Author: Ava Patterson
Ava is a San Francisco-based marketing tech writer with a decade of hands-on experience covering the latest in martech, automation, and AI-powered strategies for global brands. She previously led content at a SaaS startup and holds a degree in Computer Science from UCLA. When she's not writing about the latest AI trends and platforms, she's obsessed about automating her own life. She collects vintage tech gadgets and starts every morning with cold brew and three browser windows open.
How Magnite, MediaOcean, and MiQ handle autonomous campaign decisioning for creator-adjacent paid amplification — and what brand-side governance requirements actually demand.
Learn how to configure AI tools to detect bot activity, engagement pods, and inauthentic follower growth before creators enter your brand distribution programs.
AI generative search is reshaping brand discoverability. Here’s a practical audit framework to benchmark your visibility in ChatGPT, Gemini, and Perplexity before you lose ground.
AI-powered content analysis can verify true niche fit before onboarding creators into high-volume programs — here’s how brands should build that verification layer.
AI spend is rising but performance scores aren’t. Learn how a structured data foundation audit helps brand marketing leaders diagnose and close the gap.
AI shopping agents are now making purchase decisions based on your product data. Here’s how to structure machine-readable product information to stay in the recommendation loop.
Learn how to combine generative AI asset production with sentiment-driven distribution to prioritize audience trust over raw impression volume in influencer campaigns.
Learn how to build first-party identity-resolution pipelines that accurately signal consumer identity to generative AI recommendation engines and autonomous shopping agents.
Autonomous media planning is reshaping creator-adjacent campaigns—but only brands that define human override thresholds before launch will avoid costly bidding errors and brand safety failures.
Brands with standardized data and continuous experimentation infrastructure report smoother AI marketing integration — here’s how to build the testing-learning loop that actually works.