Discover Adobe Experience Platform AI Assistant

Discover Adobe Experience Platform AI Assistant marquee The new AI Assistant in Adobe Experience Platform is a generative AI tool that will redefine how customers work in Adobe Experience Cloud applications like Adobe Real-Time CDP, Adobe Customer Journey Analytics, and Adobe Journey Optimizer. Here is a look at the building blocks behind the natural and insightful conversational experience, and what AI Assistant offers users.

How AI Assistant works

AI Assistant combines generative and non-generative components to unlock the power of data — from both Adobe and customer sources — in Adobe Experience Platform, with user permissions and access controls honored. Here are three key components:

AI Assistant user interface

Users interact with AI Assistant through a user prompt. AI Assistant can interpret natural language and interact with a dialog management service, which guides other models to provide engaging and natural responses. It can rephrase a given question based on conversational context and determine the type of questions being asked to provide a useful answer.

Generative experience models

AI Assistant uses a collection of experience models designed to address the context of each specific AI Assistant use case and allow for quick data navigation as needed.

There are three key dimensions of generative experience models that make it so powerful:

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AI Assistant is powered by data

Data is the vital ingredient that drives personalized insights and engagement. That’s why we launched Adobe Experience Platform to unify siloed data across digital and enterprise sources to build unified customer profiles.

Adobe Experience Platform provides the foundation for AI Assistant to gather a complete view of the business. AI Assistant is grounded in Adobe data, including our product documentation, community forums, and industry or use case playbooks. The tool is also grounded in the customer’s enterprise data and metadata stored within Adobe Experience Platform.

Generative experience models can query these data stores and generate outputs used to answer a question. The data can remain organized, pre-joined, and indexed into customer-specific knowledge bases so language models can interact with it in an open-ended way.

Putting trust at the center

AI Assistant has been developed with enterprise-grade trust, governance, and customer data stewardship in mind. The generative experience models architecture allows Adobe to ensure that the following principles are respected:

As part of generative experience models, Adobe has developed a series of models that help with intent classification, natural language to query expressions, citations, and more. These are internal models that operate within the Adobe ecosystem and allow for controls to continuously improve the correctness of the answers. It also allows Adobe to be very transparent about the internal architecture and keep customers informed.

What AI Assistant can do for you

AI Assistant is designed to make your work easier and more productive, whether you’re a new user just getting familiar with Experience Platform-based applications or you’re an expert trying to streamline your workflows and drive better outcomes. Here are some of the ways AI Assistant is helping drive efficiency for teams in the enterprise.

Campaign and channel marketing teams

AI Assistant helps marketers quickly understand available tools and features within the application they are using to enhance the quality of their work. It can also help them jumpstart the creative process and accelerate campaign creation, content generation, and publishing.

Take, for example, a campaign manager who just recently started using Real-Time CDP and may have gotten stuck trying to activate an audience. They can ask AI Assistant a question like, “What are the next steps to activate an audience?” and AI assistant will provide a tailored answer based on Adobe documentation.

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Marketing operations teams

Marketing operations teams spend a lot of time measuring the performance of their campaigns as well as understanding how to configure journeys, segments, and audiences within their marketing workflows. AI Assistant helps them streamline their work by providing a fast and centralized place to get answers to questions about their marketing workflows. Operations teams will be able to troubleshoot faster based on the insights they uncover and get product guidance to unblock them when they encounter unfamiliar concepts.

An example is a marketing operations analyst who needs to understand if there are audiences that have been created but are not being utilized, or if there are audiences with duplicate definitions. Instead of having to wait for a data specialist to help, they can just ask AI Assistant to provide the list of such audiences. AI Assistant can also visualize the answers to insights questions.

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Data and IT teams

AI Assistant helps streamline routine tasks and accelerate data exploration and insight discovery that can inform and improve the accuracy of processes like pipeline management, data hygiene management, and data lineage impact analysis.

Take, for example, a data engineer who is trying to understand the lineage of their data and needs a graph that shows where a specific field is being used. They can ask AI Assistant to provide a list of segments, schemas, and destinations where a particular data field is in use. They’ll even get the source query used by AI Assistant to generate the answer.

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What’s coming and how AI Assistant will evolve

Adobe is focused on bringing the potency of AI Assistant to a range of use cases beyond the ones described above. The use cases that are generally available or in the works are focused on product knowledge and guidance, troubleshooting, content generation and insights, and help use cases. In the near future, AI Assistant will be able to perform workflow automation and goal-driven tasks for users, surfacing predictive analytics and recommendations that will help optimize business outcomes.

Learn more about what AI Assistant can do for your business.

Akintunde Ajayi is a senior product marketing manager for Adobe Experience Platform. He focuses on bringing innovative Experience Platform capabilities to market and driving awareness and adoption around Adobe’s Personalized Insights and Engagement offerings. He has over 14 years of experience in consulting, systems integration, and product marketing. Akin joined Adobe in 2019 after obtaining his MBA from Kellogg School of Management at Northwestern University. Akin is a devoted husband, father of one, and an ardent fan of Liverpool FC soccer and Utah Jazz basketball.

Horia Galatanu is a director of product management for Adobe Experience Platform. His focus is on building functionality in the areas of artificial intelligence and machine learning, data, and experimentation. Horia joined Adobe in 2007 and has been involved in multiple initiatives over the years, including Adobe Primetime, Adobe Campaign, and Adobe Journey Optimizer. Horia lives in the San Francisco Bay Area with his family and loves hiking, photography, and watching Arsenal FC play soccer.