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- Enhanced team collaboration. Break down content silos by integrating teams and systems, fostering real-time collaboration and reducing redundant efforts.
- AI-driven insights. Utilize AI to analyze and optimize content, uncover gaps, and improve efficiency, leading to better customer service and productivity.
Can generative AI support the evolution of digital experience platforms (DXPs)?
This article examines how these AI-powered advancements are impacting DXP functionalities, examining real-world applications and the effect on customer journeys and digital touchpoints.
What Is a Digital Experience Platform?
Let's step back for a minute. A digital experience platform, also referred to as a digital customer experience platform and sometimes as just a CMS, is defined as an integrated set of core technologies whose purpose is to support the creation, management, delivery and optimization of tailored digital customer experiences.
At CMSWire, we often look at four facets of a platform:
- The authoring and creator experience — what it's like to model, author, edit, version and collaborate on content and experiences in the platform
- The platform's ability to deliver content and experiences — what's it like to try to deliver and optimize personalized experiences across channels
- The platform's flexibility, composability and general architecture — what's it like to host, access, scale and integrate with the platform
- The ecosystem around a vendor and platform — what it's going to be like living with and owning a platform
Historically speaking, what we now know as a digital experience platform evolved in roughly three phases:
- Early Days of the Web: Content Management Systems (CMS or Web CMS)
- Web 2.0 Days: Web Experience Management (WEM)
- Modern Day: Digital Experience Platforms
Related Article: Digital Experience Platforms (DXPs): What to Know in 2024
The Emergence of Generative AI in DXPs
Early digital experience platforms evolved from basic content management systems (CMS) designed for static, manually-updated websites. Over time, businesses began using DXPs to manage personalized experiences, but they were limited by manual processes and siloed data.
As businesses push for competitive differentiation through customer experience, generative AI may reshape DXPs beyond their origins as simple CMS tools. Current AI-driven DXPs attempt to deliver rich, dynamic experiences tailored to individual users. For example, brands like Adobe and Salesforce have integrated generative AI into their platforms to accelerate content creation, boost engagement and automate workflows.
A 2023 Gartner report revealed that the DXP is quickly becoming the core technology of digital transformation. The promise of generative AI is, well, promising: it can streamline complex tasks, freeing teams to focus on strategic, creative work that meets evolving customer needs.
Combine this with DXPs, and the potential is real — though many practitioners say they are still taking baby steps with generative AI in the DX stack.
DXPs are still very much a part of the martech roadmap. A 2023 Statista report indicated that by 2025, the revenue of the DXP industry would be $16.98 billion.
Alex Li, founder of StudyX.AI, an AI education company, told CMSWire that AI can quickly present high-quality personalized content through the automation of content generation and the optimization of workflows.
"This not only reduces the time and energy in content creation but also improves the flexibility to respond to market changes," Li said. "For example, businesses can generate marketing materials based on real-time data to ensure that their content always meets audience needs."
Snapshot of Vendors Providing AI in Digital Experience Tech Stack
DXP vendors such as Adobe (Adobe Experience Cloud) and Contentful provide AI infusions into the DX stack that generate personalized content at scale, tailored to individual customer preferences and interactions.
Generative AI may give a boost to the personalization capabilities of DXPs by delivering hyper-targeted content, recommendations and interactions that are tailored based on real-time data and customer behavior patterns. DXPs such as Optimizely DXP, Sitecore and Acquia use AI algorithms to analyze user preferences, delivering individualized experiences across multiple channels.
Generative AI is also enhancing customer journey mapping by analyzing vast amounts of customer data and generating actionable insights. Tools such as Salesforce Experience Cloud, Bloomreach and Liferay DXP automate personalized touchpoints. These platforms use AI to optimize each step of the customer journey.
Generative AI is enhancing search and recommendation systems within DXPs. DXPs such as Adobe Experience Platform and Salesforce Experience Cloud integrate AI-driven search and recommendation features through tools like Adobe Sensei and Salesforce Einstein, enabling brands to adjust search results and recommendations based on individual preferences and past interactions.
Similarly, Bloomreach Experience and Sitecore Experience Platform incorporate ML models to refine search relevance, tailoring recommendations to users’ real-time actions and preferences. These DXPs employ generative AI and predictive algorithms to analyze vast data sets, allowing them to anticipate what users are looking for.
AI is also playing a pivotal role in multichannel orchestration by enabling businesses to manage and deliver consistent, personalized experiences across multiple touchpoints, including web, mobile, email, and social platforms. Tools such as Adobe Experience Manager and SAP Commerce Cloud allow businesses to leverage AI for synchronized messaging.
Automating Content Creation at Scale
The release of ChatGPT 3.5 in November 2022 marked a major shift for marketers, enhancing both the quality and sophistication of AI-generated content. Each subsequent update has improved the tool's depth and readability, changing how marketers, copy editors and advertisers create engaging material.
Today, a multitude of generative AI applications—including Google Gemini, Microsoft Bing, Anthropic Claude, Perplexity, OpenAI Dalle-3, Midjourney, and many others—are streamlining content creation by automating the production of digital assets, including marketing copy, articles, images and videos.
Matthew Doherty, EXTE North America CEO, which is an adtech platform for the open web, told CMSWire that generative AI has had a huge impact on his business.
"Generative AI plays a pivotal role in EXTE's tech stack, and for nearly a decade, our services have also relied on our patented contextual AI technology, which allows us to automatically scan, categorize and cash in contextual association for more optimal targeting."
