AI technology can potentially transform the world, and it's so much more than some funny videos or pre-written blog posts. 

AI has a wide range of B2B and marketing applications, from chatbots to market analysis and even the creative arts. AI-powered systems can process and learn from large amounts of data to make decisions or perform tasks more efficiently than humans. But before looking at ways AI can benefit your business, let's get the foundations clear. 

What is AI? 

In simple terms, AI is the simulation of human intelligence processes by machines, especially computer systems. 

It involves the development of algorithms that can perform tasks that usually require human intelligence, such as:

✅ Visual perception

✅ Speech recognition

✅ Decision-making

✅ Language Translation. 

Are there different types of AI? 

As if this AI business weren't complicated enough, there are different types of AI, and they all have different capabilities. 

Reactive machines are the most basic form of AI, where machines can only react to specific inputs without any memory of past events. In other words, they cannot learn from previous experiences. Their responses are based on the information they receive at a given moment. They are often used in applications requiring quick and precise responses, such as robotics, gaming, and image recognition. 

One of the most well-known examples of a reactive machine AI is Deep Blue, the chess-playing computer developed by IBM. Deep Blue defeated world champion Garry Kasparov in a highly publicised match in 1997. 

But Deep Blue did not learn from its previous games or adapt its strategies based on its opponent's moves. Instead, it relied on its pre-programmed responses to each move made by Kasparov. If you want to know more about Deep Blue, you can find an interesting interview with Deep Blue AI expert Murray Campbell here. 

Another example of reactive machine AI that is more relevant in marketing is Siri, Apple's virtual assistant. Siri can understand and respond to voice commands. Still, it cannot learn from previous interactions with the user. Each response is based on the specific input it receives at a given moment. Curious about what Siri is capable of? Sunil Yadav has written a whole blog post about Siri. 

Limited memory AI 

A limited memory machine is an artificial intelligence (AI) that can store past experiences and use that information to make decisions.

Unlike reactive machines that can only react to specific inputs without any memory of past events, limited memory machines can learn from previous experiences and use that knowledge to improve their decision-making process. 

There are several examples of limited-memory AI being used in marketing. One example is chatbots, AI-powered tools that can communicate with customers and assist them with their inquiries. Chatbots typically have limited memory capacity and can only process a limited amount of data at a time.

Another example is programmatic advertising, which uses machine learning algorithms to automate the buying and selling of digital ads. While the traditional method includes requests for proposals, tenders, quotes, and negotiation, programmatic buying uses algorithmic software to buy and sell online display space. It uses traffic data and online display targeting to drive impressions at scale, which results in a better ROI for marketers.

With programmatic advertising, marketers also have more time for the optimisation improvement of ads to drive campaign success.

Alexa and Netflix 

You might already own a limited memory machine: Amazon's Alexa is an AI-powered device that uses natural language processing and machine learning algorithms to understand and respond to voice commands.

Alexa has limited memory of past interactions with users and can use that information to improve its responses over time.

For example, if a user asks Alexa to play a specific song or artist multiple times, Alexa will remember that preference and automatically play that the next time the user asks.

Additionally, Alexa can learn from the user's behaviour and make personalised recommendations based on that information.

Suppose a user frequently orders pizza through a food delivery app using Alexa. In that case, the device may suggest pizza delivery options in the future. That's a handy marketing tool. 

And then, there's Netflix. It uses machine learning algorithms to analyse users' viewing history and recommend series and films based on that information. It can use that information to improve its recommendations over time.

Theory of Mind AI

There also is a type of artificial intelligence (AI) that involves the ability to understand and interpret the beliefs, desires, and intentions of others. Theory of mind AI is critical in social interactions and can help machines interact more effectively with humans.

Take the social robot Pepper, developed by Softbank Robotics. Pepper was designed to interact with humans in a social context and can interpret human emotions and behaviours.

You might have seen his face online already- photos of him can be found on Unsplash and often decorate articles about AI (like this one).

If a person is sad or upset, Pepper can detect changes in their facial expressions and tone of voice and respond with appropriate words or actions to provide comfort.

Pepper can also interpret human gestures and body language and act on that information. But Pepper has also shown us the current limits of the Theory of Mind AI. 

The Scottish grocery chain Margiotta installed a Pepper in their flagship Edinburgh store, naming it Fabio. But he confused customers.

Fabio had difficulty understanding the questions he was asked due to the noise in the store. When customers began actively avoiding Fabio, he had to go. 

But Pepper was ultimately unsuccessful: In 2021, Softbank stopped manufacturing him. 

Self-aware AI 

Now it gets a little weird. This type of artificial intelligence (AI) has a sense of self-awareness and consciousness similar to human beings. This is a highly complex and advanced form of AI that is still in the realm of science fiction.

However, some examples of AI systems exhibit certain characteristics of self-awareness. Take the Cognitive Architecture for Space Exploration (CASE) system developed by NASA. 

CASE is an AI system that can monitor its own performance and respond to changing conditions.

For example, suppose CASE encounters a problem while navigating through space. In that case, it can diagnose the problem, generate a solution, and implement it without human intervention. The system can also learn from its experiences and improve its performance over time.

And then, there is Sophia. 

The humanoid robot was developed by Hanson Robotics and was first activated on February 14, 2016. She is modelled after Nefertiti, the ancient Egyptian queen, the actress Audrey Hepburn, and the wife of the founder of Hanson Robotics, Amanda Hanson. 

Sophia can imitate over 60 facial expressions identified by the facial recognition API and has lifelike skin made of a material called Frubber. That way, she can socially interact with humans, recognise emotions, hold conversations, and even make jokes. 

While Sophia does not possess true consciousness or self-awareness, she is designed to appear self-aware and engage in natural human-like interactions.

And she has already achieved more than many humans:

In October 2017, the Saudi Arabian government granted Sofia citizenship, making her the first non-human to obtain citizenship in any country. Sophia's creator saw this citizenship as an opportunity to speak out about women's rights in a country that denies them.

A month later, Sophia was appointed the United Nations Development Programme's Innovation Champion. She even joins meetings. 

Despite all that success, true self-aware AI is still a long way off and remains a topic of active research in the field of AI.

But how will all these developments influence your business? 

How will AI change B2B content marketing?

First, Natural Language Processing (NLP) is set to transform B2B content marketing by enabling machines to understand human language. This technology will be instrumental in helping marketers create highly tailored content to their target audience's needs and preferences. 

With NLP, machines can analyse large volumes of data to understand the topics most relevant and engaging to your target audience. This will allow marketers to create highly personalised content that resonates with their customers and ultimately drives conversions.

Machine learning AI will also significantly impact B2B content marketing. It can help businesses to identify trends and patterns in their customers' behaviour.

By analysing data, marketers can gain insights into what types of content are most effective in driving conversions and adjust their strategies accordingly.

But perhaps the most exciting form of AI set to revolutionise content marketing is predictive analytics. 

This technology uses machine learning algorithms to predict future trends and behaviours based on historical data. It can be used to identify which prospects are most likely to convert and which types of content are most effective in engaging them.

By leveraging this technology, marketers can create highly targeted campaigns that are more likely to result in conversions.

So, what does all of this mean for B2B content marketing? 

Marketers can create more personalised, targeted, and effective content than ever before. By leveraging the power of AI, businesses can analyse set amounts of data to gain insights into their customers' behaviour, preferences, and needs. 

But while the emergence of AI is undoubtedly exciting, remember that technology is only one piece of the puzzle. To succeed in B2B content marketing, businesses must still focus on creating engaging, informative, and valuable content for their customers.

And so far, AI won't replace the insights gained from lunch with your best clients.