Feature
The Gist
- Data, not algorithms, is the roadblock. AI in digital experience underperforms when customer data remains siloed, fragmented or inaccessible.
- Foundations beat features. Unified data strategies, governance and interoperability drive more impact than flashy AI add-ons.
- Culture creates silos, too. Organizational structures and power dynamics often block innovation as much as legacy tech does.
Despite all the hype surrounding AI-powered digital experience (DX) platforms, one critical barrier keeps holding back real innovation: data silos. While vendors promise pain-free, privacy-friendly personalization and real-time orchestration, most businesses still struggle to unify the fragmented data that is spread across legacy systems, marketing tools and customer touchpoints.
Until these silos are addressed, even the smartest AI can only deliver incremental improvements—leaving the true potential of DX unrealized. This article examines why data silos remain such a stubborn challenge for AI in DX and explores what it will take for the industry to finally break through.
Table of Contents
- Introduction: The Hype vs. the Reality of AI in DX
- Why Data Silos Undermine AI in DX
- What AI Innovation in DX Should Look Like
- The Current State: Partial Progress
- Is AI Transforming Digital Experience Platforms in 2025?
- Widespread AI Adoption Across Major Vendors
- Core AI Use Cases Emerge
- Digital Asset Management Gets AI Boost
- Integration Challenges Remain
- Data Quality Becomes Priority
- Tangible Steps Forward But Data Layer Remains Essential
- Where Real Innovation is Emerging
- What DX Leaders Should Do Now
- Conclusion: DX Innovation Under the Hood
- FAQ on Artificial Intelligence in Digital Experience Innovation
Introduction: The Hype vs. the Reality of AI in DX
AI has become the buzzword of choice in DX circles. Vendors tout a future where AI enables hyper-personalized content, frictionless customer journeys and even automated content creation. The sales decks are slick, and the promises are ambitious—and if you believe the marketing, we’re already living in the age of intelligent DX.
But the on-the-ground reality tells a very different story.
The Integration Problem, Not the Model
For most businesses, the true roadblock isn’t a lack of powerful algorithms or access to cutting-edge AI models—it’s the tangle of legacy systems, disconnected data sources and internal silos that stand in the way of innovation.
When Data Can’t Flow, Intelligence Can’t Act
These barriers prevent even the most sophisticated AI from delivering actionable insights or orchestrating personalized experiences.
Legacy Systems Quietly Cap AI Performance
Even as AI solutions promise a more personalized experience, the real barriers often trace back to years of underinvestment in data infrastructure and the persistence of legacy systems.
Victor Horlenko, head of AI innovations at data management solutions provider Devart, told CMSWire that a lot of information, especially for old customers or projects, is separated and stored in old systems that may not be easily accessible to all teams or new, fancy external tools.
"To fix this and give AI 100% power, companies need to fix this first; otherwise, hallucinations and broken AI pipelines will emerge shortly." Horlenko explained that in his experience, businesses that are relying on outdated platforms risk both inaccurate AI results and operational breakdowns, unless they first address the underlying infrastructure.
Fix the Data Layer Before Chasing New Features
Innovation in DX isn’t about who has the flashiest features or the newest model; it’s about who can finally solve the integration problem. Until businesses can unify their data and make it accessible across platforms and teams, the transformative promise of AI will remain just out of reach.
Related Article: Digital Experience Platforms (DXPs): What to Know in 2025
Why Data Silos Undermine AI in DX
AI-powered personalization and journey orchestration have become central promises in the modern DX stack. Yet, the persistent challenge isn’t the AI itself—it’s fragmented, incomplete or poorly integrated customer data. Even as generative AI and customer data platforms (CDPs) have become more sophisticated, many businesses still struggle to unify information from disparate CMS, CRM, commerce and analytics systems.
According to CMSWire’s 2025 State of Digital Customer Experience report, 28% of businesses cite “siloed systems, technology integration challenges and/or fragmented customer data” as a top-three barrier—nearly as significant as budget constraints.
The Top Challenge to AI in Digital Experience—Data Silos and Fragmented Systems
Survey data from the CMSWire State of Digital Customer Experience report shows the leading barriers businesses face when trying to unlock AI’s full potential in digital experience.
| Barrier | % of Organizations Reporting |
|---|---|
| Data silos, fragmented systems, integration challenges | 28% (chief obstacle) |
| Budget constraints | 27% |
| Lack of skilled staff | 24% |
| Data quality issues | 18% |
Personalization Without Unification Feels Generic
The result is predictable: “personalization” that feels impersonal, inconsistent customer journeys and a lack of actionable insights. CDPs are designed to address this very issue by providing a single source of truth—a unified customer profile built by gathering, cleaning and resolving data from multiple sources.
