TLDR: Building a composable content ecosystem

• Section 1 – Stacks are expanding, not consolidating. More tools, more integrations, and layers of AI make complexity inevitable.
• Section 2 – Modern stacks resemble ecosystems with core and supporting systems organized like solar systems. A few platforms anchor the center, while specialist and experimental tools orbit around them.
• Section 3 – Personalization requires data plus content. Data alone is inert; content alone is generic — fused together they create adaptive experiences.
• Section 4 – The content lifecycle is the blueprint. Core platforms — CMS, DAM, CMP, analytics, personalization, and AI workflows — anchor the system, while supporting tools integrate cleanly into the core.
• Section 5 – Agentic AI must act as a shared platform capability with context that flows across the core. With governance as the multiplier, composability becomes a system for growth.

Introduction

AI has turned a once-abstract concept into a boardroom imperative: composability. What began as an architectural ideal is now the operating model for modern marketing technology.

The shift is stark. Just as SaaS rewired enterprise software two decades ago, AI is now accelerating the disassembly of rigid martech suites into modular ecosystems. Data alone no longer wins. To deliver personalization at scale, companies must connect intelligence with dynamic, reusable content.

Yet most marketing organizations remain stuck between ambition and execution.

• Only 33% of martech capabilities are “utilized” by companies.
• With 7.7% of total company revenue in 2025, Marketing spend remains unchanged from the previous year, constituting.
• 75% of CMOs claim their “marketing organization is facing increasing pressure to cut marketing technology spend to deliver better ROI.
• 39% of CMOs (2nd highest priority) need external help to guide investments in martech stacks and help increase utilization and ROI.

This is the inflection point. Composability is not a buzzword. It is a discipline. Designed well, governed tightly, and fueled by AI, stacks shift from cost centers to engines of adaptability. The challenge is no longer whether composability is possible, but whether organizations can apply it with intent: deciding what belongs at the core, how integrations flow, and where governance enforces standards.

The moment calls for us to step back, recognize what has changed, and rebuild the stack as an ecosystem designed for continuous adaptation. Companies that embrace this reality will unlock not just efficiency but resilience, the ability to pivot at speed as markets, customers, and technologies evolve.

Section 1

The promise of composability

The fabric of technology is composability

Technology has always been composable. Every modern system is built by combining smaller components into something greater, whether through code libraries, APIs, frameworks, or plugins. Developers reuse what exists to create faster and aim higher. This principle is not new, but its application to marketing technology has reached a turning point.

In martech, the word composable is now everywhere: composable CDPs, composable DXPs, composable commerce. The term may sound technical, but the reality is simple. Composability means flexibility. It is the ability to build by combining, to scale by integrating, and to adapt by swapping. It is the principle that makes stacks scalable, resilient, and responsive to change.

• On a software level, composability shapes the user experience.
• On a stack level, composability defines the customer experience.

Stacks are not static. They evolve as customer needs evolve. Composability is not a theory. It is the operating fabric of modern marketing.

More tools: The technology landscape is (still) exploding

The martech landscape keeps expanding. There are now more than 15,000 tools. That represents one hundred times growth over the past 14 years, a compound annual growth rate of 39%. Predictions of consolidation have failed again and again. Instead, companies keep adding tools, and the market keeps expanding.

On average, stacks now contain 275 applications, with 62% of companies expecting that number to rise. The most advanced enterprises run even larger stacks. They have learned to manage complexity deliberately and turn it into an advantage.

Consolidation will not happen across the industry. It must happen inside each enterprise through rationalization and intentional design. Left unmanaged, stacks grow by accident. The sustainable path forward is a stack by design: curated, governed, and continuously simplified.

More specialist apps: The long tail in your stack

Martech has always followed a long-tail pattern. At the head sit a handful of global platforms. In the middle, hundreds of category leaders. At the tail, thousands of specialist tools that fill gaps or serve niche needs.

The long tail is not shrinking. Growth increasingly comes from small, well-integrated apps. They provide adaptability and speed, but they also complicate governance. Used intentionally, specialist apps extend the stack, fill critical gaps, and allow rapid experimentation. Used without governance, they create fragmentation and redundancy.

More in-house apps: The hypertail in your stack

Beyond commercial platforms lies the hypertail: custom-built applications developed inside organizations. These include portals, configurators, calculators, and workflow tools. Increasingly they are created by business technologists rather than IT, often with low-code or no-code platforms.

