AI Assistant in Adobe Experience Platform product documentation

Last update: February 13, 2025

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AI Assistant in Adobe Experience Platform is a conversational tool that you can use to accelerate your workflows with Experience Platform applications and services. Use AI Assistant to boost your overall productivity, amplify your understanding of product knowledge and operational insights, as well as gather recommendations relevant to your queries and business use cases.

AI Assistant supports Experience Platform, Real-Time CDP, Adobe Journey Optimizer and Customer Journey Analytics.

Get started

Get started by reading the material in the links below to familiarize yourself with the concepts and capabilities of AI Assistant.

Understand your data objects
Retrieve operational insights pertaining to your data objects for a better view on their statuses, usage, and linage impact.

Enhance your learning
Discover, troubleshoot, and amplify your understanding of product knowledge without interrupting your workflows.

Visit the AI Assistant video playlist
Learn about the many different challenges and use cases that you can address with AI Assistant.

New features

Get a glimpse of the newest enhancements in AI Assistant, including capabilities currently in the Alpha or Beta stages.

Monitor significant changes and forecast audiences

You can use AI Assistant to monitor significant changes and provide growth forecasts for your audience and dataset sizes. You can then use this information to ensure the integrity of your audience data and offer forward-looking projections to support data-informed decision-making.

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Estimate audience size and propensity

You can use AI Assistant’s natural language estimation capabilities to estimate audience sizes and predict audience propensities, giving you easier access to insights on your audiences.

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Discover XDM fields for audience creation

You can use AI Assistant to help your discover of Experience Data Model (XDM) fields that you can then use to create target audiences within Experience Platform.

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AI Assistant for Customer Support

You can use AI Assistant for Customer Support to seamlessly troubleshoot without leaving your workflows. When needed, support administrators can now use AI Assistant for Customer Support to create customer support tickets, complete with context and session details from your interactions with AI Assistant.

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Security features of AI Assistant

Watch the following video for more information on the security features of AI Assistant:

