AI, marketing and fashion - can they all go hand in hand?
In a world that seems to be changing at a scary pace, it is no surprise that AI has now revolutionised how marketers communicate with their audience. Revolutionary pieces of technology have been developed which has made marketing simpler, cheaper and, in some ways, easier than ever. Whilst there are obviously many positives to this change, there are also some considerable downfalls.
Prada is one example of businesses using AI to advertise their products, such as the Prada Candy perfume advertised in 2021, and then again for their social media campaign in 2023. Since then, there have been more companies such as H&M, who went a step further to create digital twins of real life models for their campaign in 2025, as well as Hugo Boss and Levi’s, who have also started using AI for their digital marketing campaigns since 2023.
Whilst it is known that the real models will keep the rights to the use of the artificial intelligence doppelgangers, with companies insisting that they still pay them fairly and at a similar rate to how they would be normally, some insist that this controversial move will take many jobs away for those who work in studio photography, makeup and editing.
Generative AI is used to produce imagery that would otherwise take a whole team of people to create, just from a few short sentences of input. Not only can it be argued as morally wrong, it will be interesting to watch the impact of these campaigns have on the consumers perception of the brands. This could arguably be viewed as less humanistic, and a way of cutting costs so brands can get away with producing and posting more digital content at a faster and cheaper rate. The lack of human touch to a brand can be detrimental to an organisations appearance, especially when in today’s world, everything is so fast paced and there will easily be something better round the corner.
The constant use of AI can also contribute to a lack of originality within marketing. Google AI, for example, uses a constant average of all data on its website database to produce an answer; although majority of the time the answers will be relatively accurate, the information provided can never be 100% reliable as the quality of the sources is unknown without further reading. AI can also encourage a lack of initiative and a lazy approach to finding a quick and ‘bandade’ approach to researching. Many use platforms such as Chat GPT for information that is easily accessible, but are developing an over-reliance due to the growing need for quick answers.
The impact on the environment is another reason why many may find AI difficult to accept. Generative AI is incredibly resource-intensive, as it uses huge amounts water and energy to create and run large AI models. AI also emits high levels of carbon emissions, a study found that training one AI model emitted almost 626,000 pounds of CO2.
Whilst there are significant negatives, there are examples where, of course, AI has been used in a positive way, for example communicating to the consumer on a more personal level. Whilst there are risks that AI has taken away personal connections between real people, it does allow businesses to build a personal touch in places where the customer may have been otherwise overlooked. For example, the use of AI within email marketing has revolutionised how companies have been able to segment and target consumers, based on things such as ages, incomes, location, much quicker than if they were to employ a marketing team of people to undertake this task.
Other positives include increased creativity with marketing campaigns, as not only can marketers have more time to focus on producing concepts that are outside the box, AI can create imagery and visuals that would take a graphic designer more time to produce. Meta AI, for example, allows users to take footage they have shot, and adapt it to how they wish. All they have to do is upload a video, and then a few short sentences describing what they want changed/ added/ removed.
Not only this, but AI has also been used for predictive features in Google Analytics, such as producing predicted audiences and campaign deliverables, as well as enhanced analysis of data, to assist digital marketers when managing Ad Word campaigns.