As highlighted in the CDP Institute’s 2025 CDP Market Guide, “the driving need for the adoption of a CDP for most companies is the requirement to consolidate and rationalize customer information that is being held in disparate systems across the enterprise.” Without this foundation, even the most advanced AI/ML-driven tools can only deliver shallow, generic outputs.
Related Article: What Is a Customer Data Platform (CDP)? 2025 Guide for Marketers
Foundations Drive Outcomes More Than Advanced Features
The business impact is significant. The 2025 Generative AI Benchmark Report revealed that companies that fail to master foundational capabilities—such as high-quality, unified data—leave substantial conversion and customer experience opportunities untapped. The report showed that “mastering fundamental capabilities alone drives 2X greater impact on conversions than advanced AI capabilities in isolation.” In other words, businesses that chase next-generation AI without fixing their data silos are building on shaky ground.
Technology Isn’t the Only Problem—Culture Is
Even as businesses invest heavily in AI, the root problem often isn’t technical—it's cultural and structural. In commercial development and construction, especially in cities like New York, fragmented data across SaaS solutions can come with enormous costs. According to Adelaide Godwin, associate vice president at wellness solution provider UpSpring, “privacy and security concerns over LLMs learning from and exposing proprietary intel are likely to further exacerbate the data silos that many companies already face when using multiple SaaS solutions.”
Usable Data Beats More Data
In addition, the challenge isn’t just collecting data, but making it usable.
Brady Lewis, senior director of AI innovation at fractional marketing firm Marketri, explained that most silos are created by “a mixture of legacy infrastructure, organizational culture and inconsistent data standards”—and are often made worse after mergers or acquisitions. “Imagine an AI customer service agent trained to interact with your customers. In this common siloed scenario, the agent is not able to fully, or at least efficiently, unify a customer's data across these disjointed sources,” said Lewis. “This means the agent is likely missing opportunities to understand the full profile and journey of the customer, failing to provide a truly personalized and intelligent solution.”
Silos Downgrade AI From Strategic to Superficial
Lewis explained that data silos not only slow down AI adoption, but fundamentally limit how intelligent or relevant AI-powered customer service can be—turning what could be a pain-free journey into a frustrating patchwork of missed connections.
Related Article: Overcoming Data Silos for Enhanced Customer Experience
What AI Innovation in DX Should Look Like
For years, the conversation around AI in digital experience has centered on new product features and flashy algorithms. But genuine innovation— the kind that actually moves the needle—doesn’t start with more features. It starts with how data is architected, governed and shared across the enterprise.
Leading brands are shifting their focus from “best-of-breed” tool collection to foundational architecture. Instead of chasing the next feature release, they’re building composable systems that enable the orchestration of AI across platforms, channels and data sources. This composability unlocks flexibility: teams can plug in best-in-class AI modules where they’re needed most, without getting trapped in vendor lock-in or “frankenstack” bloat.
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Foundational data problems often persist not due to technical complexity, but because of entrenched organizational structures and data ownership silos within the business.
Deepak Singh, chief innovation officer at intelligent data automation provider Adeptia, told CMSWire, "Marketing owns customer behavior data, sales guards transaction data, and operations hoards logistics information. Each department invests in best-of-breed solutions that excel within their domain but speak different data languages. Major investments often introduce new silos rather than breaking down old ones, as vendors sell transformation while delivering translation layers. The silos persist because they reflect organizational power structures, not technical limitations."
Privacy-Preserving Learning Unlocks Cross-Org Value
Another crucial innovation is the adoption of federated learning and secure data sharing frameworks. By allowing AI models to learn from decentralized data—without ever pulling sensitive information into a central repository—businesses can unlock insights while maintaining privacy and compliance. This approach is already powering breakthroughs in sectors from healthcare to financial services.
The Unified Data Layer Is the Real Product
At the core of all these advances is the unified data layer. Customer data platforms and enterprise data fabrics—modern data architectures built to unify and integrate information across on-premises systems, multiple clouds, and edge locations—are emerging as critical enablers. They deliver the clean, consolidated data that modern AI systems need to generate value. Without a unified data strategy, even the most advanced AI systems risk poor outcomes, as fragmented and low-quality data can undermine reliability, transparency and business value.
True AI innovation is measured not by the length of the feature checklist, but by the ability of your architecture and governance to enable intelligent action. The winners won’t be those with the most tools—they’ll be those with the best-designed foundations.
The Current State: Partial Progress
Vendors across the DX marketplace are making visible strides toward smarter, more integrated stacks.
- Adobe has tightly coupled generative AI (Firefly) with its Experience Platform and CDP, promising optimized content creation and real-time personalization.
- Sitecore has rolled out AI-powered assistants alongside its composable stack, aiming to give brands more flexibility and automation across channels.
- Acquia, with its integration of Drupal and a robust CDP, pitches a unified approach to managing content and customer data.