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Transcript
In this video, we will review the security details for the Adobe Experience Platform AI Assistant. Please note that this video is based on the current security fact sheet located at the link on the screen. If there is ever a discrepancy between this video and the current security fact sheet, then the fact sheet takes precedence. AI Assistant and Adobe Experience Platform is a generative AI tool integrated within native applications built on Adobe Experience Platform. Designed to enhance productivity and help users expand product mastery, efficiently navigate enterprise data objects, and simplify tasks while ensuring adherence to the customer’s organization data security standards. AI Assistant and Adobe Experience Platform can answer questions about product knowledge and operational insights. Adobe’s agnostic approach to large language model enables us to choose the best-in-class technology for the task at hand. AI Assistant and Adobe Experience Platform currently leverages Microsoft’s Azure Open AI service to answer both product knowledge and operational insights questions. There are three key components in AI Assistant. The Adobe Experience Platform user interface. Users interact with the AI Assistant by clicking the icon in the upper right-hand corner of the Adobe Experience Platform UI, which reveals a right rail screen with a text box where the users can enter prompts. Generative Experience Models, or GEMs, the primary brains behind AI Assistant and Adobe Experience Platform. The GEMs include foundation and custom models that power AI Assistant use cases. For the details on the specific models, please see the security fact sheet. Data Services. API services invoked by GEMs to query the data stores that contain relevant data. Data in the data stores is organized, pre-joined, and indexed into a knowledge base, which then enables the GEMs to interact with it in an open-ended fashion. To enable a user to access AI Assistant and AEP, the customer’s Adobe admin must grant specific permissions. For real-time customer data platform and Adobe Journey optimized users, the Adobe admin must grant permissions within the permissions UI of the Adobe Experience Platform. For customer Journey analytics users, the Adobe admin must grant permission for the users to access the AI Assistant within the Adobe admin console. For more information, please review the security fact sheet. Data Encryption. In transit, all data is encrypted in transit over HTTPS using TLS 1.2 or greater. At Rest. Any data stored by AI Assistant is encrypted at rest using AES 256-bit encryption. All data is encrypted in transit over HTTPS using TLS 1.2 or greater. Step 1. User opens the AI Assistant and Adobe Experience Platform user interface. Step 2. AI Assistant authenticates the user with Adobe Identity Management Services, IMS, and checks that the user is entitled to use the AI Assistant. Step 3. Users enter a product knowledge type question in the prompt text box. Step 4. AI Assistant UI sends the prompt text to the dialog management GEM, which classifies the prompt into the appropriate question type, product knowledge, operational insight, or out of scope. If the question is in scope for AI Assistant and AEP, the process moves to step 5. If the question is out of scope, the user receives an error message. Step 5. The dialog management GEM checks with the AEP access control service to confirm that the user is entitled to ask product knowledge questions. Questions outside the scope of AEP and its native applications, including questions about other Adobe products such as Adobe Target and the Creative Cloud Suite, cannot be answered by the AI Assistant in AEP. Step 6. If the user is entitled, the dialog management GEM applies a series of content filters to determine if the prompt adheres to Adobe’s generative AI user guidelines. If any part of the prompt violates these guidelines, the user receives an error message. Step 7. The dialog management GEM then sends the prompt text to the product knowledge GEM, which uses semantic search to retrieve relevant snippets of documentation from the product knowledge data service to answer the question. Step 8. The dialog management GEM combines the prompt text with the retrieved snippets of documentation from the product knowledge data service and sends them to the Azure OpenAI service. Step 9. Before sending the formulated answer back to the dialog management GEM, the Azure OpenAI content filtering service moderates generated responses that violate Azure OpenAI user guidelines. Step 10. The product knowledge GEM cross-checks the answers provided by the Azure OpenAI service against the documentation snippets, adds the appropriate citations, and sends the complete answer and citations to the dialog management GEM. Step 11. The dialog management GEM returns the answer and the relevant citations, along with suggested next prompts, to the user in the AI Assistant for Adobe Experience Platform user interface. Now let’s look at the Data Flow Narrative for Operational Insights. Step 1. The user opens the AI Assistant in the Adobe Experience Platform user interface. Step 2. AI Assistant authenticates the user with Adobe Identity Management Service and checks that the user is entitled to use AI Assistant in Adobe Experience Platform. Step 3. User enters an operational insights type question in the prompt text box. Step 4. AI Assistant sends the prompt text to the dialog management GEM, which classifies the prompt into the appropriate question type, product knowledge, operational insight, or out of scope. If the question is in scope for AI Assistant in Adobe Experience Platform, then the process moves to step 5. If the question is out of scope, the user receives an error message. Step 5. The dialog management GEM checks with the AEP Access Control Service to confirm that the user is entitled to ask operational insights questions. Step 6. If the user is entitled, the dialog management GEM applies a series of content filters to determine if the prompt adheres to Adobe’s generative AI user guidelines. If any part of the prompt violates these guidelines, the user receives an error message. Step 7. The dialog management GEM sends the prompt text to the operational insights GEM, which retrieves a customer agnostic schema and sample queries relevant to the current prompt. Step 8. The dialog management GEM combines the prompt text with the customer agnostic schema and sample queries and sends the data to the Azure OpenAI service, which uses the information to formulate an answer. Step 9. Before sending the formulated answer back to the operational insights GEM, the Azure OpenAI Content Filtering Service moderates generated responses that violate Azure OpenAI user guidelines. Step 10. The operational insights GEM applies the relevant permissions on the business objects present in the query using role-based access control and object attribute level access controls. Step 11. The operational insights GEM runs the query in the context of the customer’s operational insights data service and generates an intermediate response, which is typically a single or multiple row table. Step 12. The operational insights GEM sends the query and the intermediate response to the Azure OpenAI service, which generates the natural language description of the answer and provides the natural language explanation of the query. This step-by-step explanation helps the user to verify the query’s accuracy. Step 13. The dialog management GEM returns the answer to the user. AI Assistant in Adobe Experience Platform and Azure OpenAI. AI Assistant in Adobe Experience Platform currently leverages Azure’s OpenAI to answer customer questions. The following data may be passed to Azure OpenAI to facilitate answering product knowledge or operational insight questions. Experience League Documentation. Information related to the page that the user is on. User’s Conversation History. The prompts and answers. The following data may be passed to Azure’s OpenAI to facilitate entering operational insights questions only. The schema of the tables being queried. Example questions with ground truth queries. Attributes within application business objects such as the name, description, and counts. Adobe has disabled logging in Azure OpenAI, helping to ensure that Microsoft does not collect or review any data sent for processing to Azure OpenAI by the AI Assistant in Adobe Experience Platform. More information is available at the Azure OpenAI Data Privacy and Security link. Adobe does not use any customer data to train or fine-tune the Azure OpenAI service. Chat History. Users can access their AI Assistant in Adobe Experience Platform Chat History, including the prompt and answer for 30 days. Chat History is stored in the same data center as the customer’s Adobe Data Storage location. If a customer would like to delete a user’s chat history, they should contact their Adobe Customer Support representative. Data Usage. Adobe uses customer-agnostic annotated data to fine-tune Adobe internal models. For example, the linguistic models for documentation and the operational insights and classifier models for prompt classification or out-of-scope detection. The responses from these models are not shown directly to the users. Data Processing and Storage Locations. Adobe Identity Management Services. Regardless of the geographic location of the customer, all identity data is stored in multi-region, load-balanced, cloud infrastructure providers with data centers located in North America, Europe, and APAC. Identity data is replicated across all data centers for reliability reasons. All identity data is secured at rest using AES 256-bit encryption in compliance with the Adobe Common Controls framework and meets our internal policies for encryption and storage of sensitive data. AI Assistant and Adobe Experience Platform and Azure OpenAI Service. All server-side components of AI Assistant and Adobe Experience Platform and corresponding data storage are co-located in the same region as the customer’s Adobe Experience Platform service infrastructure, which is determined upon initial provisioning. Data sent to the Azure OpenAI Service may be processed in a different data center but located within the same geographical region, per the tables that are in the security fact sheet. Questions. If you have any additional questions about the security posture and capabilities of Adobe Experience Platform, native applications, or AI Assistant in Adobe Experience Platform, please contact your Adobe account manager. For all other questions about Adobe security programs and processes and compliance certifications, please visit the Adobe Trust Center. Also, be sure to bookmark the security fact sheet for Adobe AI Assistant so that you can refer to it in the future.

