The AEP AI Assistant is capable of answering technical questions, automating tasks, simulating outcomes, and generating new audiences and journeys. It leverages generative experience models to provide insights based on an organization’s unique data, campaigns, audiences, and business goals. This is a significant step in democratizing access to enterprise applications and empowering more individuals to participate in content production and insights workflows.
Brands will soon be able to leverage AEP AI Assistant to generate everything from new customer experiences to audiences for personalization campaigns and visualizations for data analysis. This will include generating entire marketing assets such as emails and web pages with copy, design and images via Adobe Firefly—driving always-on experimentation as a result.
AEP AI Assistant will also deliver creative briefs that accelerate content production, auto-generated through existing assets such as PDFs, slide decks and Word documents. To support automation, AEP AI Assistant will help teams bypass tedious tasks from organizing data segments into folders to ensuring that AI-generated content meets brand-approved standards.
Brands like General Motors have been working with the AEP AI Assistant to boost productivity and accelerate time-to-value, while democratizing access across their organizations. With the ability to process large volumes of data and interactions, the AEP AI Assistant is poised to transform how brands engage with customers and manage their workflows.
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Key features of the AEP AI Assistant
The Adobe Experience Platform AI Assistant boasts several key features designed to enhance enterprise productivity:Conversational Experience: It offers a conversational interface that allows users to interact with Adobe applications more intuitively.
Generative Experience Models: These models are tailored to specific use cases within the Adobe Experience Platform, enabling quick navigation and data processing.
Knowledge Graph: An internal representation of data that translates database queries into human-readable answers, providing insights across various meta-stores.
Operational Insights: In beta, this feature generates answers about metadata objects like attributes, audiences, dataflows, and datasets, without looking at data within the sandbox.
Product Knowledge: It can answer questions grounded in Experience League documentation, aiding in product understanding and troubleshooting¹.
Custom Models: Augmenting base models with customer data, these models provide customer-specific context and can power predictive use cases.
Decisioning Services: These services inform the AI Assistant's responses, offering next steps, recommendations, or multi-turn conversation capabilities.
These features are designed to help users understand concepts, gather insights, generate segments, troubleshoot problems, and ultimately increase the quality and speed of their work while adhering to data security standards.
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