By 2025, Gartner predicted that 70% of new applications would be developed with low-code or no-code technologies, up from less than 25% in 2020. Generative AI is accelerating this trend, giving non-technical teams the ability to build apps quickly. This widens the scope of governance. Companies must now manage not only purchased software but also a growing portfolio of internal creations.

The new reality is that stacks must cater to both commercial and in-house apps. Success depends less on buying the right platforms and more on managing a mix of enterprise, specialist, and custom-built systems with discipline.

New technology: AI entering your stack

Artificial intelligence has become the defining force in martech. In a single year, 2,324 GenAI-native tools entered the market, representing 77% of total growth. The following year, 1,211 tools went bankrupt.

But here’s the kicker: they weren’t GenAI tools. 93.8% of the bankrupt tools were founded before the GenAI era, 5 years or older.GenAI isn’t just part of the growth; it is the growth engine.

AI is reshaping martech in two ways.

1. Inside-out by embedding intelligence into existing SaaS platforms and extending their capabilities.
2. Outside-in by enabling new architectures built on copilots, workflows, agents, and emerging protocols for agent integration.

This is more than incremental functionality. It is redefining how stacks operate.

Yet adoption lags. More than 70% of companies remain in pilot programs or limit AI to small-scale use cases.

AI is rewriting the playbook, but most organizations are still learning how to read it. Treating AI as ordinary software creates duplication and silos. Treating it as shared infrastructure creates coherence, governance, and compounding value.

The next phase is agentic AI. Unlike tactical applications, agentic AI orchestrates workflows across the lifecycle and adapts them in real time. The winners will be those that treat AI not as a feature hidden in isolated tools, but as a strategic layer of intelligence across the entire ecosystem.

Integrations: The glue in your stack

More tools, more apps, more AI. At first glance, the stack looks like fragmentation gone wild. The common fear is whether data and content can move freely across so many systems, or whether integration costs will spiral out of control.

The evidence shows otherwise. Integration is the glue that holds stacks together. Nearly 87% of companies automate workflows across multiple tools. 41% rely on integration features inside their core platforms, while 16% use low-code automation platforms, and 30% use both. More than 80% say APIs are a top requirement when evaluating new products. Cross-application integration is no longer the exception. It is the norm.

Fragmentation looks like a problem, but with integration as the backbone it becomes a source of resilience. The stack is manageable if integration is designed and governed intentionally.

Your stack is a composable ecosystem

Composable architecture is no longer theory. It is everyday practice. Instead of relying on monolithic platforms, organizations are assembling modular ecosystems anchored by core systems and extended by specialist tools. This makes deployment faster, maintenance easier, and dependence on IT lighter.

High-performing companies focus on composing capabilities with clear outcomes in mind. They enforce playbooks, rationalize tools, and strengthen infrastructure before introducing new layers such as AI. The result is not just flexibility but adaptability.

Composability is not about chasing every new tool. It is about engineering outcomes with structure, governance, and accountability built in.

Five composability pitfalls

Composability promises scale and resilience, but without discipline it can backfire. Five pitfalls stand out:

1. Binary policy. Choosing between one big suite or many small apps. Organizations need both and must decommission tools once capabilities are absorbed into larger systems.
2. Blunt consolidation. Treating rationalization as a one-off project. It must be continuous. 20% of tools deliver 80% of value. The rest require constant scrutiny and evaluation.
3. Big-bang rollouts. Attempting to re-architect the stack in one move. Incremental steps balance ambition and complexity.
4. Lack of alignment. Composability fails without the right people, skills, and mindset. Governance is as critical as APIs.
5. Weak governance. Less than half of organizations with IT teams have formal frameworks in place. Without governance, modular systems devolve into chaos.

Composable stacks succeed when leaders avoid these traps. The real determinant of success is not just technology, but organizational discipline and the ability to sustain composability over time.

Section 2

Four principles of a composable ecosystem

Every martech stack is already composable: a layered system of platforms and applications feeding into one another. The challenge is not whether composability exists, but whether organizations apply its principles effectively to create measurable outcomes. Four principles guide this shift.

Principle #1: Stacks behave like solar systems

Most company stacks now resemble solar systems. A few core platforms act as the sun, with specialized systems orbiting as planets, and smaller tactical tools circling as moons. The idea that one platform can do everything is outdated. Reality is layered, with each element playing a distinct role.

