Generative AI is much more than a trend these days. It is something everyone is using more and more. For me, it started to support my studies and work, but eventually grew into my new job. That shows how GenAI is growing into a real business innovation.
I used to use in GenAI as support in my work, but now I get to make my work revolve around GenAI. I want to use AI to automate surveys for businesses. This means we are developing a product that can make hugely accurate predictions of what consumers would answer on surveys. As a result, we are changing the way market research is done. We are addressing the pain points of high costs, long wait times and biases of people through this AI-related solution.
However, there are a few difficulties in creating AI services. For instance, an enormous amount of testing is required to fulfil the promised accuracy. After all, you don’t want the product to be less reliable than traditional ways. However, by constantly testing and adjusting the algorithms, you can overcome these problems and I can start selling my first AI service within a few weeks.
GenAI has therefore not only affected my work, but it will soon affect everyone’s work. For instance, you find that huge numbers of companies are emerging that can deliver real-time services via integrations with AI. In fact, it is relatively easy to set up because AI can do much of it for you. So anyone can start performing services that used to have to be done by an entire team of staff. In addition, all companies increasingly want to use AI services because it is reliable and predictable. It can greatly increase employee output.
So what’s stopping you from creating your own AI service? And what kind of service would you like to see in the market?

In recent years, as a result of digitalisation, it is easier for entrepreneurs and businesses to reach consumers. Anyone can relatively easily offer all their products and services on large platforms, such as Amazon, Itunes and Google. The advantage of these digital products is also that anyone can easily find them by searching properly (Brynjolfsson et al., 2011). But AI seems to be a risk for long-tail businesses, as AI’s recommendations are often focused on more popular and likeable items.

Today, with the advent of AI, recommendations on products, services, and activities are increasingly being made for you. Everyone has some experience with the digital recommendations of big players. Nowadays, these recommendations are also becoming more and more accurate, as much more data can be used through AI. AI continues to learn more about our behaviour and what we want to consume, resulting in more accurate recommendations. But this also means that AI is more likely to recommend popular products and services. Popular products are valued more often and therefore recommended faster. Thus, these products are easier to find on the internet. So this can all come at the expense of small businesses and speciality shops.
AI is also getting the ability to increasingly dictate or influence our recommendations, which is also part of algorithmic determinism (Pasquale, F. 2015). AI can trap consumers in a bubble in which you are increasingly influenced by and that allows different interests to narrow (Kitchin, 2016).
With social media, it is already known that such a bubble can have dangerous consequences for society (Bessi et al., 2015). For instance, there are conspiracy thinkers who no longer get to see different news sources and thus get a monotonous world view. But where do we draw the line between AI’s recommendations and consumer behaviour. Big companies can now influence all consumers in what they can like and what they should consume. As users, we need to remain aware of the risk posed by AI and what remains our own choices.

How do you think AI influences your interests and consumer behaviour? And how do you prevent it from narrowing your interests?

References:

Bessi, A., Zollo, F., Del Vicario, M., Scala, A., Caldarelli, G., & Quattrociocchi, W. (2015). Trend of Narratives in the Age of Misinformation. PLoS ONE, 10(8), e0134641. https://doi.org/10.1371/journal.pone.0134641
Brynjolfsson, E., Hu, Y., & Simester, D. (2011). Goodbye Pareto Principle, Hello Long Tail: The Effect of Search Costs on the Concentration of Product Sales. Management Science, 57(8), 1373–1386. https://doi.org/10.1287/mnsc.1110.1371
Kitchin, R. (2016). Thinking critically about and researching algorithms. Information Communication & Society, 20(1), 14–29. https://doi.org/10.1080/1369118x.2016.1154087
Pasquale, F. (2015). The Black Box Society: The Secret Algorithms That Control Money and Information. Harvard University Press.
http://www.jstor.org/stable/j.ctt13x0hch