Everyone’s touting AI-powered DXPs. But true innovation starts with a well-governed content foundation — not just another chatbot.

The Gist

• Two sides of AI in DXPs. Customer Experience AI and Productivity AI drive different types of innovation but share a dependency on clean, structured content.
• Customer-facing AI defines trust. Chatbots, personalized recommendations and predictive assistants shape first impressions — but only work if supported by solid content foundations.
• Productivity AI delivers ROI quietly. Behind-the-scenes automation for tagging, classification, and content workflows offers measurable value when context is well-engineered.
• Structured content is the ultimate differentiator. Agentic AI and Model Context Protocol will only thrive when data, taxonomies and semantics are tightly governed.

I don’t have to tell you that AI is the running hot topic and must-have in the digital experience innovation conversation these past few years. Every digital experience platform (DXP) vendor is shouting from the rooftops about how they have AI-powered this and AI-integrated that.

Still, it’s worth asking: are we actually innovating, or just rushing AI into the DX mix without a solid foundation?

AI in DXPs comes in two flavors:

• Customer Experience (CX) AI
• Productivity AI

The innovation conversation for each is a little different, but there are some pivotal similarities that can’t be overlooked. Let’s explore these two facets of AI in DXP and get into what’s actually innovation and what’s empty words.

Table of Contents

• CX AI: More, Better Customer Interactions
• Productivity AI: More, Better Content
• Your Content Library Is Your AI’s Lifeline
• Model Context Protocol (MCP) and Agentic AI
• AI’s Present and Future Are Content-Dependent

CX AI: More, Better Customer Interactions

As investment in DXPs has continued steadily, and some are asking if DXPs are the MVPs of digital customer experience, integrating AI into these platforms and the experiences they drive is the critical next step to keep investment going. Given that much (but perhaps not as much as you’d expect) of our experience with an organization is powered by their DXP, if CX is going to be improved with AI, it must happen through or in concert with the DXP.

In order of complexity, the primary modalities of experience are:

• Chatbots
• Personalized recommendations
• Search assistants
• Personalized content
• Predictive product or content suggestions

This is the AI your customers interact with directly; think of it like your brand’s AI handshake. You can learn a lot from a first impression. The innovation here lies in making interactions feel personal, relevant and timely without crossing the line into being invasive, creepy, or, heaven forbid, useless. It’s important to think of AI-powered experiences as just another layer of digital experience. Customers ultimately don’t care about the details of your backend architecture or tech stack, they care about a smooth and trustworthy experience.

Still, the echo in the AI dialogue is that if the underlying content library is incomplete, outdated or poorly organized, no sparkling new AI tool will deliver a satisfying digital experience.

Related Article: Digital Experience Platforms (DXPs): What to Know in 2025

Productivity AI: More, Better Content

Less flashy, but the real ROI for AI is often behind the scenes. This isn’t theory, this was one of the primary finding of the widely discussed MIT case study declaring 95% of projects fail. What they saw is what we’ve seen too: backoffice applications of AI produce ROI, even if it’s a bit more work to measure it.

Of course, there are challenges and real hurdles to overcome, but when implemented properly, AI can drive major transformation in content and experience production. This is the AI that helps the daily work of content teams, marketers, product managers, developers, technical writers and anyone working inside the DXP. A few examples come to mind:

• Automated content tagging and classification
• Semantic search across huge content or digital asset repositories
• Workflow recommendations for publishing
• AI-assisted A/B test planning
• Web copy and SEO recommendations

AI’s role here is to streamline operations, collect insights and free up humans for higher-value tasks. A step further, AI properly harnessed makes teams a ton more efficient.

But, yet again, the catch! AI only works well when its context is properly engineered. This means that the inputs surrounding the request to the AI system need to be complete, accurate and well-governed. Doing this at scale is virtually impossible if the content it draws from isn’t well-structured, tagged consistently and stored in an organized manner.

Your Content Library Is Your AI’s Lifeline

This point is repetitive, but it bears repeating: both customer-facing and internal-facing AI agents depend on organized, well-structured and up-to-date content libraries. AI can’t meaningfully personalize an interaction, answer a detail oriented question, or provide significant value if the materials it has access to are haphazardly built and stored. Again, this isn’t theory. We’ve seen organizations improve accuracy rates with AI-agents and conversational systems by more than 40%, some even achieving high 90%s, when leveraging well-managed, structured content libraries.

Well-governed content operations, with clear taxonomies, governance, and robust semantics, build trust into the foundation of your AI agent.

Model Context Protocol (MCP) and Agentic AI

Looking forward, well-managed connectivity is going to be another differentiator for AI in DXP implementations. Model Context Protocol (MCP), the burgeoning standard for connecting AI models to tools, databases and real-time data sources. Paired with Agentic AI, more advanced agents capable of reasoning, planning, and executing tasks, MCP could allow:

• A customer-facing AI to pull accurate, context-rich product info directly from an internal database before making a recommendation.
• An internal AI assistant to coordinate between analytics dashboards, content libraries and CRM tools without a developer building custom integrations.

MCP could make AI in DXPs less single-use and more multi-talented for customers and your internal personnel.

AI in DXP: Key Takeaways

This table summarizes the two main types of AI in digital experience platforms, their functions and what organizations must prioritize to make them effective.

AI Type	Primary Focus	Example Use Cases	Innovation Challenge	Key Success Factor
Customer Experience (CX) AI	Enhancing customer interactions and personalization	Chatbots, personalized recommendations, predictive product suggestions	Delivering relevance without being intrusive or inaccurate	Complete and organized content libraries that power accurate responses
Productivity AI	Improving content creation and internal workflows	Automated tagging, semantic search, workflow optimization, AI-assisted testing	Ensuring contextual accuracy and data quality	Strong content governance, taxonomy, and metadata management
Agentic + MCP-enabled AI	Connecting systems and enabling reasoning-based automation	AI agents pulling live product data, coordinating analytics and CRM tasks	Integrating tools securely and maintaining context fidelity	Unified, structured, and semantically rich content foundation

AI’s Present and Future Are Content-Dependent

AI in DXPs will move toward context-aware, multi-modal interactions. Imagine a customer asking a chatbot about a product, and the AI not only serving an accurate answer, but pulling in the latest video demo, adjusting the answer based on purchase history and offering an instant checkout link.

Learning Opportunities

Internally, AI could become the veritable overwatch of digital experiences, monitoring performance metrics, predicting content gaps, identifying customer data trends and even suggesting campaigns based on market shifts or customer behavior.

But the truth that will outlast every AI-driven wave of innovation (until further notice) is that AI is only as intelligent as the content it’s fed.

Even the most advanced, MCP-connected, agent-powered DXP AI will flounder if its content base is outdated, inconsistent or incomplete. The winners in this next phase of DXP innovation won’t just have the shiniest new AI; they’ll have content operations on lock.

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