GroupM has published a new study titled “The Next 10: Artificial Intelligence”.
According to the report, artificial intelligence-enabled advertising is estimated to become more than $370 billion this year and is likely to form the vast majority of media by 2032, reaching $1.3 trillion, or more than 90% of total ad revenue.
This study examines how the media landscape and consumer behaviour will shift over the coming decade, in large part due to AI-enabled advertising that likely already accounts for nearly half of all advertising revenue, or more than US$300 billion.
Some major implications emerging with the rise of AI
- The declining reach of linear TV and less tolerance of irrelevant, interruptive ad pods.
- The growth of audio-first devices with digital assistants (e.g. earbuds and smart home speakers) means that voice search will overtake text-based search.
- Data will most often be managed on-device and will be increasingly obfuscated or anonymised by AI and privacy services.
Takeaways from the report across a few categories:
- Advances in AI and these evolving media channels could result in marketers increasingly tying together products, consumer experiences and advertising experiences:
- Automotive: The use of generative AI and digital twins will enable greater personalisation of advertising in the sector—i.e.: a custom colour model shown driving in the buyer’s own city.
- CPG: Machine learning paired with genomic sequencing will make personalised nutrition and personal care products increasingly possible.
- Apparel: Computer vision, machine learning algorithms and generative AI could disrupt the apparel and retail industry by creating a vast grey market of copycat goods or user-generated designs competing for image searches.
- Entertainment: Personalised storytelling could become a reality as ads and IP are customized based on audience data and/or selections.
The Next 10 also raises “Ethical and responsible AI” questions such as:
- How do we protect at-risk users and all consumers from AI that exploits dark patterns or behavioural “hacks”?
- What are the ways we can protect against the weaponisation of AI in advertising tools and platforms used to amplify misinformation, deep fakes, fraud and abuse?
- What is our level of comfort with what remains hidden in the black box of machine learning?
- Should people be notified when they’re speaking or chatting with an AI chatbot and not a human?
- How do we build safety and accountability into algorithmic incentives?
- How should disclosures about the use of AI in advertising work?