Read the AI Assistant security fact sheet

For more information about AI Assistant, read the security fact sheet for AI Assistant in Adobe Experience Platform.

Video library

Refer to the videos below to further amplify your knowledge on AI Assistant capabilities and use cases:

Get to know AI Assistant

Watch the following video for an overview of AI Assistant.

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Introducing AI assistant, a natural language interface built into Experience Platform designed to enhance productivity, expand product mastery, and help users efficiently navigate enterprise data objects. But what exactly can I assistant help with? It can quickly share product knowledge, helping users learn concepts and troubleshoot, and it can provide operational insights to assist with lifecycle management, impact and value analysis. All product knowledge answers are verifiable and cited, linking to product documentation.

The suggested prompts make it easy for me to continue the conversation to get an overview of capabilities. I can head over to the discoverability panel where they are clearly outlined. Now that I know what I assistant can help with, I’ll use it to help me clean up my environment. Let’s figure out which of my audiences have never been used in journeys. Wow, that was so easy. Before, I would have had to look through all of my journey definitions to identify used audiences, and then find the remaining audiences that were not used. It would have taken me hours and this took seconds to get a list. I can see how my question was interpreted to ensure no misunderstandings. Navigate to the audience page. Review the step by step process that generated the answer, and even see the SQL that ran behind the scenes. This really helps me trust the results. Before I consider getting rid of some of the unused audiences I just uncovered. I want to see if they’re being used by other audiences. Oh well, it looks like three of them are. I’ll be sure not to delete those.

