How do you see generative AI transforming marketing?
Generative AI technology has boomed this year, primarily due to the expansion of Chat GPT and other platforms that enable content creation. The previous AI was predictive AI, which essentially allows you to make predictions based on historical data about the probability of an event happening in the future. This is very positive because it will enable you to understand what will happen in the future. For example, predictive AI predicts whether you like a movie in a product recommender, such as Spotify or Netflix. From an efficiency point of view, it brings a lot of value.
Generative AI creates content. It can create text, audio and images. If we think about what marketing is: sound, sight and motion, and what creativity is: communication through text, audio, photos and videos, we are in an environment where you can create all these kinds of content to help communication. It is a new tool for creating content for creatives, agencies and companies.
The key benefits of Generative AI are cost and variety. It allows the generation of content at a cost that is a fraction of the cost of human content generation, and the combination allows multiple variations of the same content to be generated. We, for example, have published some cases where we generate tens of thousands of product descriptions at a cost of one-thousandth of the cost of production by one person. It will undoubtedly transform marketing from the perspective of diversity and efficiency in content creation.
How does Making Science address the challenges this poses for the sector?
At Making Science, we have two sides: On the one hand, we are an agency, and on the other hand, we are a technology company. From an agency point of view, we are reviewing how to use generative AI in all our processes. We generate campaigns for our clients and are starting to use generative AI in different parts of the value chain of our business process: ad and content generation.
As a technology company, we have developed a platform called Trust Generative AI, which allows us to apply generative AI to the business cases that are most relevant and that allow us to scale globally. For example, it will enable us to generate product descriptions, blogs, web content, and copy generation for ads and images, and we are working on video generation for the near future.
At Making Science, we are taking a very proactive approach. We believe in embracing technology and maximising it for your business.
What impact, both positive and negative, can its use have?
Every technology has a positive and negative effect depending on how you use it. For example, you can use iron or steel to build a bridge, a machine gun, or a tank. On the positive side, generative AI can generate much more variety of content at a much lower cost. In other words, we must see artificial intelligence as a tool that helps us improve.
An MIT study analyses how, in generating content, an internal memo, an article or a communication, using ChatGPT, it is possible to improve by 20% times and improve the quality of the content by 30%. Therefore, it should be seen as an opportunity for Human Augmentation, which turns us into humans with more capabilities, as has happened in the past.
The downside is that it can generate virtual spaces that can be used for impersonation, generating your voice or false images, and generating fake content and information. As with any new technology, there are positive and negative uses. Instead, it’s a question of taking advantage of all the positive benefits and fighting against the negative ones.
Where does generative AI leave marketers in this new era?
Technology has changed marketing over the last 50 years, and marketers have had to adapt. Advertising at the beginning of the century, in the 1920s, was mainly text in newspapers or outdoor billboards. Then came radio, television, the internet, and now digital; we must adapt to every advertising landscape evolution.
In general, technology allows many lower value-added activities to be done in a scalable way, and marketers need to adapt. A similar example is what has happened in media buying through technology platforms such as Google or Meta, where techniques such as automatic bidding or other automation do tasks that only 20 years ago were done by consultants. Marketers, therefore, have to evolve their roles.
Similarly, let’s think about how creatives were made 100 years ago, where an illustrator was the one who drew, and how later video and audio editing tools appeared that allowed to evolve towards professionals with editing capabilities. Likewise, generative AI is an opportunity, and marketers must embrace it and evolve towards a technology that will enable them to do much more varied things at a lower cost.
How should the risks associated with their use be dealt with, and is there a way to avoid them?
You have to be cautious. From an agency or end-client point of view, communication always has communication guidelines defining how the brand expresses itself at text, audio and image levels. The risk of generative AI is that the content needs a sufficient level of control or compliance and results in content that is not appropriate or does not express the brand identity.
Controls must be implemented to ensure the generated content is valid, relevant and aligned with brand standards. At Making Science, we have developed our platform, Trust Generative AI, which incorporates algorithms that help users control how close or far their content is from the company’s standards and allows them to incorporate Human-In_The-Loop, which are processes in which there is a person who is validating the final content before publication.
How should brands integrate generative AI into their strategies to get the most out of it?
Fundamentally, from two points, taking advantage of the creativity capacity of platforms such as ChatGPT Dall. e or Google’s Vertex AI, which are platforms that have been trained with hundreds of millions of images, videos and texts to allow interaction with them, generating a lot of information in the divergent phase of the creative method. For example, when you are brainstorming, that is, in the phase of generating new ideas.
The second important application is the scalability and personalisation of content. Generative AI develops a lot of variations of content, and you have to take advantage of this strength. For example, when you create a website, the text of the descriptions is unique. With generative AI, you can create many variations of content and context with a different tone of voice depending on the business target. It differs from how you would address millennials as you address Generation X, with hyper-personalising communication.
Generative AI is one of the tech trends of 2023. How do you think it will evolve? Is it here to stay, or will nobody remember it in a few years?
Generative AI is the big hit of 2023, mainly due to the tools such as ChatGPT that have democratised the use of this technology. Previously, everyone used artificial intelligence with Google, Amazon or Spotify’s product recommender that presented videos, products or content relevant to the user. Still, it was not visible to them due to the artificial intelligence working in the background. Now, however, with ChatGPT, we are experiencing, in a very personal way, a 1-to-1 with the use of artificial intelligence, and we know that we are talking to a machine.
So, the significant change is that it’s consumed at the mass market level. Everybody understands generative artificial intelligence and its possibilities, and many people are thinking about used cases and opportunities. I think it’s a technology that is here to stay and will have a revolutionary impact on society and all sectors.
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