Author: Ava Patterson
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.
Google’s latest guidance changes what “optimized” means for brand blogs — here’s how to rebuild your content strategy for AI-first search.
Third-party cookies never touched CTV, but the identity gap is real. Here’s how brands target streaming households without breaking privacy rules.
YouTube’s automated brief-to-creator matching reshapes campaign sourcing, but brands still need governance to protect ROI and brand safety.
YouTube’s automated brief-to-creator matching is reshaping campaign sourcing—here’s what brands need to know about speed, control, and risk.
A close read of Google’s 76% ROAS claim for its Agentic Media Suite, and what brand media buyers should verify before shifting budget.
Learn how to write agentic AI-ready campaign briefs that let AI systems select creators, configure deployments, and generate reports without constant human intervention.
Build a generative AI brand asset repurposing infrastructure with the right tool stack, brand safety gates, approval checkpoints, and metadata standards to scale campaigns instantly.
Learn how brand operations teams should architect a generative AI asset repurposing infrastructure—covering tool selection, brand safety gates, approval workflows, and metadata standards.
Learn how brand procurement teams should evaluate AI-powered creator discovery platforms against manual methods for niche markets—covering behavioral affinity, audience intent, and ROI signals.
Build, license, or stick with point solutions? Here’s how enterprise marketing teams should evaluate AI infrastructure decisions as agentic deployment becomes the expected operating model.