The latest version of ChatGPT can simulate human behavior in a busy social media feed, specifically the "micro-pause" when a user stops scrolling. Marketers can upload posts and ask the AI to predict engagement, providing a valuable pre-launch analysis of whether content is compelling enough to capture attention.

GTM leaders no longer need to delegate strategy implementation. With tools like ChatGPT, their spoken words can become code, allowing them to rapidly prototype and test complex, data-driven prospecting campaigns themselves, directly connecting high-level strategy to on-the-ground execution.

Instead of using AI to write final copy, leverage it as a brainstorming partner. Dave Gerhardt uses ChatGPT to generate 15 variations of a subject line. This process allows him to cherry-pick words and phrases, combining them into a superior, human-crafted final version.

AI agents can continuously experiment with variables like subject lines, send times, and offers for each individual user. This level of granular, ongoing A/B testing is impossible to manage manually, unlocking significant performance lifts that compound over time.

Top creators like Mr. Beast relentlessly A/B test thumbnails and video intros to maximize views. AI video platforms now bring this data-driven experimentation to SMBs, allowing them to rapidly test variations of spokespeople, demographics, and creative elements to optimize ad performance.

Instead of batching users into lists for A/B tests, AI can analyze each individual's complete behavioral history in real-time. It then deploys a uniquely bespoke message at the optimal moment for that single user, a level of personalization that makes static segmentation primitive by comparison.

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.

Instead of asking an AI tool for creative ideas, instruct it to predict how 100,000 people would respond to your copy. This shifts the AI from a creative to a statistical mode, leveraging deeper analysis and resulting in marketing assets (like subject lines and CTAs) that perform significantly better in A/B tests.

Despite mature backtesting frameworks, Intercom repeatedly sees promising offline results fail in production. The "messiness of real human interaction" is unpredictable, making at-scale A/B tests essential for validating AI performance improvements, even for changes as small as a tenth of a percentage point.