AI Infusion into Paid Social
Buying paid social media effectively is challenging, to communicate hard vs. easy with AI, pushing the performance needle forward and increasing efficiency for agencies and their client partners. In the last five years AI has seen a massive usage in marketing, and social media platforms were early adopters of the new technology. These new AI-powered tools are rewriting the way advertisers build marketing strategies, set up campaigns, and think about creative adaptations. However, AI is not a cure-all for advertising needs—now more than ever, the industry needs the expertise of media buyers to add nuance and strategy to marketing campaigns.
Meta was early to adopt AI-forward features when they rolled out the Advantage+ campaign setup in 2022. This revolutionary advertising tool automatically optimizes budget, audiences, and placements, resulting in a 9% improvement in cost per conversion according to a global test. Automated setup has since become a standard practice for the industry, with other advertising platforms including TikTok and Snapchat have adopted similar campaign features. Automated campaign setups have been a great response to ever tightening targeting and privacy parameters being implemented industry-wide, allowing advertisers to make the most of their advertising dollars while optimizing campaign performance.
Tools Continue to Disrupt the Paid Social Industry
As AI progresses further, so do the tools made available to advertisers. Recent industry disruptors include the ability to expand images, create ad copy iterations, and even predict trends and campaign performance in real-time. TikTok recently rolled out a campaign type called Smart+ Campaigns where advertisers can input creative assets, KPIs, and target audience and the platform will automatically output the rest of the campaign creation process including campaign objectives, creative combinations, and CTAs. While many like to consider AI-integrated tools to be “fool-proof,” adoption of these features can be complex. These features can be used as a low-cost stop-gap for advertisers working on a restricted timeline or smaller budgets, but they cannot replace the hand-on-keyboard workflow of expert media buyers to ensure brand consistency.
Influencer Marketing and Social Listening
AI is rapidly transforming influencer marketing by streamlining workflows, improving targeting, and boosting campaign performance. AI tools analyze vast amounts of data across social media platforms to identify influencers whose audience demographics, engagement rates, and content style align with a brand’s goals. This eliminates much of the manual research and guesswork as AI can evaluate an influencer’s followers for authenticity, sentiment, location, interests, and even purchase intent. A prime example of this social listening.
Social listening is the process of monitoring and analyzing conversations, trends, and mentions across social media platforms and online channels to understand what people are saying about a brand, industry, product, or topic. It goes beyond simply tracking likes or comments—it’s about gathering insights from public sentiment, keywords, hashtags, competitor activity, and emerging trends.
AI advancements have transformed social listening from a time-consuming luxury into a mission-critical tool for brands and marketers. Before AI, social listening required manually combing through posts, comments, and hashtags—making the process slow and labor-intensive. Now, AI-powered platforms can process millions of conversations in real time, across languages, geographies, and channels, instantly surfacing actionable trends and sentiment shifts. This agility turns what was once reactive into a proactive advantage. Brands can act faster and smarter—pivoting messaging, responding to emerging conversations, or capitalizing on viral trends before competitors. AI usage in influencer marketing and social listening is making the space more data-driven, efficient, and performance-focused.
Understanding Risk
As new features and products roll out, there will always be some level of investment and risk for testing. The success of AI integration is contingent on high quality data signal passthrough, as well as human oversight for execution. Since many of these tools rely on the transmission of user data, industries such as healthcare may run into hurdles when trying to adopt some features while maintaining user privacy. Additional risk also comes from the unknown nature of AI algorithms and how they actually work. Many clients have reservations about removing audience control altogether, which would ultimately give the algorithm free reign to deliver ad dollars to the users it deems most fit.
Brands should understand that AI is not a replacement for hands-on-keyboard workflow but should be seen as a partner. AI automation allows marketing teams to work more efficiently and focus on higher level strategy and creative development. With the use of campaign automation and optimization, advertisers can spend more time focusing on big picture needs instead of time consuming manual tasks.
This article is featured in Media Impact Report No. 67. View the full report here.