CES 2024: Four AI Trends on CMOs’ Minds

Steven Millman
Global Head of Research and Data Science, Dynata

If there’s one thing about the 2024 Consumer Electronics Show (CES) upon which most everyone can agree, it’s that 2024 was the year of artificial intelligence (AI). Carried on the back of the generative AI wave, AI was in just about everything this year — in toys, mobility assistance devices, software, financial tech, healthcare and literally every other category to be found.  

The marketing world was no less affected by the AI fever circulating at CES and I had the opportunity to hear from and speak to a variety of chief marketing officers (CMOs) about how their thinking on the subject has been evolving. Hopefully AI was the only fever circulating, but when you put 130,000 people together in one place, you take your chances… 

So what are CMOs thinking about the new AI revolution in the marketing space? Here are four major themes I heard from them over the week at CES. 

No. 1: Privacy Concerns Are Expanding

CMOs are used to worrying about privacy, whether it’s protecting their own customers, following relevant data privacy laws such as the California Privacy Rights Act (CPRA) or the European Union’s General Data Protection Regulation (GDPR), or handling sensitive health information. 

The large language models (LLMs) that underlie generative AI models present new privacy problems that CMOs — along with the rest of the advertising ecosystem — need to manage. The privacy challenge from LLMs centers around the training data used to create these models. LLMs that are not very careful with what they choose to include in their training sets may easily ingest personally identifiable information (PII) that can resurface later in response to a prompt. 

On the other side of the equation, PII may be inadvertently ingested into a LLM when included in a prompt — a serious violation of privacy rules — as many LLMs add user prompts directly to their training sets to improve the models. There are already easy-to-find examples of both kinds of violations.  

Finally, there is a new kind of hack frustrating LLMs. It’s called “injection prompts,” which are hidden instructions placed on a webpage that a bad actor understands will be pulled into the training set for one or more of the larger models. Researchers have created injection prompts that have made major LLM chatbots start to speak like pirates or ask the person using them to provide bank account details1. Any of these kinds of privacy violations could create major havoc for a brand and potentially crippling fines. 

No. 2: Concerns About Authenticity of the Creative 

In content marketing, brands are eager to engage with their audiences at a deeper level, to make their audiences feel heard and included, and to build connections with people that feel personal and real. Authenticity creates both trust and credibility — but it’s very difficult to establish in practice. Consumers are discriminating and quick to discern fake or overly polished attempts by brands to connect.  

While generative AI makes it easier to create new content at scale as you’ll see below, there is a concern that overuse of these models in the ecosystem will make all content feel, more or less, like all other content, eliminating the opportunity to create the connection with the consumer to which they aspire. This lack of perceived authenticity could actually turn consumers off to these brands, decreasing engagement with their most coveted audiences.   

No. 3: Increasing the Speed of the Creative Process 

All the CMOs I heard from appeared to agree that generative AI as it exists today cannot recreate the kind of creativity that humans are able to produce; therefore, the central role of the content creator in the marketing process is not at imminent risk of replacement as many fear. That said, these tools do present those creators with valuable tools which can speed up the creative process and greatly enhance the scale of production.  

We have all faced at some point the “blank-page problem” — staring at a blank screen or canvas and not knowing where to begin. Generative AI can be an effective assistant in kicking off the creative process by simply writing a prompt that asks the LLM to provide a list of options.  

Consider this hypothetical example I posed to Google’s Bard:  

I am building a new ad campaign for a new electric car called the Whizzer. This electric car differentiates itself from its competitors in that it is sportier, has faster acceleration, and better range. We want the ad to drive consideration of our brand for people thinking about buying their first electric car. The audience target is ecologically minded adults with no children aged 25-45 who own their own homes. Please provide two different messages for this ad campaign.

You can see how Bard responded to that prompt below: 

Unleash Your Green Speed

  • Headline: ”Eco-Warrior with a Need for Speed: Introducing the Whizzer.” 
  • Visual: Show a sleek Whizzer tearing through a winding coastal road, leaving a trail of leaves instead of exhaust. 
  • Message: ”Reduce your carbon footprint without sacrificing the thrill of the drive. The Whizzer’s sleek design, lightning-fast acceleration, and extended range let you zoom past expectations – and gas stations. Leave the boring hybrids behind and rediscover the joy of driving, guilt-free.” 

Charge Up Your Lifestyle:

  • Headline: ”The Whizzer: Power Your Life, Not Your Emissions.” 
  • Visual: Showcase a montage of a young professional using the Whizzer for various errands, charging at home, and enjoying leisure activities. 
  • Message: ”Live sustainably without compromise. The Whizzer seamlessly integrates into your dynamic lifestyle. Power your morning commute, fuel your workday errands, and charge up for weekend escapes – all from the comfort of your own home. The Whizzer isn’t just an electric car, it’s an extension of your conscious, connected life.” 

While neither of these message campaigns are nearly polished enough to be ready to use in this form — although I’m personally quite taken with the idea of leaves coming out of a non-existent tailpipe — they do have the ability to inspire a creator to start thinking about versions and variations that may resonate with their audience, greatly reducing the time necessary to generate the new copy.  

If these did not inspire, the creator could simply ask the LLM for five more ideas. Or 10. Or 20. It is similarly simple to iterate on an idea by creating the copy you want and then asking the LLM to give you a version that was directed to men, or to the older age range, or to certain races or geographic residences. LLMs can help creators version their ideas at speeds that would have been unthinkable just two years ago. 

No. 4: Hyper-Personalization of Ads 

With generative AI tools, it may become possible to not just create new versions of ads to different segments quickly, but to deliver unique ads to individual consumers based on demographic and other data already known about them. This is something that has many of the CMOs I spoke with very excited about the future.  

In today’s digital and connected television advertising, the ad delivery systems already go through a process of selecting the most appropriate ad for a given device or household based on what is known about them in their identity graphs. For example, a lipstick brand may not want to waste its ads on men or an arthritis drug brand on an 18-year-old.  

Several large advertisers are currently experimenting with the idea that an ad could be modified in real time to suit the characteristics known or believed to be true about the consumer. A base ad creative could be modified on the fly as it is delivered by placing the generative AI tool in the same workflow as the ad selection process. The technology isn’t there yet, and speed is still an issue, but this capability could be coming sooner than one might think. 

The Generative AI revolution over the last few years has ignited the marketing world, sparking both extraordinary potential and very serious concerns. From lightning-fast creative sprints to hyper-personalized ads, AI promises to transform how CMOs engage with their audiences while at the same time forcing them to navigate a complex and sometimes opaque series of new risks.  

A balance needs to be developed to amplify human creativity and productivity with these new AI tools while at the same time safeguarding privacy and preserving brand authenticity. AI-savvy CMOs who can walk this line are going to do extraordinary things for their brands in 2024 and potentially change the way we think about marketing into the future. 

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Steven Millman
Global Head of Research & Data Science, Dynata 

Steven Millman is an award-winning researcher with a focus on innovation, quantitative and statistical analysis, survey research, research design and other applied research techniques. At Dynata, he leads all research and data science initiatives globally, responsible for execution, automation, innovation, data quality and thought leadership. Previously, he led research, data science and operations for Dynata’s Advertising Solutions division. Steven has served as a member of the Board of Trustees for the Advertising Research Foundation (ARF) since 2018 and is the inaugural Chair of the ARF’s industry-wide Workstream on Artificial Intelligence. Before Dynata, Steven was Chief Scientist at Simmons Research (MRI-Simmons), leading measurement sciences and developing new products. As SVP of Research at Comscore, he led the ad effectiveness line of business and all custom survey work, combining self-report with behavioral data.