• The Sun: The core layer
  Center platforms such as CRM, CDP, CDW, CMS form the backbone and integrate with more than half of all stack tools.
• The Planets: The foundation layers
  Specialized systems extend the core’s capabilities, connecting both inward to the Sun and outward to the Moons.
• The Moons: The funnel layers
  Orbiting systems deliver tactical, outcome-driven functions.

Stacks were not always designed this way. Their structure has evolved as the software landscape has expanded.

2000–2015: Monolithic SaaS

Organizations adopted all-in-one suites. Simplicity came at the cost of flexibility, locking customers into vendor roadmaps and limiting specialist innovation.

2015–2025: Composable SaaS

APIs and modular platforms opened the market. Best-of-breed vendors thrived, and ecosystems began to resemble solar systems with multiple planets orbiting a small core. CDPs and cloud data warehouses became gravitational centers, but fragmentation increased.

2025–2030: Micro-composable SaaS

The next phase will fragment further. Features will break into microservices, AI agents, and MCP protocols. The number of moons will multiply, orbiting around shared cores. Integration complexity will rise, making governance and architectural clarity more important than ever.

As stacks evolve, they must be designed with intent. The shift from monolithic to composable to micro-composable has expanded choice but also multiplied complexity. Strength must remain anchored in the core, while orbiting tools stay flexible, so that ecosystems can scale without collapsing under their own weight.

Principle #2: Stacks have center platforms

For marketers, the center of the stack is not a single system of record. It is more like a conductor’s podium, orchestrating customer journeys and directing how tools connect.

Most companies see their center in one of four places: CRM (31%), MAP or CEP (26%), cloud data warehouse (15%), or CDP (13%). But there are big differences across business models.

• B2B companies center on CRMs (42%), while only 8% of B2C businesses do.
• B2C companies lean on MAP/CEP platforms (39%), often paired with cloud data warehouses (39%).

The anatomy of the stack is becoming clearer. If MAP or CEP, CRM, and ecommerce platforms are grouped into “customer-facing systems,” their combined share grew from 43% to 52%. This shows that companies are increasingly centering their stacks on engagement, not only on customer data storage. Data and content platforms form the foundation, but engagement is now the gravitational pull.

The center of gravity is shifting. Data platforms once held the core, but the emphasis has moved to engagement. Modern stacks prioritize orchestrating journeys and delivering personalization in real time. Stacks are no longer static systems of record. They are dynamic engines of customer engagement.

Principle #3: Seamless integration (when and where needed)

A true center platform integrates with most of the stack. Across organizations, 72% report that at least half of their martech stack integrates with their center platform. Nearly a third (32.1%) go further, saying more than 80% is integrated. That’s gravity in action.

Integration is the force that holds the solar system together. Native integrations and APIs make this possible. More than half of companies rank APIs as a top requirement when evaluating new martech products. Almost two thirds have chosen center platforms with good or great API coverage. The gap between potential and reality remains wide. AI integration is even more uneven. While 59% of companies report that most AI tools integrate with their platforms, 41% still face significant hurdles.

As stacks evolve, integration cannot be treated as a feature. It is the foundation. The value of data, AI, and content depends on how seamlessly they connect. The future of composability will be defined not by the number of tools an organization owns, but by the intelligence of the integrations that bind them together.

Principle #4: Duplication by design

Many assume that duplication in the stack signals inefficiency. In reality, duplication is often intentional. Companies need both agility and scale, which requires overlapping tools at different stages. A stack must act as an experiment-and-adapt lab and as a command-and-control center at the same time. Each role is essential. One secures future revenue through new customer journeys. The other secures current revenue through established ones.

Duplication is not inefficiency. It is a strategy. Organizations balance two forces:

• Experimentation, which thrives on the flexibility of small, fast, disposable apps.
• Scalability, which depends on the stability of the core platform, slower to change but built to last.

Companies duplicate tools and features on purpose. More than 82% report using alternative apps instead of built-in modules in their core platforms, a third say they do this frequently.

• Better functionality (67%): Specialist apps dedicate resources to a single purpose, offering superior capabilities that large suites cannot match.
• Less expensive to use (33%): Out-of-the-box specialist apps can be less expensive than configuring, integrating, and training on built-in features.
• Better user interface (31%): Specialist apps, often designed closely with users, provide more intuitive and refined experiences.
• Easier to govern/control usage (29%): Specialist apps can be switched on or off to enable agile experimentation without disrupting the core, as long as compliance policies are enforced.

