Adobe Experience Platform gets AI assistant for customer data insights

Adobe has unveiled a generative AI (genAI) assistant for Adobe Experience Platform (AEP) apps, a conversational interface that the company said would simplify access to customer experience data.  

The AI assistant, now available, lets a wider range of employees access and interact with customer data for marketing purposes, answering technical questions, and automating tasks, according to Anjul Bhambhri, senior vice president, Adobe Experience Cloud.

“We are helping brands become more productive, and taking away the dependence on a small set of subject matter experts to make everybody an expert,” she said.

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AEP is the data layer that underpins a suite of customer experience analytics tools to provide insights into how customers interact with a brand. Apps built on AEP include Adobe Real-Time Customer Data Platform, Journey Optimizer, and Customer Journey Analytics. 

The conversational AI assistant, which is embedded on the right-hand panel in each of the AEP apps, has three main functions. One involves product expertise and usage insights.  Users can ask the assistant technical questions relating to the use of tools within the AEP suite, such as “What is an identity map?” or “How do I build an audience segment?” 

“As opposed to somebody having to comb through a lot of documentation, this gives you the right information that you need, just when you need it,” said Bhambri. 

For usage insights, it’s possible to get information about customer data, audiences and customer journeys without running SQL queries. A user could ask the AI assistant, for example, which attributes in a customer profile are leading to a conversion, said Bhambri, the sort of task that might require contacting different colleagues and consulting multiple documents. 

Unlike other aspects of the launch, the ability to ask for operational insights is still in public beta, Adobe said. 

The AI assistant can also be used to create content. In addition to creating audience segments based on a customer’s own data, it’s possible to produce assets that can be included in an email marketing campaign, for instance. In this case, Adobe’s Firefly image model could be used to generate images and design layout within the relevant AEP app. The ability to quickly create content variations allows for greater experimentation, Bhambri said, and can help personalize communications to different audiences. 

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Finally, the AI assistant can provide “predictive insights” and recommendations about  customer data with simulated scenarios based on historical data. This could mean predicting how many conversions to expect from a particular customer segment, with references to data sources to show how the decision was made.  It can also recommend actions in the customer journey based on historical trends. 

Underpinning the AI assistant is what Adobe calls a generative experience model. This comprises “base models” trained on data such as product information, community forums, best practices, and custom models AEP customers can opt in to. Custom models are trained using AEP customer data to provide more context for AI assistant outputs. The third element includes large language models (LLMs) that enable natural language interactions with the AI assistant; customers can select an LLM of their choice for this purpose, Adobe said.

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