Sure, Generative Automates Content. But Is It Any Good?
David G. Ewing, co-founder and CEO of DXP ContentLion, told CMSWire that generative AI helps create and manage digital content at scale, but the key isn't just generating content but generating on-brand content that leads to conversions.
"Generative AI can produce articles on specific topics. However, we've found it lacks a feedback mechanism to learn how well that content performs,” said Ewing. “In my experience, generative AI content often lacks creativity; it feels like a high school essay rather than an engaging story. It's not going to spontaneously craft an amazing narrative that's relatable.”
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Ewing related that when it comes to using generative AI to create and manage high-quality digital content at scale, there are still challenges. That said, many generative AI tools now have the ability to create high-quality videos, articles and images that adapt to customer needs at scale, often with remarkable accuracy.
Enhancing Personalization and Customer Experiences
Personalization has played a greater role in the customer experience in the past decade. As technology has progressed and more businesses have stepped up to the plate, consumers have come to expect a greater level of personalization in their interactions with the brands they do business with.
Raviraj Hegde, SVP of growth at Donorbox, a non-profit fundraising software provider, emphasized that generative AI has provided profound capabilities for analyzing his brand’s donor behaviors and preferences, making it easier to craft targeted outreach campaigns. “For instance, we use AI to auto-draft customized donation appeals reflecting what a donor has shown past interest in,” said Hegde. “This has led to higher engagement rates since the content feels more relevant and meaningful to our audience.”
The ability of machine learning (ML) models to process data in real-time is central to personalization. Real-time data processing in this context means that the model can analyze incoming data—such as a customer’s actions on a website, purchase history, location and browsing behavior—within milliseconds.
Where Tools Like Apache Kafka, Amazon Kinesis Feed AI in DXP Engine
This process often uses streaming data architectures, like Apache Kafka or Amazon Kinesis, which can process high volumes of data almost instantly. With these frameworks, businesses can continuously stream and analyze customer behavior, immediately detecting actions and preferences to update the experience accordingly.
Once trained, these models are integrated with the DXP through APIs that enable fast, bidirectional data flow between the platform and the ML models. For instance, if a user shows a pattern of engagement with certain products, the DXP’s API can call on the recommendation engine to adjust the displayed content, such as showing similar items or highlighting a related promotion. This requires a robust infrastructure where data from various sources—CRM, website interactions, social media engagements—feeds back into the model, which updates in real-time or in near-real-time batches.
Improving Customer Journey Mapping
Doherty said that his business’ AI tech enhances the customer journey through the automation of data analysis, identifying patterns in customer behavior, and providing real-time insights.
"This data and pattern recognition helps businesses to personalize interactions, optimize journey pathways and anticipate customer needs for a more satisfactory, targeted, and data-driven customer experience," Doherty explained.
By automating data analysis and uncovering patterns in customer behavior, generative AI offers brands the ability to map journeys with precision. L’Oréal, for instance, uses an augmented marketing strategy that leverages generative AI to create customized beauty solutions and enhance the creative process. Brands are using AI-driven insights to identify points of friction and proactively address them, resulting in smoother pathways to purchase. Such data-rich mapping will be pivotal in an era where customers expect effortless, highly personalized experiences with minimal friction.
Wes Kempa, manager of sales engineering at Liferay, a DXP cloud platform provider, told CMSWire that generative AI helps his business analyze massive amounts of data to understand customer behavior and preferences across channels.
"This allows us to personalize journeys, recommending relevant content or products at the right time,” said Kempa. “We've seen increased engagement and conversions as a result of these targeted, data-driven experiences.”
Liferay DXP, for instance, is integrated with AI-powered chatbots, which facilitates personalized customer support and quick issue resolution.
Kempa said that AI is transforming how Liferay uses real-time content orchestration, ensuring that messages are tailored and adaptable across channels.
“Generative AI will enable businesses to quickly connect and adapt content across channels, creating fluid, real-time, personalized experiences,” said Kempa.
Related Article: Navigating the Evolution: From CMS to DXP in the Digital Age
Driving Intelligent Search and Recommendations
FutureCommerce's recently released State of AI report revealed a huge shift in shopping behavior: 42% of consumers use ChatGPT for purchase decisions, with 10% preferring it over Google for product research. The study also found that 71% of shoppers have embraced AI-powered recommendations, signaling growing consumer trust in AI-curated shopping experiences.
That said, generative AI search has not yet fully realized the level of deep personalization that many brands aspire to. As Ewing noted, while AI excels in customer support and product selection, “a truly personalized generative AI search or recommendation model is not yet available.” He envisions a future where AI could “figure out your space requirements, aesthetic preferences, budget, and even analyze a photo to help determine the best couch for you,” providing the kind of personalized experience consumers will come to expect. This represents the next frontier for AI-powered personalization.
The Road Ahead: AI-Powered Innovation in DXPs
Generative AI could reshape DXPs by automating content creation, enabling greater levels of personalization and enhancing multichannel orchestration. The real ROI on these implementations in the DX stack? That's to be determined. We're still bracing for those case studies, and our travels tell us tales of cautious optimism with AI in the DX stack.
Although challenges persist in creating hyper-personalized experiences, the technology could improve customer journeys, search capabilities and consistency across digital touchpoints.
As Kempa predicted, “Generative AI will be central to the future of DXPs, driving innovation in multichannel orchestration and immersive experiences.”