This is why stacks must support the swapping of specialist apps. As orchestrators, core platforms are docking stations, not closed hubs. Flexibility is the competitive edge. Still, orchestration is not easy. While 82% of companies duplicate, fewer than half find the process straightforward, and only one in ten say it is very easy. In a composable world, openness matters. The center isn’t just a hub. It’s a docking station. Flexibility — the ability to swap and connect — is the new competitive edge.

This approach allows companies to experiment with flexibility while maintaining a stable core. Small tools act as speedboats for rapid learning. Core platforms act as tankers for scaling proven journeys. The process is not wasteful. It is a way to balance today’s revenue with tomorrow’s opportunities.

The challenge is orchestration. While most companies duplicate, fewer than half find it straightforward, and only one in ten say it is very easy. This is why openness matters. Core platforms must function as docking stations, not closed hubs. Flexibility is the competitive edge, but it only delivers value when combined with governance and intent.

Section 3

Three core composability components

Companies are increasingly building stacks around engagement. Data and content provide the foundation, but the real outcome is personalization, where prospects convert into customers. These three elements together define the composable stack: data, content, and personalization.

Data layer: Accessing customer data

Data is the foundation. It is what turns generic AI into differentiated intelligence that creates business advantage. This is why the cloud data warehouse has become essential. Unlike marketing-only tools, a warehouse spans the enterprise. It brings together sales, service, finance, and product data, giving marketers broader insights and richer signals.

Adoption is strong. More than 70% of companies integrate their CDW with the martech stack, and nearly half do so bi-directionally, feeding data both in and out.

• Among enterprises, adoption rises to 82%, with 53% bi-directional.
• Small businesses are also moving in this direction, with 56% integrated and 41% bi-directional.
• The split is sharper by business model. B2C leads with 88% integration and 66% bi-directional, while B2B lags at 59% and 35%.

AI is already working on top of this backbone. Nearly 60% of companies with data warehouses have AI tools tapping into them.

The frontier is shifting from connecting data to connecting signals. Data stitching now combines third-party intent with first-party engagement across sales, service, product, and marketing. The result is deeper engagement guided by buyer intent.

Yet data alone cannot act. It must be paired with content to become meaningful. With AI-driven optimization and personalization, the content layer becomes the multiplier. Agentic AI extends this further. It allows the content layer to listen and respond directly to signals, closing the gap between what customers want and how brands deliver it.

Content layer: Accessing company content

Content is how a brand shows up, yet it remains the weakest link in most stacks. While data is automated and API-driven, content creation is still fragmented and slow. No major content platforms connect directly into warehouses. Assets such as copy, product details, images, and video remain scattered across DAMs, CMSs, shared drives, and agencies.

The result is inefficiency.

AI has placed content at the center of the stack. With data, companies could listen. With AI, they can finally respond at scale.

The challenge is scale. Creating and rendering copy, images, and video demands human creativity, cross-team alignment, heavy computing power, and seamless distribution across channels. None of this scaled in the past. Creativity meant manual work or agencies. Rendering files hundreds of times larger than data required specialized tools. Moving them between systems was often a manual process. The result was a content layer that never matured.

Tools did exist: DAMs for assets, PIMs for product data, CMSs for delivery, and MDMs for definitions. But they were stitched together in fragile, point-to-point ways. They rarely connected to customer data until late in the process. Much content never reached campaigns at all, scattered across drives, wikis, agencies, and independent cloud tools. Compared to the structured, API-driven data layer, content was chaos.

Agentic AI changes the dynamic. It acts as an integration layer for content, unifying creativity, rendering, and distribution. Instead of recreating variations manually, companies can enrich a master file with metadata and let AI generate channel-specific or segment-specific versions instantly. AI can also synthesize scattered repositories, enabling new uses for existing assets.

Content must now be treated as a multiplier. The challenge is not production alone but orchestration: ensuring assets flow through journeys that feel contextual and outcome-driven.

The personalization layer: Creating customer experiences

Personalization is where data and content converge. Data tells us who the customer is. Content expresses who the brand is. Together they create the experience.

The personalization layer powers three critical capabilities:

1. Optimization aligns and refines journeys across all channels, not just the website. It borrows from CRO but extends further, with A/B and multivariate testing, hypothesis building, and journey mapping. The goal is continuous improvement, driving more engagement, more conversions, and greater customer satisfaction.
2. Decisioning happens in real time. Which message should a customer see? Which product should be promoted? Which journey should unfold next? Here, insights from the data layer flow into the content layer, turning signals into contextual interactions.
3. Orchestration ties everything together. Rules still matter, such as segment X seeing message Y, but machine learning and predictive analytics push it further. They enable the next best action, the next best offer, and the next best experience.