- Optimizely is advancing content intelligence, tying it directly to experimentation and optimization cycles.
Conversion Impact: Unified Data Foundations vs. Advanced AI Alone
Data strategy proves to be a stronger driver of business results than advanced AI features when implemented in isolation.
| Approach | Conversion Impact |
|---|---|
| Mastering foundational data capabilities | 2X greater impact than advanced AI alone |
| Advanced AI without unified data | Minimal improvement |
| Unified data + advanced AI | Maximum conversion uplift |
Is AI Transforming Digital Experience Platforms in 2025?
Digital experience platform vendors did accelerate AI integration throughout 2025, embedding generative artificial intelligence capabilities across content creation, personalization and analytics functions.
Widespread AI Adoption Across Major Vendors
Leading DXP providers including Adobe, Acquia, Optimizely, Crownpeak, CoreMedia, Progress, Squiz, Zesty.io and Liferay deployed new AI-driven features during 2025. These platforms now integrate with large language models and offer connectors to third-party AI services.
Adobe Experience Cloud expanded its Sensei GenAI capabilities for campaign optimization and content creation. Acquia and Optimizely introduced AI-powered content assistants alongside semantic search functionality.
Related Article: Optimizely Enhances Opal With AI Agent Orchestration Tools
Core AI Use Cases Emerge
The most common AI applications in DXPs focus on four primary areas:
- Automated content generation
- Chatbot functionality
- Personalized marketing campaigns
- Advanced analytics and insights
CoreMedia's KIO suite and Progress Sitefinity's generative AI tools now power real-time personalization and predictive analytics. Squiz invested in Retrieval Augmented Generation technology to enhance search and conversational AI capabilities.
Digital Asset Management Gets AI Boost
AI transformed digital asset management with automated features becoming standard across platforms. Auto-tagging, intelligent image cropping and video transcription now handle routine tasks that previously required manual intervention.
Personalization engines evolved to use unified customer profiles and behavioral analytics, according to platform providers. These systems aim to deliver tailored experiences at scale through data-driven decision making.
Integration Challenges Remain
Most vendors now support API-first approaches to accommodate headless and composable architectures. This allows organizations to integrate AI capabilities across their marketing technology stack.
However, many organizations remain in early stages of strategic AI implementation. Companies currently focus on productivity improvements, accelerated content workflows and enhanced customer experience rather than deeper automation.
Related Article: Inside the AI Engines Powering Digital Experience Platforms
Data Quality Becomes Priority
Platform providers emphasize that AI effectiveness depends on clean data pipelines and governance frameworks. Data quality emerged as a rising priority as organizations discovered that AI capabilities require structured, accurate information to function effectively.
The next wave of innovation will likely center on advanced automation, improved self-service capabilities and more agile marketing operations, according to industry observers.
AI Initiatives by Major DXP Vendors
According to the 2025 SMG Digital Experience Platform (DXP) Market Guide, here’s how leading digital experience platform providers are embedding AI into their offerings.
| Vendor | AI Initiatives |
|---|---|
| Acquia | Generative AI in Drupal for content creation and tagging, GPT-4 developer assistant, computer vision auto-tagging, predictive models, AI-assisted DAM and Responsible AI framework. |
| Adobe | Expanded Sensei GenAI for campaign optimization, content creation and personalization across Adobe Experience Cloud. |
| Optimizely | AI assistant Opal, semantic search, DAM tagging/cropping automation, LLM integration for text and image generation. |
| Sitecore | Sitecore Stream integrates generative AI for ideation, content creation, personalization and predictive search. Full OpenAI integration confirmed. |
| Crownpeak | AI Assistant Suite (content, image, and analyze assistants), integrates with ChatGPT 4 or other LLMs and AI-powered product discovery (Attraqt acquisition). |
| CoreMedia | Launched Personalization Hub and Event Hub for AI-driven optimization; acquisitions expanded automation and marketing personalization . |
| Progress Sitefinity | Generative AI tools powering real-time personalization, predictive analytics and marketing automation. |
| Squiz | Invested in Retrieval Augmented Generation (RAG) for search and conversational AI. |
| Zesty.io | Focused on AI-enhanced automation within its composable CMS, emphasizing efficiency and personalization. |
| Liferay | Embedding AI features into portal and intranet solutions for personalization and analytics. |
Tangible Steps Forward But Data Layer Remains Essential
These are real steps forward. They demonstrate a growing industry consensus that AI must move closer to the core of the stack and that composability is essential for future-ready digital experiences.
However, most of this “innovation” will remain surface-level—enhancing features or improving integrations — if we don't fully solve the underlying data silo problem. Each platform can deliver value in isolation, but cross-platform orchestration and unified data flows often remains more vision than reality for most enterprises.
