Gartner research: 2 types of emerging AI near hype cycle peak

According to new Gartner research, two types of emerging artificial intelligence (AI) — emotion and generative AI — are both reaching the peak of the digital advertising hype cycle. This is thanks to AI’s expansion into targeting, measurement, identity resolution and even generating creative content. 

“I think one of the key pieces is that the options for marketers have been accelerating,” Mike Froggatt, senior director analyst in the Gartner marketing practice, told VentureBeat. “When you think about the fragmentation of digital media, ten years ago, there was display, search, video, rich media, but now, there’s podcasts, over-the-top platforms, blockchain and NFTs. AI is helping marketers target, measure and identify consumers, even generating the content that can appear in those channels, creating all new artifacts to give marketers a voice in those channels.” 

Traditional methods for targeting customers are depreciating, noted the Gartner report, Hype Cycle for Digital Advertising 2022, evolving from an assumed quid pro quo to an explicit consent-driven media and advertising economy.

While Google continues to delay the date it will stop supporting third-party cookies — which digital advertisers have historically relied on for ad tracking — digital marketers will need to learn to adapt as customer data becomes more scarce and targeting difficulty increases. 

Emotion AI: Opportunities and privacy challenges

According to an analysis by Gartner analyst Andrew Frank, emotion AI technologies “use AI techniques to analyze the emotional state of a user…[and] can initiate responses by performing specific, personalized actions to fit the mood of the customer.”

Frank says it is part of a larger trend called “influence AI” that “seeks to automate elements of digital experience that guide user choices at scale by learning and applying techniques of behavioral science.” 

With public criticism around the use, or even potential use, of emotion AI tools, privacy and trust will be essential to emotion AI’s success, said Froggatt.

“It’s going to have to be transparent in how it’s being used and we’re going to have to move away from bundling it in types of tracking within apps that collect things implicity,” he explained. 

But emotion AI will create interesting opportunities for brands if tied to trust and explicit consent, he added. According to the Gartner report, access to emotion data “delivers insights into motivational drivers that help test and refine content, tailor digital experiences and build deeper connections between people and brands.” 

The Gartner report cautioned that emotion AI would likely take another decade to become firmly established. For now, organizations should review vendor capabilities carefully, since the emotion AI market is immature and companies may only support limited use cases and industries. 

Generative AI: Soon to reach mainstream adoption

The Gartner report also found that generative AI covers a broad swath of tools that “learn from existing artifacts to generate new, realistic artifacts such as video, narrative, speech, synthetic data and product designs that reflect the characteristics of the training data without repetition.”

Within the next two to five years, the report predicts, these solutions will reach mainstream adoption. 

Elements of the metaverse, including digital humans, will rely on generative AI. Transformer models, like Open AI’s DALL-E 2, can create original images from a text description. Synthetic data is also an example of generative AI, helping to augment scarce data or mitigate bias. 

For marketing professionals, generative AI tackles many issues they face today, including the need for more content, more assets and to engage customers in smart and personalized ways.

“Imagine a brand taking a generative AI tool and feeding their existing creative and copy assets into it and coming up with whole new versions of ad, video and email content,” said Froggart. “It automates a lot of that and allows marketers to focus on the strategy around it.”

In addition, generative data assets can remove the individual identity necessary for targeting.

“I think that it can be super-powerful for advertisers and media,” he added.

Still, steep challenges around possible regulations and issues such as deepfakes remain. The Gartner report recommends examining and quantifying the advantages and limitations of generative AI, as well as weighing technical capabilities with ethical factors. 

Gartner research: Future of AI in marketing

For now, marketing pros still have the old tools – like third-party cookies – available to them. But with trends like media fragmentation and deprecation of customer data sources not slowing down, they will need the right tools to adapt to new forms of measurement and targeting. 

“I think that’s where AI is really going to start showing its value,” said Froggart, adding that while he doesn’t think solutions like generative and emotion AI will avoid the Gartner Hype Cycle’s “trough of disillusionment” after reaching the peak, “I think they will be finding their own route through the hype cycle.”