I am interested to find out what attributes are used in the in segment audience. Isn’t it nice to have all the information at your fingertips? Next, I’ll identify unused journeys. It makes it easy that I can navigate directly to journey. Optimize from the response. Last, on my hygiene journey, I’ll ask AI assistant. Best practices for deleting a schema AI assistant gives me all the information I need, including contextualizing the information for my specific sandbox.

I have learned so much from AI assistant, and I have a rich chat history that I can go back to and visit. AI assistant truly is a shortcut for getting value out of experience. Platform.

For more information, read the AI Assistant UI guide.

Get access to AI Assistant

Watch the following video to learn how to configure access to AI Assistant for your organizations and users.

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For more information, read the AI Assistant access guide.

Understanding product knowledge in AI Assistant

Watch the following video to learn about product knowledge in AI Assistant.

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AI Assistant in Adobe Experience Platform is a conversational experience that you can use to accelerate your workflows in Adobe applications. AI Assistant responds to your submitted questions by querying a database and then translating data from the database into human readable answers. It’s important to note that the product context you are in will determine which products the AI Assistant will consider when returning information. For example, while you are in Adobe Experience Platform, you will get the best result asking questions specific to Adobe Experience Platform. If you want to ask questions about customer journey analytics, your results will be better if you first navigate to CGA within the UI before using AI Assistant. In this video, we are going to explore what AI Assistant can provide in the areas of product knowledge. Product knowledge refers to concepts and topics grounded in experience-led documentation. Product knowledge questions can be further specified into the following subgroups, pointed learning, open discovery, and troubleshooting. With that in mind, let’s get started. If you are not sure what the AI Assistant can do, why not ask it that? Let’s ask, what can the AI Assistant do? As you can see in the response, AI Assistant can guide you with learning concepts and continuing workflows to help ramp up your skills and knowledge, troubleshooting, learn how to debug basic errors that you might encounter, sandbox hygiene, leverage those capabilities to keep your data optimized, value analysis helps you find your most used data objects. Oh yeah, you can search too. You can find information on your audiences, datasets, destinations, schemas, and sources. Be sure to check out the related suggestions at the end of the responses as well. These often can help guide you to great follow-up questions or help inspire you to think through what to ask next. If you are still stuck and not sure what to ask, click on the lightbulb icon to enter the discoverability view. On this view, you will find even more suggested questions that are broken out into categories like operational insights for audiences, datasets, and more. Also there are categories for more product knowledge questions specific to pointed learning and troubleshooting. At the end of the responses, make sure you are checking out the sources of the response. You can show the sources, see the inline citations, and even click on the links to read even more on the topic of interest. But wait, there’s still more to see. Let’s take a look at some of my favorite bonus tips or features that I rely on when using AI Assistant. What’s new? I like to ask what’s new in AEP to see a highlight of the most recent updates and features. This can help you stay current with the evolution of the Adobe Experience platform. Ask for steps. Help AI Assistant help you by being as specific as possible when crafting your questions. Instead of just asking how do I create an audience, ask what are the steps to create an audience. This will provide a set of instructions for you to follow and accomplish the task. Ask anyway. Sometimes we ask AI Assistant questions that it flags as out of scope and returns a message that informs you of this and offers some suggestions on adjusting your question. But you can leverage the ask anyway suggested prompt to bypass the out of scope message and try your question anyway. Sometimes you might land on answers that you are looking for, but be sure to double check the sources used in the answers. Your mileage may vary, but it’s worth a shot. Hopefully you now have a better understanding of how to use AI Assistant within Adobe Experience platform to expand your product knowledge. What are you going to ask Adobe AI Assistant?

For more information, read about product knowledge in AI Assistant.