Despite investment in new tools, the true causes of data fragmentation often go unaddressed, leading to persistent silos and missed opportunities. Horlenko stated emphatically that silos do not arise from a single cause: "They are usually the result of several issues interacting with each other—organizational structures, legacy technology, too many tools, and a lack of shared goals. Even as companies add more solutions, they often neglect to integrate or govern the data, which only perpetuates fragmentation." Horlenko emphasized that without a holistic approach to integration and governance, businesses risk building new silos even as they attempt to innovate.
Although the progress is tangible, the true breakthrough will require vendors (and the businesses that use them) to rethink architecture and governance—not just add AI-powered bells and whistles. Until then, even the best platforms will struggle to deliver on the full promise of AI-powered DX.
Where Real Innovation is Emerging
While much of the market is still tinkering at the edges, a handful of trailblazers are demonstrating what’s possible when AI is built on solid data foundations. The most promising advances are happening where agentic AI systems—autonomous agents—can access and orchestrate data across platforms, rather than being locked into a single tool or silo. When data is truly unified, these AI agents can deliver real-time personalization, automate multi-step customer journeys and even optimize experiences on the fly.
With a clean, consolidated view of each customer, businesses can deliver highly relevant content, offers and support in the moment. Meanwhile, leading vendors are finally embracing cross-cloud interoperability, building bridges between their platforms and third-party ecosystems, rather than reinforcing walled gardens. This move toward open integration is enabling AI to act as the connective tissue across the entire digital experience stack.
Consider the case of a luxury fashion brand based in New York. By migrating to Salesforce Data Cloud, they consolidated customer data from boutiques, outlets, international stores, and ecommerce.
From Market Examples to Leadership Lessons
This unification enabled the creation of richly detailed customer profiles (calculated traits such as purchase frequency and engagement), which allowed the creation of highly targeted email and SMS campaigns. The results were striking—over 40% of their online revenue now comes from these personalized interactions. Behind the impact wasn’t a flashy new AI tool, but clean, accessible data threaded across the stack.
What DX Leaders Should Do Now
For business leaders looking to unlock the true value of AI in digital experience, the message is clear: treat AI as an accelerator, not a workaround. No algorithm, no matter how advanced, can deliver on its promise without integrated, high-quality data as its foundation. The real differentiator isn’t adding the latest AI feature—it’s building the infrastructure and culture that allow intelligence to flow freely across the company.
The path forward starts with a unified data strategy. Brands should invest in technologies such as CDPs, data fabrics and robust APIs to break down silos and enable real-time access across systems—but they should not stop at technology. Establish strong data governance frameworks to ensure that the data remains accurate, consistent and compliant as it moves through the stack. This isn’t just about avoiding regulatory risk—it’s about guaranteeing that every AI-driven decision is based on trustworthy information.
Governance and Culture Matter as Much as Technology
Leaders must recognize that breaking down silos and enabling AI innovation requires a coordinated effort spanning technology, governance and company culture. "Practices such as unified data fabrics, built-in quality controls and shared data ownership help ensure that information flows without duplication or fragmentation,” Horlenko said. “Leaders should avoid over-engineering and vendor lock-in, and instead prioritize interoperability and strong governance from the start."
True innovation requires cross-functional collaboration. IT, marketing, customer experience and analytics teams all have a stake in the quality and accessibility of data. Breaking down internal silos is as critical as solving for technology integration. Leaders should encourage a culture where shared goals—and shared data—drive smarter, faster innovation.
When it comes down to it, and it cannot be reiterated enough—the breakthrough isn’t about accumulating more tools or features. It’s about solving for seamless data flow and accessibility, creating the conditions where AI can do what it does best: deliver relevant, personalized, and truly dynamic digital experiences.
Conclusion: DX Innovation Under the Hood
The future of AI in digital experience won’t be won by those brands that have the flashiest features, but by those who get their data in order. At the end of the day, it’s the businesses that are willing to tackle the gritty work of integration, unify their data and knock down data silos that will unlock real results and value.
True innovation isn’t just about new tech—it’s about building the right foundations so intelligence can actually flow. Until brands focus on making their data work together, all the AI in the world will only scratch the surface of what’s possible.
FAQ on Artificial Intelligence in Digital Experience Innovation
Editor's note: Key questions surrounding AI’s role in breaking down data silos and enabling true digital experience innovation.
To break down data silos, businesses should invest in unified data strategies, such as Customer Data Platforms (CDPs) and data fabrics, which aggregate, clean and manage data across systems. Paired with robust data governance and cross-functional collaboration, these solutions provide the foundation needed for AI to generate accurate insights and drive next-level personalization.
Data silos in DX occur when customer information is isolated within separate platforms—like CMS, CRM, commerce and analytics tools—making it difficult for AI systems to access a complete, unified view. This fragmentation leads to inconsistent personalization and prevents AI from delivering meaningful, real-time experiences, limiting the impact of even the most advanced algorithms.