The purpose is not only relevance but measurable outcomes. It ensures that every piece of content is delivered with precision, every customer signal is acted on, and every journey is continuously tuned for business impact.

Data without content is silent. Content without data is generic. Personalization is the layer that fuses them into adaptive experiences. With agentic AI embedded, this vision is closer than ever to becoming operational reality.

Section 4

Building a content ecosystem

Data has long been treated as the backbone of composable stacks, but content remains the bottleneck. While the data layer is now automated, API-driven, and deeply integrated, the content layer is still fragmented, manual, and underdeveloped. The numbers are telling:

• 65% of content marketing assets go unused.
• 89% of enterprises expect demand for content to at least double, with nearly half bracing for a 300% increase.
• 77% of teamsalready struggle to meet today’s content demand.

This imbalance between data readiness and content readiness has turned the content layer into the critical choke point for personalization. If data tells us who the customer is, content determines how the brand shows up. Without a strong content layer, personalization remains an aspiration rather than a reality.

Organizations must now treat content with the same architectural rigor once reserved for data. A content ecosystem that is governed, integrated, and AI-enabled is no longer optional. It is the bridge between insight and experience. It is the multiplier that ensures every customer interaction is relevant, scalable, consistent, and impactful.

Most organizations still face structural issues. Content is created in silos, with little reuse, leaving assets underutilized and teams duplicating effort. Brand materials are scattered across disconnected systems such as DAMs, CMSs, and shared drives. The result is slower collaboration and weaker consistency.

The ripple effects are significant. Even small website or campaign changes often require IT, delaying launches and complicating multi-channel publishing. ROI remains difficult to link to workflows, as teams struggle to connect content activity with business outcomes. Without an integrated ecosystem, content stays slow, fragmented, and misaligned with customer expectations.

These friction points show why legacy approaches fall short. Instead of assembling tools in isolation and hoping for cohesion, organizations must design their stack around the continuous lifecycle of content experiences. This shift provides the blueprint for aligning workflows, selecting technologies, and creating the connective tissue that turns a collection of tools into a functioning ecosystem.

The five content lifecycle stages

The goal of a content ecosystem is to support the full content lifecycle. Traditional marketing has been campaign-centric: produce content, push it to market, and measure results at the end. Composability requires a different rhythm, one that operates as a continuous loop.

At its core, content moves through five interdependent stages: produce, deliver, personalize, analyze, learn. With each cycle, insights feed back into future activities, sharpening personalization and improving outcomes over time.

1. Produce: Content begins with planning and alignment. Teams define goals, assign workflows, and set a shared strategy. They then ideate, collaborate, and create assets. Feedback cycles refine quality, and approved outputs are stored in structured repositories for reuse.
2. Deliver: Once produced, content moves into assembly and distribution. Assets are localized for markets, formatted for specific channels, and published across websites, apps, social platforms, and paid media. The aim is speed, accuracy, and consistency at every touchpoint.
3. Personalize: Delivery enables personalization. Experiences are adapted to segments and individuals. Personalization engines and experimentation platforms tailor creative, messaging, and offers. SEO and accessibility checks ensure discoverability and inclusivity. Testing ensures performance can be refined at scale
4. Analyze: Every interaction generates data. Analytics and BI platforms measure effectiveness across engagement, conversion, and business outcomes. Event data connects back to campaigns and assets, showing what drives results and where gaps remain.
5. Learn: Insights flow back into planning and creation. Teams capture what worked, what failed, and what can be optimized. This closes the loop, so workflows grow more efficient and customer experiences more relevant over time.

This lifecycle is circular, not linear. Every asset delivered creates data that informs personalization and shapes future content decisions. The result is a closed loop that shifts marketing from episodic campaigns to adaptive experiences. For practitioners, every technology decision must reinforce this cycle, ensuring that insights from one stage flow seamlessly into the next.

The content lifecycle martech map

Technology only creates value when it reinforces this cycle. Tools alone do not guarantee outcomes. Success depends on how technologies are selected, connected, and governed. The added challenge today is AI. It is no longer only about choosing the right platforms. It is also about deciding which AI processes to adopt, where they belong, and how to prevent them from creating new silos. Each stage of the lifecycle brings its own mix of technologies and AI capabilities. These must be evaluated together to ensure they reinforce the cycle rather than fragment it.

