Instead of guessing what short-form content will resonate, identify existing long-form videos or articles with the highest engagement. Transcribe these proven winners and use AI to extract impactful clips, carousels, and tweets. This method leverages past success to increase the probability of future performance.

The audience for marketing content is expanding to include AI agents. Websites, for example, will need to be optimized not just for human users but also for AI crawlers that surface information in answer engines. This requires a fundamental shift in how marketers think about content structure and metadata.

The best initial use for AI in marketing operations is automating high-volume, low-complexity "digital janitor" tasks. Focus AI agents on answering repetitive questions (e.g., "Why didn't this lead qualify?") and cleaning data (e.g., event lists) to free up specialist time for more strategic work.

Every customer call is a potential blog post. An AI workflow systematically redacts all sensitive and identifying information from call transcripts, then rewrites the core use-case discussion into an SEO-optimized article. This creates a scalable content machine fueled by real customer problems, generating thousands of posts.

Instead of asking an AI to repurpose content ad-hoc, instruct it to build a persistent "content repurposing hub." This interactive artifact can take a single input (like a blog post URL) and automatically generate and organize assets for multiple channels (LinkedIn, Twitter, email) in one shareable location, creating a scalable content remixing system.

Leverage AI tools to process transcripts from long-form content like webinars or podcasts. Prompt the AI to extract key takeaways and tactical advice, which can be quickly turned into valuable email sends. This creates an efficient content engine and drives traffic back to original assets.

The company developed an AI that conducts highly technical expert network interviews, automating a high-friction manual process. This enables new, scalable content creation like monthly channel checks across dozens of industries—a task too repetitive for human analysts to perform consistently at scale.

Using plain-English rule files in tools like Cursor, data teams can create reusable AI agents that automate the entire A/B test write-up process. The agent can fetch data from an experimentation platform, pull context from Notion, analyze results, and generate a standardized report automatically.

Leverage AI to analyze your year's worth of data to quickly identify top-performing content. AI can then go a step further by summarizing these top pieces or extracting key takeaways, creating new derivative content from your existing assets with minimal manual effort.