About a year ago, ChatGPT and the generative AI (genAI) boom catapulted AI into the spotlight. The breakneck pace of innovation has made it difficult to discern current use cases from hypothetical eventualities. Like any technological development, AI is a double-edged sword, and it has already started to change the topography of the programmatic landscape.
Quality display inventory will get harder to come by
- The made-for-advertising (MFA) problem is about to get worse. MFA websites—which are, as their name suggests, created with the sole purpose of attracting as many ad revenues as possible—can produce content using genAI with little to no human oversight. This disregards the tradeoff in user experience, which is already poor to begin with.
- Current incentive structures aren’t helping. Ad tech providers get a cut of the transaction regardless of impression quality. And when a brand is promised low costs per thousand (CPMs), chances are high that MFA or other low-quality inventory is used to deflate average costs.
- Even high-quality publishers face an existential threat in AI. They’re already seeing less traffic from social media, according to data from Similarweb cited by Axios. Traffic will likely take another hit as generative search tools roll out to general audiences, rendering clickthroughs largely unnecessary.
But machine learning will empower advertisers with better targeting and measurement
- Identity resolution will get a boost. AI is being built into advanced probabilistic solutions that connect various deterministic data sets (i.e., known audiences) and fill in gaps where necessary (i.e., unknown audiences).
- Analytics will be democratized. Personalized reporting and natural language queries will allow more people without data science backgrounds to generate insights, freeing up bandwidth for data scientists to apply their skills elsewhere.
- But creative production will see the biggest benefits. Automated creative production and optimization will supercharge personalization efforts and allow better resource allocation. For example, talent can focus on developing smart creative concepts rather than reformatting ads to fit various dimensions.