We used to choose what influenced us. Now, advanced technology does it for us.
The social space has shifted from organic discovery to perfectly curated influence, forming an illusion of choice. As a user, you’re no longer simply deciding to follow a creator; someone (or something) has already decided who you’re most likely to trust.
And usually, that guess is spot-on.
It’s no news that AI is already changing marketing in fundamental ways, from personalization to prediction. What’s changing is that this technology is no longer experimental, it’s crucial infrastructure.
Back in 2018, McKinsey published an analysis of over 400 advanced AI use cases. Their conclusion was that marketing stood to benefit more from artificial intelligence than any other business domain. Fast-forward just a few years, and that prediction is already coming true.
From awareness to retention, brands now deploy AI at every touchpoint in the customer journey. Algorithms identify intent, predict behaviour, and personalize experiences with a level of precision that no human team could ever match. Even in the consideration phase, when a customer is still weighing their options, AI is already hard at work, providing targeted content, digging through product reviews, and quietly accelerating the decision-making process.
And we unknowingly see this everyday on our algorithms.
How can something artificial improve or automate an inherently personal industry?
Unlike programmatic ads or chatbots, influencer marketing relies on perceived authenticity and human connection. From outreach, to negotiating rates, to providing feedback on user-generated campaigns, it seems counterintuitive to leave the logistics to a non-human employee.
In a way, AI is a matchmaker, helping brands find their people better than humans can. Instead of replacing the human decision-maker, leveraging new technology can augment and, most importantly, scale their professional intuition.
Traditionally, choosing the right influencer is partly art, partly gut feeling. A marketer might look for a personality that feels right for the brand, someone whose aesthetic and values seem aligned in a non-quantifiable way.
AI is replicating this process. The platforms in use today go far beyond counting likes and followers, analyzing sentiment, scanning historical content for consistency, and segmenting an influencer’s audience with surgical precision. These tools help brands identify resonance: which voices are truly trusted, and which audiences are primed to listen.
Looking into the future of influencer marketing, a question arises: could AI actually predict the perfect post for a campaign?
Predictive modeling uses machine learning to determine the best time to post, most effective phrasing, and aesthetic congruence with audience preferences. So, from a technical perspective, the answer is a resounding yes.
Sephora’s approach to influencer marketing is a hybrid of machine-driven discovery and profound human storytelling. Each year, the brand launches a new cohort of the Sephora Squad: a diverse group of creators who range from emerging micro-influencers to larger-scale personalities.
The selection process is powered, in part, by AI. Algorithms help identify influencers who demonstrate high engagement, niche expertise, or unexpected overlap with Sephora’s target segments.
But the soul of the program is the sense of connection, not perfection. Applications for the Squad include written testimonials, engagement data, and an emphasis on authentic storytelling.
By pairing AI’s analytical power with a brand philosophy rooted in inclusivity and expression, Sephora shows us what the future of influencer marketing might look like: a partnership between humans and machines that makes room for both precision and personality.
Here at Creator, we love to keep our finger on the pulse of influencer marketing. We believe in the power of data in harmony with human expertise. Soon, we’ll be introducing new AI features to our dashboard, designed to make it easier to uncover alignment between creators and brands.
These new features are here to streamline the search, not reduce it to a formula. Even the perfectly matched collaboration is still built on something algorithms can’t fully capture: people choosing to listen.
