David San Filippo is Senior Vice President of Digital Content and Experience at Altudo and a CMS Critic contributor.

On November 30, 2022, OpenAI released ChatGPT as a “research preview,” and almost overnight, public perception shifted: the implications for creative work became impossible to ignore. What was once a specialized tool for researchers and early adopters became a broad lever for content creation, triggering a ferocious debate. If writing, designing, and ideating could now be mass-produced by machines, what happens to creativity, to value, and to who controls narrative?

Voices from prominent technologists have made bold declarations. In The Wall Street Journal, an Andreessen Horowitz leader argued that generative AI is bringing “the cost of creation close to zero.” In parallel, Spotify’s CEO similarly claimed that producing content is nearing free. These assertions have tended to frame the issue as a fight over creativity, authorship, and value in art. But there’s a deeper, more structural question brewing beneath, one about how enterprises must evolve to survive the coming deluge of digital material.

We are living through an information explosion on a scale that dwarfs human history. As Eric Schmidt once observed, “There were 5 exabytes of information created between the dawn of civilization through 2003 — but that same amount is now created every two days.” Meanwhile, global data volumes have ballooned: 90% of the world’s data is now estimated to have been generated in just the past two years. The reality is that we are drowning in content, and generative AI promises to pour gasoline on that fire.

This is the heart of the Zero-Cost Content Paradox: as the marginal cost of producing content falls toward nothing, the cost of managing it rises exponentially. The challenge for organizations is no longer whether they can create content, but whether they can orchestrate, govern, and optimize the torrent that AI makes possible. Every new variation adds another thread to the fabric of the customer journey, and without a way to weave those threads together, the experience quickly unravels.

Rethinking Customer Journeys

Consider the experience of a single prospect moving through a buying journey. Their day may begin with a personalized email shaped by their past browsing, leading them to a microsite tailored for their segment. Later, a search delivers a paid ad tuned to their industry, followed by a LinkedIn post that resonates with their role. In the evening, a chatbot suggests a bundled package designed around their unique context. And the next morning, they walk into a physical store or sit down with a sales rep who needs to pick up the conversation exactly where the digital touchpoints left off.

Generative AI takes this already complex landscape and multiplies it exponentially. What once required dozens of coordinated assets now becomes thousands, even millions of micro-variations, each crafted for an individual in real time. Every one of these assets isn’t just a message; it’s part of an unfolding conversation.

A CRM that once logged call notes now must record the entire fabric of those conversations: which ads were seen, which offers were clicked, which chatbot answers landed, and which sales floor promotions were presented. Only then can a salesperson, a support agent, or even a store associate continue the journey without forcing the customer to start over.

Equally important is the need for feedback. Every piece of content is a test. Whether it drove a click, earned trust, or was ignored entirely. Without a closed loop that measures effectiveness and adapts the next round of messaging, the abundance of content becomes noise. With it, the customer journey becomes an adaptive system, learning and refining in real time.

And soon, it won’t just be human customers moving through these journeys. Agentic AI systems will increasingly act on behalf of people: research bots scanning offers, procurement agents negotiating bundles, digital assistants evaluating value propositions. Supporting these “machine customers” will demand experiences that are intelligible to both humans and AI: structured, contextual, and traceable across every channel.

Customer journeys are no longer a tidy sequence of touchpoints; they’ve become dynamic conversations that unfold across digital platforms, in-person interactions, and even machine intermediaries. Generating the content for those conversations is already trivial. The real challenge is stitching them together: breaking content into atomic units, syndicating it across channels, and ensuring it adapts to context without losing coherence. To meet that challenge, organizations need more than scale; they need connective tissue that can manage, track, and optimize every fragment of the journey in real time.

Atomic Content and the Future of Syndication

As customer journeys fragment into countless micro-variations, the way organizations think about content must change. For years, most digital teams have built experiences around pages: a web page for a campaign, a landing page for an offer, a knowledge base article for support. Those pages were authored, approved, and managed as self-contained units, often tightly bound to the channel where they would appear. That model is breaking down.

When every customer touchpoint might demand its own nuance, content can’t be locked inside a single page or platform. Instead, it must be broken down into atomic units: a headline, a product description, an image, a call-to-action, each tagged with meaning and context. Those units can then be composed and recomposed, pulled into an email, a chatbot script, a dynamic ad, or a sales enablement deck. This is what allows a brand to maintain consistency even as AI systems spin up thousands of variations.

The future of content won’t be about writing one perfect page; it will be about designing a system of building blocks that can flex across channels. That means creating a library of reusable elements governed by brand guidelines, regulatory requirements, and business logic, not just for human authors, but for the AI models that will increasingly assemble these experiences on the fly.

Syndication becomes the other half of this capability. Content can no longer live only inside a CMS or an email platform. It must flow freely between systems: from marketing automation to CRM to point-of-sale, so that every touchpoint speaks in the same voice and reflects the same understanding of the customer. Without syndication, atomic content is just fragments. With it, those fragments become the connective tissue of a unified journey.

In other words, the real shift isn’t just toward breaking content down, but toward equipping AI to put it back together in context.

