Generative AI in Digital Marketing

In the modern marketplace, brands are constantly trying to connect with people. They want to know what makes a customer click a link, share a post, or decide to buy a product. For decades, marketers have relied on human creativity to craft these messages, but a new tool has entered the room: generative artificial intelligence. This is a type of computer program that can create original text, images, and videos simply by reading a description from a person. It has changed the speed at which companies can produce content, allowing them to write thousands of advertisements or social media posts in the time it once took to write one. However, speed is not the only thing that matters. The real question facing businesses today is whether this speed comes at a cost. Does a message written by a machine feel as trustworthy as one written by a person? Does a customer care if they know a computer helped write an ad? These are the questions that drive the study of how artificial intelligence shapes the relationship between a brand and its audience.

A recent research article by Ankit Shah and Rakesh Panchal investigates exactly this dynamic. The authors did not run new experiments or interview people themselves. Instead, they acted as careful archivists, gathering and comparing findings from many different studies and industry reports published between 2018 and late 2026. Their goal was to piece together a clear picture of what is actually happening in the real world. They looked at two main sides of the story: how companies are using these tools, and how customers are reacting to them. The result is a nuanced view that suggests artificial intelligence is a powerful helper, but a risky replacement for human judgment.

The study begins by looking at the business side of the equation. Data from a major global survey conducted in 2025 shows that the use of this technology is spreading quickly. About 71 percent of the organizations surveyed reported that they now use generative artificial intelligence regularly for at least one part of their business, a number that has risen steadily from the previous year. For many of these companies, the most common use is creating text, such as product descriptions or social media captions. However, the researchers found a gap between how much companies are using the tool and how well they are managing it. Only about 27 percent of these organizations require their employees to review every single piece of content created by the machine before it is published. Furthermore, nearly half of the companies reported experiencing at least one negative outcome from using the technology, such as errors or off-brand messaging. Perhaps most telling is that more than 80 percent of the respondents said they had not yet seen a clear, measurable improvement in their overall profits that could be directly blamed on the use of this technology. This suggests that while companies are eager to adopt the tool, they are still figuring out how to make it work for them without causing trouble.

On the consumer side, the findings reveal a complex mix of reactions. When a brand uses artificial intelligence to create content, it can make the message feel fresh and creative, which often grabs a person's attention. The technology is good at generating many variations of an idea, which can help a brand sound innovative. However, this same efficiency can backfire. If a customer senses that a message was produced by a machine without much human effort, they may feel that the content lacks authenticity. The study highlights that people are particularly sensitive to this when the task requires empathy, taste, or a personal touch. If a brand tries to use a machine to write a heartfelt story or handle a sensitive customer complaint, the audience may feel disconnected or even deceived.

The way a brand talks about its use of technology also matters greatly. The researchers found that telling a customer "this was made by AI" does not have a single, predictable result. In some situations, being honest about the use of artificial intelligence can make a person trust the brand more because it feels transparent. In other situations, that same disclosure can make the content seem less genuine or less impressive, causing the customer to lose interest. It depends heavily on the context. For example, on social media platforms where people follow specific creators for their unique personalities, admitting that a post was machine-generated can weaken the bond between the creator and the follower. The audience often values the feeling that a real human put effort into the work.

The study also looked at whether trust leads to sales. It found that while certain signals, like a third-party seal of approval or a verification code, can make a customer trust an advertisement more, this trust does not automatically translate into a purchase. A person might believe an ad is honest and still decide not to buy the product because of price, availability, or other reasons. This means that simply making content look trustworthy is not enough; brands must measure the actual results, such as how many people buy the product or return to the website, rather than just counting how many people liked the post.

Ultimately, the authors conclude that generative artificial intelligence should not be seen as a tool that replaces the human touch. Instead, it works best when it is used to support human creativity, not to take over. The most successful approach involves using the machine to generate ideas and drafts, while keeping a human in charge of the final decisions. Humans should be responsible for checking facts, ensuring the tone is right, and making sure the message feels authentic. The study suggests that brands need to be careful about how they use these tools, ensuring they do not sacrifice the unique personality of their brand for the sake of speed. By combining the efficiency of the machine with the judgment and empathy of a human, companies can create content that is both innovative and trustworthy. The path forward is not about choosing between humans and machines, but about finding the right balance where the machine helps the human do their best work.