90% of companies are using GenAI, 62% report their initiatives are stalled or struggling¹

Michaels served diverse customer segments (quilters, painters, DIY decorators, etc.) but personalized only 20% of emails. The team was overwhelmed by data and creative volume, leading to generic messages that didn't reflect individual crafting interests and flat engagement.²

Michaels applied GenAI to a high-effort, judgment-based workflow focused on personalized email copy generation that directly supported retention goals. The system analyzed purchase and browsing data to generate individualized messages at scale, while marketers guided tone, themes, and overall quality.

Email personalization increased from 20% to 95%. click-through rates rose 25 - 40%, and customer retention improved significantly.²

As an omni-channel CPG startup competing across DTC, Amazon, and retail, we needed to scale performance ad production efficiently without sacrificing performance.

Built "The Golden Kernel," a custom Claude copywriting and scriptwriting implementation that pulls from the context of our brand guidelines, top-performing ads, and domain expertise in creative strategy.

Prediction-market platform Kalshi received six and seven-figure quotes from production studios for an NBA Finals ad, timelines and budgets the startup couldn't justify.

Hired GenAI filmmaker PJ Accetturo who used a human-in-the-loop prompting chain to turn an ad concept into a broadcast-ready ad:

95% cost reduction vs. traditional production, 20 million impressions across TV and online, 3+ million views on X within one week, became first fully AI-generated ad to air during major sporting event.⁴

Stitch Fix needed to scale personalized styling for millions of clients without losing the warmth and individuality expressed in the "style recommendation notes" — the short, human-written messages that accompany each clothing box and explain the stylist's outfit choices.

The team used context engineering to feed generative AI with stylist-written notes, customer profiles, purchase history, feedback, and trend data. This allowed AI models to draft high-quality "style notes" that mirrored Stitch Fix's brand voice and personalized rationale. Human stylists then reviewed, refined, and approved these AI drafts—preserving authenticity while increasing speed and scale.⁵

Reduced stylist writing time by over 50%, maintained engagement and satisfaction scores equal to fully human-written notes, and lifted average order value through more cohesive, data-informed outfit recommendations. Context-rich AI became an extension of the stylist, not a replacement.⁵

CarMax's digital team wanted to provide detailed, SEO-rich vehicle descriptions and reviews for its massive online inventory. Traditionally, creating expert-style summaries required content teams to research, write, and edit thousands of vehicle pages, a process that took years.

CarMax deployed GenAI and grounded it in the company's proprietary database of expert vehicle research, specs, and customer insights.

The system automatically drafted vehicle overviews and buying guides at scale, while human editors reviewed, fact-checked, and approved final content. This automated workflow transformed what was once a bottleneck into a scalable content engine that continuously updated and expanded CarMax's library.⁶

Paid acquisition costs were rising while organic growth required new language courses that took years to develop - capping the current total addressable market.

Duolingo redesigned its content creation workflow around GenAI, compressing course production from years to weeks. Each of the 148 new language pairs became a searchable entry point, community flywheel, and viral "finally, my language!" moment, which turned product expansion into a marketing engine. ⁷,⁸

Sustainable advantage doesn't come from access to models.