Gartner study: 2 types of emerging AI near the peak of the hype cycle

according to new Gartner Research, two types of emerging artificial intelligence (AI) — emotion and generative AI — are both reaching the pinnacle of the digital advertising hype cycle. This is thanks to the expansion of AI into targeting, measurement, identity resolution, and even creative content generation.

“I think one of the most important things is that options for marketers have increased,” Mike Froggatt, senior director analyst at Gartner’s marketing practice, told VentureBeat. “If you think about the fragmentation of digital media, ten years ago there were display, search, video, rich media, but now there are podcasts, over-the-top platforms, blockchain and NFTs. AI helps marketers target, measure and identify consumers, and even generate the content that can appear in those channels, creating all the new artifacts to give marketers a voice in those channels.”

Traditional methods of targeting customers are declining, the Gartner report noted. Digital Advertising Hype Cycle 2022evolving from an assumed consideration to an explicit consent-driven media and advertising economy.

While Google continues to push the date, it will stop supporting third-party cookies — which digital advertisers have traditionally relied on for ad tracking — digital marketers will have to learn to adapt as customer data becomes scarcer and targeting problems grow.

Emotion AI: Opportunities and Privacy Challenges

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

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

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

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

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

The Gartner report warned that emotion AI will likely need another decade to become firmly established. For now, organizations should carefully assess vendor capabilities, as the emotion AI market is still immature and companies may only support limited use cases and industries.

Generative AI: mainstream adoption soon

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

The report predicts that these solutions will become mainstream within the next two to five years.

Elements of the metaverse, including digital humans, will depend on generative AI. Transformer models, such as 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 increase scarce data or reduce bias.

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

“Imagine a brand that uses a generative AI tool and puts in their existing creative and copy assets and comes up with brand new versions of ad, video and email content,” says 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 required for targeting.

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

Still, steep challenges remain around potential regulations and issues such as deepfakes. The Gartner report recommends exploring and quantifying the benefits and limitations of generative AI, as well as weighing technical possibilities against ethical factors.

Gartner Study: Future of AI in Marketing

For now, marketing professionals still have the old tools – such as third-party cookies – at their disposal. But now that trends like media fragmentation and deprecation of customer data sources are not slowing down, they need the right tools to adapt to new forms of measurement and targeting.

“I think AI is really going to show its value there,” Froggart said, adding that while he doesn’t think solutions like generative and emotional AI will avoid the “valley of disillusionment” of the Gartner Hype Cycle after it peaks. “I think they will find their own way through the hype cycle.”