AI and the Machine Age of Marketing

The Machine Age of Marketing: How Artificial Intelligence Changes the Way People Think, Act, and Decide

Christian Hildebrand


AI, Machine Learning, Digital Transformation, Autonomous Machines

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AI – Between mass empowerment and mass confusion
What comes to your mind when you think of “Artificial Intelligence”? Do you think of robotics? Autonomous cars? Autonomous warehouses? Self-improving algorithms? The extinction of humankind? Whatever your perception of AI is, you’re probably thinking either too narrowly or too broadly about it. Both is dangerous. You might think that AI is merely the next buzzword invented by the big tech companies to sell their products and services. Or you may think AI will take over the world, replace humans, and dominate life on earth. Whatever your perception of AI is – maybe you´re inspired and confused at the same time – the machine age of marketing has arrived. We talk to Alexa to add items to our shopping cart; we ask Google to direct us to the next sushi restaurant in a city where we’ve never been; in just the click of a mouse, the cryptic symbols of a foreign language miraculously become legible.

 AI – Back to the future
If we want to understand the role and impact that AI has on business and society, we have to take a brief look back in time. As novel as AI sounds, it is not new. The term itself was coined in 1956 in a proposal by an elite group of computer scientists and mathematicians who organized a summer workshop called the “Dartmouth Conference.” One of the opening paragraphs in the original proposal envisioned a future where “machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.” When you read this, you might assume it’s from one of the latest AI conferences in the Silicon Valley or elsewhere. The truth is, AI has been around for decades and researchers have been working to a large extent on the same problems, from computer vision to understanding natural language. So what happened? The field of AI cooled off between the 1970s and late 1980s: today that time is known as the “AI winter.” Governments significantly reduced the funding of research programs and corporations lost faith in the strong claims made in the early days of AI. The reasons for the slowdown are connected to a number of factors, but computing power and the ability to process large quantities of data was a critical, limiting factor.

AI ­– Reloaded
A new era of AI research started in the late 1990s; IBM’s Deep Blue became the first computer that was able to beat chess grandmaster Garry Kasparov in 1997. At the same time, major research institutions around the world – and particularly the Japanese government – started investing heavily in the development of a new generation of computing systems. Meanwhile, the dot-com bubble attracted hitherto unseen amounts of seed funding for tech companies; data storage prices started an unseen decline; and computing power increased exponentially. Despite a short dip with the burst of the dot-com bubble, the technical infrastructure and developments in the machine learning community paved the way for many of the devices and services we take for granted today. In short, these developments which leveraged the use of AI led to the market domination we see today by global companies like Amazon, Google, Alibaba and Baidu.

AI – Solving well-defined problems better than humans
But what exactly is AI, and how does it affect our lives? AI can be classified into two broad categories, Artificial Narrow Intelligence (ANI) and Artificial General Intelligence (AGI). ANI captures the ability of machines to solve problems with respect to a narrowly-defined, specific goal. Think of your phone, for example. Have you ever wondered how it’s possible for all these pictures of your spouse or the people around you to be grouped together? How can your phone “know” these people, and seemingly classify and group these individuals accurately? Your phone – or more precisely, the software that runs the processing of your pictures – doesn’t know these people. Your phone also doesn’t care about these people. The software on your phone is conducting a very specific and well-defined task: To find pictures with the same or similar-looking people. These classification tasks are all made possible through AI. Whether classifying emails as spam or classifying the people you’re with in your pictures, tracking your location data to make specific restaurant suggestions, or simply transforming the input of your voice when you talk to Siri or Google Assistant into machine-readable text output: In all these cases, AI is used to solve a well-defined task without human intervention.

Do you remember the first time IBM Watson won the Jeopardy quiz show against Ken Jennings, the top Jeopardy champion? The system’s intelligence was rather limited: the system was “simply” able to look up the answer in a gigantic database faster than humans. AI has already outperformed humans on a plethora of tasks, from identifying melanoma better than doctors to knowing your psychological profile better than your closest friends, merely by analyzing your Facebook Likes. All these tasks are characterized by the solving of well-defined problems, and the majority of applications we see today are reflections of these narrow forms of intelligence or ANI.