Having defined the categories of technology that power the lifecycle, the challenge is not to acquire them in isolation but to assemble them into a functioning ecosystem. Tools on their own do not create value. The way they are prioritized, connected, and governed determines whether they accelerate outcomes or multiply complexity.

AI raises the stakes.

• 65% of enterprises now use generative AI regularly in marketing, yet only a fraction report consistent ROI.
• Where governance is strong, the returns are real: marketers report 54% time savings when embedding AI across the content lifecycle.

To move from a collection of composed technologies to a cohesive experience engine, organizations should follow a structured approach built on five core steps.

1. Map your solar system
2. Integrate the essential
3. Choose your design model
4. Put governance guardrails in place
5. Embed AI across the ecosystem

How to build a content ecosystem

1. Map your solar system

   Identifying the right categories of tools is not enough. You must also decide which systems become the gravity centers of your stack. This is where the Solar System methodology applies.

   The criteria for assigning tools to each layer include value criticality, data gravity, scalability, interoperability, governance, and switching costs. Systems with high data ownership and cross-team impact belong in the core. Fast-changing or niche capabilities belong outside it.

   • Core platforms: Foundational systems that anchor the content lifecycle. They hold critical data and workflows, must be stable and interoperable, and require tight governance. These are long-life, low-change tools
   • Specialist tools: Systems that extend and enrich the core. They add capabilities but should not become the source of truth. Their role is to support, not to trap, content or workflows.
   • Experimental add-ons: Fast-moving, low-risk additions. These pilots and innovations are tested under strict learn-and-iterate cycles. They can be scaled if successful or retired without disrupting the core.

   CIOs increasingly prioritize consolidation within the core. Nearly seven in ten technology leaders plan vendor reduction initiatives, often targeting a 20% cut in vendor count. The rationale is clear. Tighter cores simplify governance, reduce integration overhead, and create cleaner data environments for personalization and AI.

   The principle here is architectural intentionality. Concentrate content and workflows in a small, governed nucleus, and keep everything else loosely coupled. This minimizes lock-in, enables agility, and ensures that experimentation does not destabilize the foundation.

   

Integrate the essential

More than half of your stack should be integrated into your core platforms. Integration is the strategic glue. Identifying core and surrounding systems is only half the battle. The real value of an ecosystem depends on how well those systems communicate. Without integration, organizations risk duplicating effort, fragmenting data, and losing the efficiency that composability promises. The principle is simple: design integrations as deliberately as you design the tools themselves.

There are four primary types of integration, each with its own complexity and purpose:

1. UI integration: Integrating the experience

   UI integrations embed elements of one application inside the interface of another, so users do not need to switch between tools. For example, showing DAM previews directly inside a CMS, or embedding analytics dashboards in a CMP.

   • These integrations require close vendor cooperation and are difficult to maintain unless tools are part of the same suite.
   • Best for improving adoption and user experience when teams primarily operate in one system but need visibility into another.
2. Workflow integration: Integrating control

   Workflow integrations allow actions in one tool to trigger processes in another, such as publishing content from a CMP into a CMS or automating campaign setup between a marketing automation platform and a CRM.

   • These integrations demand that two tools are open and able to execute commands, often requiring APIs, middleware, or vendor-provided connectors.
   • Ideal for recurring, high-volume processes where automation improves governance and efficiency.
3. Data integration: Integrating the source of truth.

   Data integrations keep information consistent by automatically syncing records between systems, for example syncing customer profiles from a CRM to a personalization engine or passing analytics events into a BI dashboard.

   • Most modern platforms include standard data connectors and APIs, making basic data syncs straightforward.
   • Essential for enabling personalization, analytics, and consistent reporting across the stack.
   • These require no development effort and remain under team control.
   • Best for experimental tools, low-frequency tasks, or scenarios where automation does not justify the investment.
4. No integration: Manual processes

   Not every tool requires technical integration. Manual processes, such as exporting a report or uploading a file, may be more practical for edge cases.

   1. These require no development effort and remain under team control.
   2. Best for experimental tools, low-frequency tasks, or scenarios where automation
      does not justify the investment.

Integration decisions should be deliberate, not incidental. Consider:

1. Complexity versus value: Is the integration worth the build and long-term maintenance effort?
2. Business outcome: Are you aiming for efficiency through workflow, accuracy through data, or usability through UI?
3. Scalability: Will the integration hold as usage expands or as new channels are added?

