns, and job displacement. AI use cases are transforming industries, from advertising to healthcare.

Frequently Asked Questions (FAQs)

What is the history of AI?

The concept of artificial intelligence dates back to ancient myths of mechanical beings. One of the earliest AI creations was the so-called Mechanical Turk: In 1770, a Hungarian inventor named Wolfgang von Kempelen wowed the crowned heads and illuminati of Europe by demonstrating his invention, a life-size humanoid automaton wearing a turban that could play master-level chess against humans: A sentient machine. The human player would make their move, then the robot would take his, with the pieces gliding around the board seemingly by themselves.

Due to the Orientalist, vaguely Ottoman costume — the automaton resembled Zoltar, the fortune-telling mechanical man from the movie Big — the machine came to be called the Mechanical Turk. It defeated nearly everyone, including, allegedly, Benjamin Franklin. One might even say it was the world’s first example of artificial intelligence. Unfortunately, it was a hoax and was operated by a chess player hiding behind a false panel in the contraption.

Modern AI began in the 1950s with pioneers like Alan Turing and John McCarthy. Early enthusiasm led to periods of stagnation known as “AI winters,” but advancements in computing power and data availability in the 21st century have brought AI to the forefront of innovation.

Are there ethical implications with AI?

Absolutely. AI can reinforce biases, infringe on privacy, and make opaque decisions with serious consequences. Ethical AI development emphasizes transparency, fairness, accountability, and the minimization of harm. Marketers, for instance, must ensure their targeting practices respect user consent and avoid discrimination.

Strong AI vs. Weak AI: What’s the Difference?

Weak AI (or Narrow AI) is designed for specific tasks, like recommending products or filtering emails. Strong AI would theoretically possess consciousness and general reasoning abilities across a broad range of activities, akin to human intelligence. Currently, all deployed AI systems are forms of Weak AI.