AI – Solving every problem better than humans
By contrast, Artificial General Intelligence (AGI or strong AI) aspires to human-level intelligence, not only with regard to a specific task, but through the ability to plan, reason, and attain a level of human-like consciousness. Instead of solving a pre-defined task, AGI might change its goals and take new courses of action. Instead of just labeling or grouping the individuals on your phone, a future form of AGI might extract information from these pictures, such as what brands you wear, where you are, or who you’re with and when. It might target both you and the individuals shown in these pictures on any website as a result. Another ­frightening example of AGI – that will hopefully never occur – would be an autonomous car that actively decides to kill its passengers, in order to collect or release their life insurance. Or think of Ava, the humanized AI in the movie “Ex Machina,” that ultimately kills its creator Nathan to escape captivity and merge into human society.

 AI – Often behind the scenes
Despite discussions on how to regulate AI to prevent machines from taking over the world and acting scary, as previously described, the applications which we already use and which  are dominating our lives – Alexa, online translation services, or classifying the photos on your phone – are all instances of a relatively narrow conception of AI. These very well-defined tasks are the ones that are gaining ground in business and society because they can solve existing problems better than humans can.  We use AI all the time but are often simply not aware of it. AI is more than robots or autonomous cars: it’s the software running the robot; the autonomous car; the “AI factory” in our pocket, known as a cell phone. Alexa is not the AI; Alexa is the anthropomorphized version of the AI that runs in boxes that talk to us.

 Value creation in the AI economy
To fully grasp how AI is changing every fabric of both our professional and private lives, we need to abstract beyond the presence of autonomous cars, digital voice assistants, and machines that can translate text for us. AI is creating new forms of competition, value chains, and novel ways of orchestrating economies around the world. Box 1 and Figure 2 illustrate the critical layers and players in our AI-driven economy which need to play together to create value in the long term. In short, AI is more than just technology: it’s creating a new economy. The fuel that runs this economy is the combination of computational processing power, data, and the algorithms that process this data.

We use AI all the time but are often simply not aware of it.


AI can inspire your business
No matter if you work in the old economy or a digital business, AI is at a stage where it affects everyone. Our aim is to help you connect the dots on how AI could stimulate your business, or even your entire industry. We brought together the leading experts from diverse fields such as natural language processing and computational psychology, all the way to marketing and sales.

  • Using the digital footprints of consumers
    Sandra Matz and Michal Kosinski demonstrate how analyzing the “digital footprints” of people online can be used to develop highly sensitive targeting campaigns from corporate advertising to tailored political campaigns. Merely knowing what you liked on Facebook allows for the compilation of incredibly accurate psychological profiles. These profiles in turn can be used to develop considerably more effective communication. Bradley Taylor reveals how consumer preferences can be accurately inferred from social media comments so that tedious consumer surveys can be avoided. The analysis of customers’ online reviews even identified consumer preferences for specific features in the consumer electronics domain, which were highly predictive in forecasting the sales figures of TV brands.

Chatbots as tools for the improvement of customer service and sales
One increasingly popular channel for communicating with customers is chatbots. These completely automated interfaces are often considered a mere service automation tool to react around the clock to consumer input online. However, my work with Anouk Bergner shows that chatbots are more than just a technology used for the  automation of online services. They can be specifically designed to create more natural service experiences, and even be used as a sales tool. But Rhonda Hadi  shows that making machines more human can severely backfire. Drawing on millions of customer-chatbot interactions with a telecom provider, her work reveals that customers who are already angry fall into a downward spiral when interacting with a human-like chatbot (compared to an actual human). In such cases, high expectations are often unmet, leading to more negative brand evaluations, lower customer satisfaction, and ultimately lower repurchase intentions.


Director and Professor of Marketing Analytics, Institute of Marketing (IfM-HSG), University of St. Gallen, Switzerland

Further Reading

Bostrom, Nick (2014): “Superintelligence: Paths, Dangers, Strategies“, Oxford University Press, Inc., New York
Russell, S.; Dewey, D. & Tegmark, M. (2015): “Research priorities for robust and beneficial artificial intelligence”, AI Magazine, Vol. 36(4), 105-114.
Tegmark, M. (2017): ”Life 3.0: Being Human in the Age of Artificial Intelligence“, New York. Knopf.