Contextual Adaptation Through AI

Breaking content into atomic units only matters if those pieces can be reassembled in ways that make sense for the customer in front of you. This is where AI becomes less about raw generation and more about adaptation. A headline, an image, or a product description in isolation is just a fragment, but when AI understands who the customer is, what they’ve engaged with, and what matters to them right now, those fragments can be assembled into something coherent and compelling.

The difference is context. An AI model drawing from a library of approved building blocks isn’t creating content from scratch; it’s acting like a skilled conversationalist who knows the history of the relationship. It remembers that a prospect clicked on a pricing promotion yesterday, that they’ve compared two products in the past, and that they typically respond to concise language rather than long copy. Armed with those datapoints, it can adjust tone, swap imagery, or even combine offers into a custom package designed to resonate in that exact moment.

In practice, this means that what used to be a static campaign becomes fluid. The same product description might be formal in an enterprise sales deck, more playful in a consumer-facing ad, and stripped down to essentials in a chatbot response, all drawn from the same atomic source, but adapted by AI to fit the channel, the moment, and the individual. Over time, as the system observes which variations succeed, it refines the rules that guide those adaptations, creating a feedback loop that steadily improves performance.

This is the other side of the Zero-Cost Content Paradox: production is effortless, but effective adaptation requires intelligence, governance, and memory. Without those, scale produces noise. With them, it produces conversations that feel continuous and personal across every channel.

The Technology Foundations for the Next Era of Content

The shift toward AI-driven, hyper-personalized experiences doesn’t just increase the amount of content; it changes the very nature of what our systems must do. For years, digital marketing stacks were designed for a world where content was scarce, expensive, and relatively static. A CMS published web pages, an email platform managed campaigns, and a CRM logged interactions. Each operated largely in its own silo, optimized for human authors who created, scheduled, and tracked campaigns on a predictable cadence.

That world no longer exists. The cost of producing content is collapsing, but the effort required to manage it, to keep it coherent, contextual, and effective, is exploding. The future will demand systems that can keep pace with millions of variations generated in real time, while still preserving the throughline of a brand’s story. They will need to understand not only what was published but who saw it, how they responded, and how those signals should shape the next interaction.

They will also need to span the handoffs between digital and human channels so that a conversation begun in an email can be picked up in a store or on a sales call without losing context. And they will need to adapt in the moment, reshaping and repurposing content to fit the circumstances without straying from brand standards or compliance requirements.

The tools we rely on today: CMSs, CDPs, and integration frameworks will have to evolve for this reality. They must move beyond publishing and storage into active orchestration, beyond tracking broad audiences into remembering individual journeys, beyond connecting APIs into enabling agents to coordinate experiences in real time. These are the technology foundations for the next era of content, and without them, the abundance AI makes possible will collapse under its own weight.

CMS: From Pages to Semantic Content Engines

Headless CMS platforms and atomic content models have already become mainstream, but even these advances are only the beginning of what will be required. For years, the CMS has served primarily as a repository: a place where content is structured, stored, and published to the web or an app. That function was sufficient in a world where content was authored once, approved through a workflow, and then delivered in a relatively fixed form.

In the age of AI-driven journeys, that model is no longer enough. The CMS of the future will need to operate less like a publishing tool and more like a semantic engine. Content will not only need to be broken down into atomic pieces but also enriched with meaning, relationships, and context so that AI systems can retrieve and adapt it on demand. This requires indexes that understand similarity and intent, not just taxonomy or metadata, so that a product description, a testimonial, or a headline can be repurposed fluidly based on the situation at hand.

At the same time, the CMS must evolve into a guardian of brand integrity. It can no longer simply store text and images; it has to embody the rules that govern how those assets can be reshaped. Tone of voice, visual guidelines, promotional policies, and compliance constraints all need to live inside the system itself so that when AI models draw from the library, they are not inventing content in a vacuum but assembling it within the boundaries of brand and business logic.

The first signs of this shift are already visible: Sitecore’s Stream, Optimizely’s Opal, and Contentstack’s Brandkit all represent early steps toward embedding brand guidelines directly into the platform. These capabilities allow AI-driven content creation to stay consistent with brand standards, signaling where the entire category of CMSs is headed.

Equally important is traceability. With content variations multiplying by orders of magnitude, the CMS must keep a living record of what was generated, where it was used, and how it was adapted. In the future, knowing that a paragraph was written is less important than knowing that a particular variation of that paragraph was displayed in an ad campaign last week, reshaped for a chatbot interaction yesterday, and tested against an alternative headline in today’s nurture flow. Without this ability to catalog and track usage, organizations will quickly lose control of the very conversations they are trying to orchestrate.

In short, the CMS must evolve from a system of record into a system of meaning, governance, and memory. It will remain the foundation for managing content, but its role will expand to ensure that content can be understood semantically, reshaped responsibly, and tracked comprehensively across the infinite variations that AI will create.

Data: From Audience Segments to Journey-Level Memory

If the CMS is where content lives, the data layer is where its impact is understood. Over the past decade, Customer Data Platforms have become the backbone of personalization strategies. They excel at unifying profiles, stitching together data from multiple systems, and enabling marketers to act on segments. That has been powerful, but it is not enough for the world we are heading into.