Operational insights in AI Assistant

Watch the following video to learn how you can use AI Assistant to retrieve your operational insights and gain a comprehensive view on your data objects.

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AI Assistant and Adobe Experience Platform is a conversational, generative AI tool that redefines how customers work in Adobe Experience Cloud applications, such as Adobe Realtime Customer Data Platform, Adobe Customer Journey Analytics, and Adobe Journey Optimizer. A key capability of AI Assistant is being able to uncover operational insights. Operational insights refer to answers AI Assistant generates about the metadata of a customer’s objects, including counts, lookups, and lineage impact, without looking at any of the end customer or account data within a customer’s sandbox. This video is going to give you an overview of what operational insights within AI Assistant and Adobe Experience Platform can do for you. Some example questions to ask AI Assistant to showcase operational insights are, how many data sets do I have? How many XDM attributes have never been used? Which audiences are used in Journey X? Show me audiences that are duplicated. You can also find additional supported questions in Experience League and in the suggested prompts in the response from AI Assistant itself. The key use cases that we are going to look at in this video are data management, audience management, and journey management. So let’s start with data management. AI Assistance helps users uncover the lineage between schemas and data sets and gain a deeper understanding of how experience data model fields are utilized. Users have reported saving hours each month when they use AI Assistant to keep track of data in their platform. Let’s ask AI Assistant how many data sets have been ingested using the same schema. Please note you can also take advantage of Autocomplete with an AI Assistant to help you refine and tailor your query. Once our response is returned, remember that you can always expand the table, download it as a CSV. Also make sure you go and look at the sources. You can view the query, the behind the scenes SQL query that was running against the Knowledge Graph, to find exactly the answers that it brought back to you. Also be sure to check out the related suggestions. You never know what nugget you may uncover there. Next up is audience management. With AI Assistant, users can gain insights into where their audiences are being used and maintain data hygiene to ensure the accuracy and relevance of their audience inventory. Users have reported saving 12 hours each month that they use AI Assistant to manage their audience inventory. Let’s ask AI Assistant, show me duplicate audiences based on definition. Now that we have our response, you can see that AI Assistant again has brought back the duplicate and again you can view the query, look at the suggestion, make sure you check out the sources, and also make sure you can look at Autocomplete to help you fill out those queries. Finally, let’s take a look at journey management. AI Assistant empowers users to track the number of active journeys, identify the audiences used within each journey, and maintain journey hygiene. Customers can expect to save hours when using AI Assistant to manage their journeys. Now let’s ask AI Assistant what audiences are activated to journey x. What is journey x? I’m not sure either, but with Autocomplete it’ll help us find the names of those journeys even if we don’t know what they are ahead of time. So here we go, which audiences are activated to journey? Right here provides a suggestion and once you click on this provide a drop down of the journeys here. As always be sure to check out the sources and related suggestions. Now you should have a better understanding of how to use the Operational Insights for AI Assistant within Adobe Experience Platform to help you be more efficient when working with Adobe Experience Platform. What are you going to ask AI Assistant?

For more information, read about operational insights in AI Assistant.

Use AI Assistant product knowledge to reduce onboarding time

Watch the following video to learn how you can use AI Assistant product knowledge to reduce onboarding time.

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For more information, read about product knowledge in AI Assistant

Use AI Assistant to de-clutter your audiences

Watch the following video to learn how to use AI Assistant to de-clutter your audience and optimize your marketing operations.

