Gartner research: Two types of AI emerging near the height of the hype cycle

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According to new research from Gartner, two types of emerging artificial intelligence (AI) – emotion and generative artificial intelligence – have reached the peak of the digital advertising promotion cycle. This is thanks to the expansion of AI in targeting, measurement, identity accuracy and even creative content creation.

“I think one of the key pieces is that the options for marketers are accelerating,” Mike Frogat, senior analyst in the marketing practice at Gartner, told VentureBeat. “When you think about digital media segmentation, ten years ago there was display, search, video, rich media, but now, there are podcasts, top-tier platforms, blockchain and NFT. AI helps marketers target, measure, identify consumers, and even Creating the content that can appear in those channels, creating all the new artifacts to give marketers a voice in those channels.”

The Gartner report, Hype Cycle for Digital Advertising 2022, noted that traditional approaches to customer targeting are eroding, as they evolve from a supposed bartering principle to an explicit consent-driven media and advertising economy.

As Google continues to delay the date, it will stop supporting third-party cookies – which digital advertisers have historically relied on to track ads – digital marketers will need to learn how to adapt to the scarcity of customer data and the increasing difficulty of targeting.

Emotion AI: Opportunities and Challenges of Privacy

According to an analysis by Gartner analyst Andrew Frank, emotion AI “uses AI techniques to analyze a user’s emotional state…[and] Responses can be initiated by performing specific actions, customized to suit the mood of the client.”

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

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

“It needs to be transparent in how it’s used, and we have to move away from bundling it into tracking types within apps that collect implicit stuff,” he explained.

But he added that AI for emotion would create interesting opportunities for brands if it was linked to trust and explicit approval. According to a Gartner report, access to sentiment data “provides insights into motivational drivers that help test and improve content, personalize digital experiences and build deeper relationships between people and brands.”

The Gartner report warned that emotional AI could take another decade to become well established. Currently, organizations should carefully review vendor capabilities, as the emotional AI market is immature and companies may only support limited use cases and industries.

Generative AI: Coming Soon to Mainstream Adoption

The Gartner report also found that generative AI covers a wide range of tools that “learn from existing artifacts to generate new real-world elements such as video, narration, speech, synthetic data, and product designs that reflect the characteristics of training data without redundancy.”

The report expects that, within the next two to five years, these solutions will be widely adopted.

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

For marketing professionals, synthetic AI addresses many of the issues they face today, including the need for more content, more assets, and engaging customers in smart and personalized ways.

“Imagine a brand that takes an AI-generated tool and feeds its existing creatives, copies the assets into it, and comes up with whole new versions of advertising, video, and email content,” Froggart said. “It automates a lot of that and allows marketers to focus on the strategy around them.”

Additionally, generative data assets can remove the individual identity needed for targeting.

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

However, there are still severe challenges around regulations and potential issues such as deep counterfeiting. The Gartner report recommends examining and identifying the advantages and limitations of generative AI, as well as balancing technical capabilities with ethical factors.

Gartner Research: The Future of Artificial Intelligence in Marketing

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

“I think this is where AI will really start to show its value,” Frogart said, adding that while it doesn’t consider solutions like generation and emotion, AI will avoid the “basin of disappointment” in the Gartner Hype Cycle after peaking, “ I think they will find their own way through the hype cycle.”

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