As AI begins generating content variations at a massive scale, the key challenge shifts from broad segmentation to detailed memory. It is no longer sufficient to know that a user belongs to a segment such as “frequent buyers” or “enterprise decision-makers.” Brands need to know which landing page the person saw yesterday, which ad copy they clicked last week, which chatbot variation they engaged with, and whether those experiences pushed them forward or turned them away. Every micro-variation of content becomes part of a unique, unfolding conversation, and the data layer must be able to record it.

This level of granularity transforms the CDP from a marketing tool into the foundation of AI-driven orchestration. It must not only capture a multichannel view of the journey but also feed that history back into AI systems. When an agent is preparing the next touchpoint, it should have access to the complete thread of what has already been said, shown, and offered. That allows the AI to optimize not just at the campaign or segment level, but at the level of the individual: adapting tone, offer, or format based on the person’s lived history with the brand.

In practice, this means the CDP has to grow in two directions at once. On one hand, it must scale to handle vastly more data: every piece of content, every variation, every interaction, captured and contextualized. On the other hand, it must become more accessible to AI systems, exposing this history in ways that agents can query and learn from in real time. Marketers may still use dashboards and segments, but increasingly, it will be agents, not humans, that consume this detail to plan and prepare the next action.

Today’s CDPs are already inching in this direction. Many are building stronger event-streaming capabilities and real-time connectors. But the leap ahead will be profound: from serving as a warehouse of customer data to acting as a living memory of every journey, powering both human decisions and AI-driven conversations.

Integration: From Composable APIs to Agentic Orchestration

If the CMS provides the building blocks and the CDP holds the memory, the integration layer is what allows them to work together. Over the past several years, the move toward composable architecture has given organizations more flexibility. By exposing APIs and decoupling services, brands could mix and match best-of-breed platforms rather than being tied to a single monolith. That shift has been powerful, but it was designed with humans in mind: developers wiring systems together, marketers configuring workflows, analysts pulling reports.

The next era will demand something more. With agents acting as both creators and orchestrators of experiences, integration can’t stop at APIs. It must evolve into a layer of services optimized for how AI systems plan and execute tasks. Instead of simply passing data from one platform to another, these services will act like interpreters, giving agents access to the right content, the right data, and the right rules at the right moment. This is the promise of agent-oriented frameworks like the Model Context Protocol: not just connecting endpoints but providing the composable glue that allows autonomous systems to work fluidly across the stack.

To see how this works in practice, return to the journey of our prospect. When an agent begins planning their next interaction, it first queries the CMS for available content components. It doesn’t just get a headline or an image; it gets options tagged with semantic meaning, brand rules, and contextual metadata. It then looks to the CDP, retrieving the user’s history of interactions, from yesterday’s ad click to last week’s chatbot conversation.

With both the content and the context in hand, the agent applies brand guidelines and promotional logic to generate a new variation tailored for this moment. Once assembled, the integration layer syndicates that content into the channel, whether that’s an email platform, a digital ad network, or a CRM for a sales handoff, and records the interaction so it becomes part of the user’s evolving journey.

What once required a team of marketers, developers, and analysts working across systems now happens continuously, behind the scenes. Humans remain in the loop, but their focus shifts: from executing campaigns to defining the rules, from chasing data to guiding strategy, from producing content to shaping the frameworks within which AI produces and adapts it.

This is the true promise of composability in the age of AI. It is no longer just about swapping out tools; it is about building an ecosystem where agents can orchestrate journeys end-to-end, guided by the connective tissue of integrations that are designed not for people alone, but for the machines that will increasingly act on their behalf.

Preparing for the Flood

The cost of content creation may be collapsing, but the true test for organizations lies not in producing more, but in managing the abundance that follows. This is the essence of the Zero-Cost Content Paradox: the easier creation becomes, the harder orchestration gets.

The next era of digital experience will be defined by atomic content that can be assembled semantically, data platforms that hold the memory of every interaction, and integration layers that allow agents to orchestrate journeys seamlessly across channels. These are not optional enhancements; they are the foundations that will determine whether brands can deliver coherent, personalized experiences at a scale no human team could manage on their own.

The good news is that platforms are already beginning to evolve. Sitecore, Optimizely, and Contentstack have all introduced features that embed brand integrity into their CMSs, and each is experimenting with agentic capabilities and support for frameworks like the Model Context Protocol. These are meaningful steps, but the reality is that no platform is fully ready for what is to come. The volume of content, the need for contextual adaptation, and the demand for cross-channel orchestration will test every system in ways they were not originally designed to handle.

For organizations, the challenge is clear. The time to prepare is now, not by chasing the lowest cost of creation, but by investing in the connective tissue that makes content meaningful: the governance, the data, the integrations, and the ability to adapt at scale.

Those who wait risk being overwhelmed by their own abundance. Those who act will turn the collapse in creation cost into an advantage, building journeys that feel seamless, human, and intelligent in an era defined by machines.

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The Zero-Cost Content Paradox