The way people discover and engage with brands is evolving for both consumers and businesses. While traditional search, websites, and apps remain essential, Large Language Models (LLMs) like ChatGPT and Claude are beginning to influence how customers explore products, ask questions, and make decisions. These AI-powered tools are increasingly present in the customer journey, especially in the early stages, surfacing brand information, summarizing content, and even facilitating conversations.
But for many organizations, these interactions are happening out of view. AI-driven discovery and human to non-human conversations often fall outside the scope of traditional analytics, creating blind spots in measurement and optimization. As a result, brands may be missing key signals about how customers are engaging and where opportunities for improvement lie.
To help close this gap, Adobe Customer Journey Analytics now provides a set of capabilities designed to bring AI-native experiences into the measurement fold. These include tools to analyze branded chat interactions, track how LLMs are referencing brand content, and measure engagement within AI-embedded applications. Together, these capabilities help brands connect AI-driven interactions to real business outcomes so they can better understand, optimize, and act on the full customer journey.
Understanding the role of AI in the journey.
AI is no longer just powering internal tools; it’s becoming part of the customer experience itself. Whether it’s a chatbot answering product questions or an LLM surfacing your brand in a generative search result, these interactions are shaping how customers perceive and engage with your business.
However, unlike clicks or pageviews, these AI-driven moments often go unmeasured. These conversations create an incredible amount of unstructured data that is complicated to analyze and gain relevant and timely insights.
Are brands able to understand how chats are impacting the buying process or whether chat agents are responding optimally for the business? That’s where new analytics capabilities come in, helping brands bring visibility to these touchpoints and understand their impact.
Measuring conversations that drive outcomes.
AI is now embedded directly in the evaluation and buying process. Many customer journeys now include a conversation, often with a non-human agent. These chat interactions can influence everything from product discovery to purchase decisions to service resolution. But are they working and driving the right outcomes?
With new conversation analysis capabilities in Customer Journey Analytics, brands can now:
- Connect chat interactions to downstream behaviors like conversion or churn.
- Analyze sentiment, intent, and topic trends across conversations.
- Understand what led to a chat — and what happened after.
- Optimize chatbot flows and agent performance based on real outcomes.
By transforming unstructured dialogue into structured insights, brands can treat conversations as measurable, optimizable parts of the customer journey.
Gaining visibility into AI-driven discovery.
As LLMs become more common in research and decision-making, they’re increasingly influencing how customers find and evaluate brands. Website content, structured FAQs, product materials, and publicly available information all influence how LLMs describe and recommend your brand. How do you know if your content is being surfaced, represented accurately, or overlooked?
Through integration with Adobe LLM Optimizer, Customer Journey Analytics can now ingest aggregated, non-PII signals from the LLM ecosystem. This allows brands to:
- View LLM crawling activity alongside traditional web traffic.
- Analyze which content types are most frequently accessed by AI tools.
- Compare LLM-driven engagement to human behavior and conversion.
- Track trends in AI discovery over time.
This visibility helps brands understand how they’re being represented in AI-generated responses and how that representation connects to business performance.
Capturing engagement in AI-native applications.
Some brands are beginning to meet customers directly inside AI interfaces through plug-ins, embedded tools, or branded applications within platforms like ChatGPT. These experiences open new doors for engagement, but they also introduce new data.
Using Adobe Experience Platform’s Web SDK and Data Collection APIs, brands can now capture interactions within LLM-embedded applications and bring them into Customer Journey Analytics. This enables:
- Connection of AI app engagement to customer profiles
- Unified analysis across web, mobile, in-store, and AI-native channels
- Measurement of success metrics like conversion, retention, and ROI
These capabilities ensure that AI-native experiences are no longer outside the analytics framework, but are part of a complete, connected view of the customer.
Why it matters.
As AI continues to evolve, it’s becoming a more active participant in the customer journey. Brands that can measure what is working and understand these interactions will be better equipped to deliver relevant, responsive experiences.
By bringing AI-driven discovery, conversation, and engagement into the analytics fold, Adobe is helping organizations close the gap between emerging technologies and trusted measurement. These capabilities empower marketers, analysts, and CX leaders to make smarter decisions faster, based on a more complete picture of the customer journey.
In a world where AI is shaping more of the experience, Adobe is making sure you can see it, measure it, and act on it.
Sandor Jones is a Principal Product Marketing Manager at Adobe, where he leads go-to-market strategy and solution positioning for the Adobe Analytics portfolio. He has been closely involved in the launch of LLM Insights, helping define how brands measure and understand AI-driven customer behavior. With experience at LiveRamp, TUNE, and other analytics-focused companies, Sandor brings expertise in data, measurement, and translating complex capabilities into clear customer value.
Based in the Bay Area, Sandor enjoys reading history, exploring great restaurants in the city, and traveling.