A high-performing ecosystem balances ambition with pragmatism. Data integrations are often the backbone since they are relatively easy to implement and critical for personalization and analytics. Workflow integrations add automation and governance but require more careful design. UI integrations deliver the cleanest user experience but are typically only realistic within suites or tightly partnered vendors. Manual processes still have value at the edges, where experimentation matters more than efficiency.

Create an integration map

To bring this to life, map the integrations between your core and surrounding systems. Begin by listing each core platform and identifying the specific data, workflow, or publishing connections it requires. For example, a DAM should connect directly into a CMS to power content delivery. The CMS must then connect to personalization and analytics tools to optimize experiences. Analytics, in turn, should flow back into planning systems to guide future campaigns.

Surrounding tools such as SEO, accessibility, or marketing automation should dock into this core, extending reach without fragmenting workflows. The goal is to produce a clear diagram of connections that shows how information and actions move across the ecosystem. This integration map becomes both a blueprint for ongoing work and a governance artifact, ensuring that every connection is intentional, maintained, and aligned with business outcomes.

Choose your design model

Technology architecture is no longer a passive decision. The way you assemble your stack determines how quickly you can adapt, how effectively you can integrate data, and how reliably you can govern operations. Once lifecycle tooling and integration patterns are defined, companies face a strategic choice about how to assemble the stack. Broadly, three models exist:

• DXP route: Single-vendor suites that offer pre-integrated solutions but restrict flexibility.
• Fully composable: Maximum freedom to assemble best-of-breed components, requiring strong governance and integration maturity.
• Hybrid composable ecosystem: A middle ground where one platform anchors the stack and manages integration or UI, while leaving space for specialist tools.

The long-running debate between best-of-breed composability and unified DXPs is settling into a pragmatic resolution. MACH-style architectures delivered agility but often introduced hidden costs and operational complexity that few enterprises could sustain. Managing dozens of microservices and APIs demands skills that remain scarce. In practice, most organizations are moving toward hybrid models: unified where it matters, composable where it counts. This balance of control and flexibility also aligns with the consolidation goals now being driven by CIOs and CFOs.

Composable ecosystems are not DXPs

At first glance, a composable ecosystem can resemble a DXP: a single vendor delivering a broad set of capabilities under one roof. The difference lies in openness. A DXP locks customers into one vendor, one roadmap, and one pace of innovation. A composable suite provides a governed core but allows other technologies to integrate where needed. Many MACH vendors are moving closer to the DXP model by expanding their portfolios with personalization, analytics, and campaign tools. The risk is that these features are often poorly built or poorly connected, leaving customers with substandard outcomes.

Choosing a composable ecosystem requires careful evaluation of each capability on its own merits. Does the vendor’s CMS, personalization engine, and analytics deliver at a best-in-class level, or are some little more than bolt-ons? True leaders treat every component of their suite as equally strong, ideally validated by analyst rankings and customer feedback. Few vendors can credibly meet that bar today, which is why evaluation matters.

Decision criteria

When selecting an approach, organizations should evaluate against three criteria:

• Functionality: Which capabilities must live in the core? Do you require advanced hyper specialist tools, or will stability and connectivity take priority?
• Connectivity: How critical are integrations? Can your team manage custom API development, or do you need vendor-maintained connections? Remember that UI-level integrations are usually only feasible within suites or tightly partnered vendors.
• Cost: Do you have the budget and bandwidth to manage multiple vendor contracts, onboarding, and integration maintenance, or does consolidation make procurement more practical?

Investment considerations

Cost must be considered broadly, not narrowly. Beyond licensing fees, organizations should account for:

• Licensing and contracts: direct vendor costs, renewal cycles, and pricing models.
• Integration and maintenance: the effort needed to connect and sustain tools.
• Operational overhead: training, adoption, governance, and resources to manage the stack.
• Switching costs: the long-term expense of replacing or consolidating platforms, including the hidden costs of tools that fail to deliver.

The choice is not about ideology. Monolithic stacks are stable but rigid. DIY composability is flexible but fragile. Hybrid composable suites are emerging as the pragmatic standard, offering a governed core with enough openness to integrate orbiting tools. In an environment where complexity is accelerating, the winners will not be those with the most tools, but those with the clearest structure and the strongest governance.

