Five Adobe Sneaks I Want Now
Adobe's annual prototype showcase returned to Summit this year with some of its most ambitious AI ideas yet. Here are the five that caught my eye — and that I hope actually ship.
Every year, Adobe gives its employees a hall pass—the chance to pitch ideas that exist outside the company’s official product roadmap. The best ones surface at the end of the company’s Summit and Max events in a showcase called Sneaks. Typically, there are hundreds of submissions—500 this year—and only seven make the cut, a selection overseen by Principal Evangelist Eric Matisoff’s team.
However, not every Sneak makes it to market. Matisoff tells me that historically, between 30 and 40 percent of these projects ever make it into production. Those lucky enough may even become some of Adobe’s most popular features, such as Generative Fill.
Sneaks isn’t a typical demo day experience, and you should certainly not expect it to feel like another keynote. It’s meant to be fun and entertaining, which is why Adobe brings on a celebrity co-host. Past guests include Rainn Wilson, Joseph Gordon-Levitt, Jordan Peele, Kumail Nanjiani, Chelsea Handler, Kenan Thompson, and Jessica Williams. This year, Matisoff was joined by actress and comedian Iliza Schlesinger.

This week in Las Vegas, I attended my first Sneaks. Here are the prototypes that caught my attention and that I hope will make it onto Adobe’s product roadmap.
Project Face Off (Winner)
Created by research scientist Doga Dogan, Project Face Off simulates A/B testing to predict which creative variant will perform the best and why. Instead of waiting weeks for real-world traffic, marketers can upload competing designs, define the primary conversion goal, and let the system generate synthetic user personas that scroll, click, consider, and either convert or drop off. Results are generated in seconds.
Traditional multivariate testing is slow by design. Marketers have to build multiple variants, configure tracking, stand up experimental frameworks, and then wait—days, weeks, sometimes months—for enough traffic to reach statistical significance. And even when the test runs cleanly, the result is still just A versus B. What if you have a dozen variations worth testing? This prototype promises to let marketers run as many simulated tests as cheaply up front, eliminate the weak options earlier, promote stronger candidates into real-world tests, and save traffic and time for higher-quality experiments.
Project Face Off was named the Summit audience favorite, which means it has a much better chance of being productized in the future.
Project Test Kitchen
Project Test Kitchen reimagines AI image generation as a collaborative, multidimensional design workspace rather than a one-shot prompt box. Created by research intern Yuzhe You, it tackles the “too many cooks” problem head-on—giving multiple designers a seat at the table without the chaos. This prototype combines multiple people’s tastes and constraints. It enables exploration of visual directions along clear, controllable axes. The AI becomes a co-creator capable of understanding style, composition, and branding—not just keywords.
Project Tailored Takes
This AI-powered system connects workflows across Adobe Firefly, Workfront, Experience Manager, and Frame.io, making it easier to create highly localized, multi-version video ads. Today, transforming a “master” video into multiple localized spots requires separate shoots— sometimes entirely new productions—for each region. Multiple editing passes are also needed, as well as coordination across agencies and in-house teams. This can be costly, slow, and risky.
Adobe Foundry AI Creative Technologist Jordan Hall developed Project Tailored Takes to have AI do the heavy lifting. It treats videos not as single, finished files but as flexible templates. Shots, product imagery, motion, and narrative structure become modular elements you can recombine and regenerate for different markets, audiences, and channels. The goal: Marketers define what the ad should communicate and where it should run. Then, the AI-powered system handles how it’ll be visually and culturally adapted.
Project Page Turner
What if you could use AI to turn your website from a static, one-size-fits-all page into a dynamically assembled, intent-aware experience? That’s the idea behind Project Page Turner, created by Adobe’s Experience Manager engineering chief Paolo Mottadelli. The aim is to redefine personalization in the ChatGPT era by eliminating the need for a handful of fixed templates, the need for users to hunt and peck across entire websites to find information, and the need for marketers to anticipate every journey. Instead, AI will do it all by assembling, in real time, pages centered on a user’s intent.
To learn more about Project Page Turner, read my exclusive interview with Mottadelli.
Project Asset Amplify
Project Asset Amplify lets you turn a single asset into a full marketing ecosystem. With a prompt, you can leverage that artifact to create social media posts, print ads, and a website. And everything is editable within Adobe Photoshop and Express.
The brainchild of software developer Shivangi Aggarwal, it understands the source campaign’s visual language, messaging, and intent. It also knows the psychology and preferences of different audiences and demographics (e.g., millennials versus Gen Z, parents vs. performance-focused buyers).
Marketers face a content demand problem—too much needed, not enough capacity to produce it. Hero images, social posts, display ads, YouTube covers: the formats multiply faster than designer and writer bandwidth can keep up. Project Asset Amplify uses AI to turn a single asset into a full family of creative files, scaled across audiences, platforms, and use cases—freeing creative teams to focus on the work that actually requires human judgment.
You can watch every Sneaks presentation from this year now on YouTube. Alternatively, you can browse them individually at adobe.ly/sneaks.
Disclosure: I attended Adobe Summit as a guest of the company, with my flights and hotel stay paid for. The AI Economy’s coverage is editorially independent from those that it covers. These words are my own.



