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Adobe envisions agentic AI as a creative powerhouse, enhancing productivity by handling tasks so users can focus on their art. Ely Greenfield details how this technology will revolutionize Adobe’s suite of products.
At Adobe, the vision for agentic AI is clear: to empower users by giving them more control and time to focus on the work they love. This approach mirrors their stance on generative AI, emphasizing that while AI agents themselves are not creative, they can significantly enhance human creativity and productivity. In a recent blog post, Ely Greenfield, Adobe's leader in this space, outlines how agentic AI will transform workflows and content creation across various Adobe products.
Agentic AI is designed to converse, act, and solve complex problems, making it a powerful tool for professionals and enthusiasts alike. Adobe’s approach is centered on leveraging these capabilities to free users from mundane tasks, allowing them to focus on more meaningful work. Here are some key points:
Adobe Acrobat is a cornerstone of digital document management, with over three trillion PDFs in circulation and 400 billion opened in Acrobat each year. The platform has 650 million monthly active users, making it the global standard for handling digital documents. Here’s how agentic AI will enhance Acrobat:
Adobe Express aims to make content creation accessible and efficient, even for those with minimal design experience. With agentic AI, users can:

Photoshop is a staple for professional photographers and designers. Agentic AI will bring several enhancements:
The integration of agentic AI across Adobe’s suite of tools is expected to have a profound impact:
Adobe’s vision for agentic AI is not just about adding new features but fundamentally transforming how users interact with their tools. By focusing on enhancing human creativity and productivity, Adobe aims to set a new standard in the industry. As these technologies continue to evolve, we can expect even more innovative applications that will further revolutionize creative workflows.
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About the author
Kai built ML infrastructure at a Bay Area startup before developing an obsession with transformer architectures and inference optimisation that eventually pulled him out of product work entirely. A stint at a compute research lab sharpened his instinct for what actually matters in a model release versus what is marketing. He writes from the inside — from the perspective of someone who has debugged the systems he is describing at three in the morning. He is allergic to hype and instinctively drawn to the unglamorous plumbing questions that everyone else skips over.
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11 April 2025
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