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Let’s take a look at how AI Assistant and Adobe Experience Platform can help optimize marketing operations by decluttering audiences. I need to find all of my unused audiences that clutter up my system so that I can keep my audience inventory clean and optimized. To solve this problem, I would normally have to manually review each audience and identify all of those that have zero profiles. I have to check other campaigns and audience lists to confirm that these audiences are not being used in any other destinations before I can proceed with their deletion. With the new AI Assistant, I can ask these questions directly to the AI Assistant and it will provide me with a list of hyperlinks that I can act on from right within the responses. Let’s see this in action. I am going to ask the AI Assistant to help me find which of my audiences have zero profiles. Once AI Assistant has returned with the list of audiences with no profiles, you can expand the list to see more, download the full CSV, or use the links provided to go right into the audience details to confirm. Next, let’s ask the AI Assistant which of my audiences have never been activated to a destination. Again, we can explore this response, download the full list, and click through to the audience to see the details. Next, you know we’re feeling good about this information that we have found, but just to make sure that we’re doing all the steps properly, we can even ask the AI Assistant to provide a list of best practices for deleting an audience, just to make sure we have all of our I’s dotted and T’s crossed. Now we are ready to proceed. Let’s take this one more step and let’s get AI Assistant to find us a list, a combined list of audiences that have never been activated and have zero profiles. With this enhanced prompt and response, we can go through the list and start removing the clutter to streamline and clean our audience inventory, making it easier and more efficient for us to find high value audiences and activate them to our various destinations. You too can increase your operational efficiencies by using AI Assistant just like I did.

Use the discoverability panel to help you get started

Watch the following video to learn about the discoverability panel in AI Assistant, and how you can use it to get started with AI Assistant

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Sometimes the hardest part of using AI Assistant is just knowing where to get started. That is where the discoverability panel within Adobe’s AI Assistant comes in. Just click on the light bulb at the top of the AI Assistant to start using the discoverability panel. On this view, you will find suggested questions that are separated into categories like operational insights for audiences, data sets, destinations, and more. Also, there are categories for product knowledge questions specific to pointed learning and troubleshooting. Let’s try one of the suggested questions out. Which audiences have zero profiles? Click on the suggested question you want to try. This will copy the question to the dialog box where you could make any modifications if you wanted to. Then submit the prompt. A few moments later, and now we have our list of all the audiences with zero profiles that we can now take action on. Take advantage of these suggested questions to get a better understanding of how AI Assistant with an Adobe Experience platform can help you.

Use AI Assistant to validate responses

Watch the following video to learn how you can use AI Assistant to verify and validate responses.

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In this video, we’re going to show you how to verify and validate the responses that AI Assistant within Adobe Experience Platform provides. It’s important to note that questions about product knowledge and operational insights have different methods and mechanisms to verify the results. Let’s look at these now. We’re going to start with a product knowledge question. How does RTCDP manage consent for profiles? Once we have the response, you are able to see that AI Assistant provides links to Experience League under the Show Sources section. These links have inline references in the response, and we also encourage you to click on these links to not only verify the response, but to also read more about the subject. Now let’s look at the verification process with an operational insights question. I’m going to ask the AI Assistant how many audiences are activated and show me their names. Once the results are displayed, you can click on any of the audiences. This will load the audience view, and you can verify that it is indeed activated to a destination. Back in the results, it shows you how the prompt has been interpreted along with a step-by-step explanation on how that response was generated. In addition, it also generates a well-documented SQL query which explains the logic around the query. Please note, this is the query against the AI Assistant’s Knowledge Graph and therefore cannot be used to run inside of Query Service. If you happen to encounter an issue with a response, or you find the perfect answer, please use the in-product feedback features to let us know how AI Assistant is performing. With this better understanding of how AI Assistant helps you verify and validate the results, you can better refine your prompts to get the most out of AI Assistant. Be sure to also check out these additional resources for AI Assistant.

For more information, read the documentation on verifying AI Assistant answers.

Use AI Assistant for impact analysis

Watch the following video to learn how you can use AI Assistant to execute more effective impact analysis before any changes are made to your data objects.

