What is AI? What Marketers Need to Know

Download Now: How to Use AI in Content Marketing Guide
Erica Santiago
Erica Santiago



Artificial intelligence is getting a lot of traction in the marketing world. According to Statista, 80% of industry experts integrate some form of AI into their online marketing activities.

A digital brain overlays a set of data and lines, representing AI

However, if you're like me and are unfamiliar with AI beyond what you've seen in science fiction stories like I, Robot or Black Mirror — you're probably wondering what AI is and how to use it in marketing.

Is AI really what it looks like in the movies? This article will explore the definition of AI, the different types of AI, and how AI can improve marketing processes.

What is artificial intelligence?

How does AI work?

What are the four types of artificial intelligence?

How Marketers Can Use AI

The Pros and Cons of AI

The Future of AI in Marketing

Get Started with HubSpot's AI Campaign Assistant

So now you know what AI is, let's explore how it functions.

How does AI work?

AI combines large sets of data with intelligent, repetitive processing algorithms to learn from patterns and features within the data being analyzed. The AI continuously processes and learns from the data.

Within each round of data processing, the AI system tests and measures its own performance to gain additional expertise.

AI can run through thousands, even millions, of tasks repeatedly — improving its performance in a short amount of time. However, there are multiple kinds of AI, each with its capabilities and limitations.

What are the four types of artificial intelligence?

The four types of artificial intelligence are reactive, limited memory, theory of mind, and self-awareness.


A reactive AI can only use its intelligence to react and reply to the world around it. It can't store memory; therefore, it can't rely on past experiences to inform real-time decision-making or problem-solving.

Reactive machines can only complete a finite amount of specialized tasks. Though this may sound like a drawback, it has its perks. A reactive AI will react the same way to the same stimuli every time — making it reliable and trustworthy.

One of the most famous examples of reactive AI is Deep Blue, a supercomputer created by IMB in the 1990s that won a chess match against chess champion Garry Kasparov. Deep Blue could identify the chess board pieces and how each piece could move based on the game's rules.

However, the AI could not try to anticipate its opponent's next move, nor could it think of ways to put its piece in a better position.

Limited Memory

Limited memory AI stores previous data and predictions and uses it for decision-making — looking into past data to predict the future. Limited memory AI is when a machine learning model is continuously trained to analyze and use new data.

Limited-memory AI consists of six steps to follow.

  1. Create the training data.
  2. Create the machine learning model.
  3. Enable the model to make predictions.
  4. Have the model receive human or environmental feedback.
  5. Store the feedback as data.
  6. Repeat all the above steps in a cycle.

Examples of limited memory AI are self-driving cars. Self-driving cars identify civilians crossing the street, traffic signals, and other data to make better driving decisions and avoid future accidents.

Another example of limited memory AI is HubSpot's adaptive testing tool. The adaptive testing feature splits traffic evenly between page variations at first.

As HubSpot learns how these variations are performing, we adjust the traffic automatically, so optimal-performing variations are shown more than the poorly-performing ones.

Theory of Mind

Theory of mind AI is as it sounds — theoretical. AI has not yet advanced to this type, so theory of mind is still in its innovation stage. This type of AI interacts with the thoughts and emotions of humans. Theory of mind will better understand the entities they interact with so they can understand their needs, beliefs, feelings, and thought processes.

For example, we now know that self-driving cars are a form of limited mind AI. If these autonomous cars could analyze and understand their drivers' mental and emotional states to improve safety, they would evolve into Theory of Mind AI.


Once theory of mind is a reality, the next type of AI to emerge will be self-awareness. At this point, machines won't just be aware of humans' emotions and mental states — they'll also be aware of their own. A self-aware AI will have a human-like consciousness and understand its existence in the world and with others.

How Marketers Can Use AI

AI can perform parts of the marketing process — such as task automation, campaign personalization, and data analysis — so you can spend less on repetitive tasks and more on strategy.

For example, our HubSpot mobile app has a business card scanner that uses AI to pick out the name, email address, and other contact information on a business card and map them to your HubSpot properties. Instead of spending time manually entering this data yourself, the AI automates the process for you.

Screenshot of Hubspot's business card scanner, available on the HubSpot app; What is AI?


The Pros and Cons of AI

So, now you know what AI is, how it works, and the four types of AI — let's get into the pros and cons of AI technology.

The Pros of Artificial Intelligence

Fewer errors

Humans can make mistakes, miss deadlines, misspell words, and get the math wrong. Sometimes we're distracted or going through burnout; it's human nature. By implementing an AI-ran automated system, you're lessening the risk of errors.

24/7 Uptime

AI also doesn't need rest and can run 24/7. AI's can run constantly and consistently for as long as it's programmed to. This makes AI more ideal than humans for repetitive tasks, allowing marketers and business owners to focus their efforts elsewhere.

Can analyze large data sets quickly

As I mentioned earlier, humans sometimes make mistakes — especially when processing large data sets. A solution would be to work slower to prevent errors, but time is money in marketing.

Fortunately, AI machines can quickly process large amounts of information and data, making them more efficient than humans in a deadline crunch.

The Cons of Artificial Intelligence

Lack of creativity

AI is programmed to react to stimuli based solely on data from the past, meaning they're not currently suitable for creating innovative solutions. Data from the past can help predict future outcomes, but data alone isn't always enough to address a never-before-seen variable.

Therefore, AI would be better suited for "grunt" or mundane work. From a marketing perspective, humans can develop a creative marketing strategy, while AI can take care of the repetitive tasks that implement the plan.


Sometimes, human connection is the best way to forge a closer relationship with your audience. While a self-aware AI is possible in the future, current AI machines cannot perfectly mimic the human experience.

From a marketing perspective, implementing AI in every customer interaction can create a rift between you and your audience. I mean, there's a reason many of us can recall shouting, "speak to a representative!" when we're tired of speaking to a robot on the phone.

The Future of AI in Marketing

According to Grand View Research, the global AI market is expected to reach $1,811.8 billion by 2030, up from $136.6 billion in 2022.

Artificial intelligence, theory of mind, and self-aware machines all sound like things from a distant future. Still, the reality is AI is here now, and its impact across industries will likely grow in the years to come.

While AI has pros and cons, it's important marketers stay tuned in to its advancements and be open to using AI to streamline certain processes to keep up with competitors.

New Call-to-action


Related Articles

Learn how to use generative AI to scale your content operations.

    Marketing software that helps you drive revenue, save time and resources, and measure and optimize your investments — all on one easy-to-use platform