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In this video, we will look at how Adobe AI Assistant within Adobe Experience Platform can help you be more effective with impact analysis. Changes to shared objects like attributes and audiences can disrupt workflows for other teams, complicating processes, and risk important marketing efforts. With AI Assistant, you can quickly map relationships between objects by asking questions about lineage and impact before any changes are made. Let’s see this in action. So I’m working in the marketing department for a financial services company and I need to determine what the impact would be to update just one attribute in my data feed, Homeowner. So let’s find all of the schemas and data sets where the attribute Homeowner is used. As you can see, this returns the data we’re looking for. Don’t forget that you can expand this table and even download the results as a CSV if you want. Okay, now we need to find out if there are any audiences that also use the Homeowner attribute. Let’s try this. What audiences use the attribute Homeowner? Looks like there is one audience that does indeed use this field, high earning homeowners. Now that we have this, let’s see if this audience is used anywhere else. So which audiences, journeys, and destinations use the audience high earning homeowners? As you can see, this audience is used in one destination. Previously, data professionals had to manually track where an object was used and analyze the impact of updating it. This was a time consuming and error prone task that could take days and we accomplished this task in just a few minutes. As your organization grows and more teams collaborate across complex systems, the risk of overloading or incorrectly updating a field increase. With AI Assistant, you are not only going to optimize your operations, but ensure every change is informed and precise. This simplifies a tedious process and empowers data professionals to make confident decisions for effective marketing strategies. AI Assistant is now available in real time CDP, Adobe Journey Optimizer, and Customer Journey Analytics. For more details on getting access, visit access AI Assistant in the Bobi Experience Platform. We’ll continue to level up your AI skills by trying out these recommended prompts.

Use AI Assistant for Customer Support

Watch the following video to learn how you can use AI Assistant for Customer Support to seamlessly troubleshoot without leaving your workflows.

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AI Assistant for Customer Support is an always-on AI agent that can help resolve customer care issues by leveraging a vast array of knowledge sources curated by Adobe’s expert support teams, product legal documentation, and tutorials, and even your own data to enable you to troubleshoot within your own workflows. If you need additional help, AI Assistant for Customer Support can now create a support case with detailed contextual information and issues details, greatly reducing the time and work required to create support tickets. Please note that the ability to create support tickets is governed by the access control policies of your organization. For additional information, contact your Adobe administrators. Let’s take a look at the capabilities of AI Assistant for Customer Support. The underlying knowledge graph has been expanded to include knowledge articles that are curated by Adobe’s expert support teams. So let’s ask, why does my profile account differ on the license usage dashboard and the AEP homepage? As you can see, once the response is returned, we have even more detailed information, and if we scroll down and view the sources, we get direct links to the customer knowledge support articles. Let’s ask another one. What are the reasons for a journey not triggering? Once again, you will see that the detail of the responses is even greater, and as always, you can follow the links in the sources to learn even more. In addition, AI Assistant for Customer Support will now let you video content in the responses to enable easier understanding. Let’s ask AI Assistant, how can I change the status of a journey? In the response, you will now see that a video is embedded that is set to begin at the exact time most relevant for the question. All you have to do is follow along. Another great addition to AI Assistant for Customer Support is that supported users now have the ability to create and manage support tickets from within AI Assistant, and even better is contextually aware of the questions that you have been asking AI Assistant. It will capture the sandbox, the org ID and user ID, which adobe product, and even a summary of chat for the ticket description, and a chat history to not only streamline the creation of the ticket, but the resolution as well. You can even ask AI Assistant to check on the status of the support ticket as well. What’s the latest on my case? AI Assistant will provide you with a status update in the response. AI Assistant for Customer Support provides you with even more tools to help you overcome obstacles in your workflow. Hopefully, you now have a better understanding of how to use the AI Assistant for Customer Support within Adobe Experience Platform. What are you going to ask Adobe AI Assistant?

For more information, read the documentation on using AI Assistant for Customer Support.

AI Assistant use case library

Watch the following videos for additional AI Assistant use case examples:

Browse the links below to further your understanding of AI Assistant use cases, capabilities, and much more.

Real-Time CDP
Documentation - UI guide - Access AI Assistant - Privacy, security, and governance - FAQ		Adobe Journey Optimizer
Documentation		Customer Journey Analytics
Documentation

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