1. Put governance guardrails in place

   The organizations that are winning are not those with the largest martech stacks. They are those with tighter, simpler systems made effective through strong governance. With governance in place, companies reduce cost, increase speed, and improve visibility and control.

   More tools do not equal more capability. As stacks expand, so does the burden of maintaining integrations, workflows, and data standards.

   More than half of SaaS licenses go unused. Marketing teams often manager over 100 tools. The result is predictable: higher spend, lower utilization, and slower outcomes. Instead of accelerating performance, bloated stacks hold organizations back.

   Composability magnifies the challenge. Modular architectures unlock flexibility, but they also introduce complexity if not governed with rigor. Taxonomies, permissions, workflow standards, and integration contracts must be defined centrally and enforced consistently. Without this, composability devolves into chaos: point solutions proliferate, silos deepen, and AI adoption falters due to poor data hygiene and fragmented oversight.

   The industry is responding with simplification. Under flat budgets, CIOs and CMOs are prioritizing vendor consolidation. Nearly 70% of enterprises plan to reduce vendor counts by 20% or more. The direction is clear, fewer systems, governed more tightly, performing more reliably.

   Strong governance outperforms bigger stacks. The most advanced organizations treat governance as the multiplier that turns composable systems into engines of scale. For practitioners, this means investing less energy in expanding toolsets and more in creating standards, enforcing workflows, and consolidating around what delivers measurable outcomes.

   Checklist for a governance framework

   A composable ecosystem only works if it is governed with discipline. Governance defines how tools are used, how data flows, and who is accountable. Organizations can adapt the following framework:

   1. Ownership and accountability
      • System Owner: Responsible for each platform (CMP, DAM, CMS, etc.)
      • Data Steward: Manages accuracy, taxonomy, and compliance
      • Integration Owner: Maintains connections and workflows
      • AI Governance Lead: Defines use cases, manages permissions, ensures compliance
   2. Standards and taxonomies
      • Naming conventions for campaigns, content, assets, and audiences
      • Metadata schema shared across DAM, CMS, analytics, and personalization tools
      • AI training data standards defining what data can and cannot be accessed
      • Versioning rules for updates, revisions, and AI-generated outputs
   3.  Workflow discipline
      • Approval processes before content or AI-generated outputs are published
      • Automation rules defining what is automated versus manual
      • Exception handling for overrides, errors, or urgent requests
   4. Integration and data flow
      • Data contracts specifying which fields sync between systems and how often
      • APIs and connectors with a standard approach (native, iPaaS, custom)
      • Audit trails tracking integration and AI activity, performance, and failures
   5. Measurement and compliance
      • Utilization metrics to track adoption and usage per platform
      • Data quality KPIs such as error rates, completeness, and duplicates
      • AI KPIs for accuracy, bias detection, time savings, and ROI attribution
      • Regulatory compliance covering GDPR, CCPA, accessibility, and emerging AI rules
   6. Review and simplification cadence
      • Quarterly stack reviews evaluating utilization, ROI, overlaps, and AI performance
      • Vendor rationalization criteria for retiring redundant tools
      • Sunset policy for phasing out tools or AI pilots without disruption

   Governance is not paperwork. It is operational clarity. By documenting ownership, standards, workflows, integrations, AI policies, and review cycles, organizations create a model that scales. Strong governance allows smaller stacks, with AI embedded across the core, to deliver bigger outcomes with greater confidence.

2. Embed AI across the ecosystem

   AI is not disrupting composability. It is fueling it. Agentic AI aligns with the trend of technology atomization, taking composability to the next level.
   The risk is that many organizations approach AI tactically, adding instructions or agents into isolated tools. This may deliver value for a single team but fragments the organization and weakens the customer experience. The result is not acceleration but confusion: more silos, more governance gaps, and inconsistent outputs.

   To avoid this, AI must be treated as a platform capability that spans teams, roles, and technologies. It cannot remain a feature buried inside individual tools. The true differentiator comes when intelligence is embedded across the stack, turning workflows from manual to adaptive and data from static to predictive.

   When AI draws from shared, governed data and is embedded into core systems, it becomes a multiplier. It aligns every stage of the content lifecycle, ensures consistent outputs, and accelerates adoption.

   The same four steps that define composability also apply to AI. To make AI an architectural layer rather than another disconnected feature, organizations must embed it deliberately into their operating model:

   1. Share AI across the core

      AI must be shared across the core, not scattered across individual tools. If each platform runs its own AI, the result is duplicate models, conflicting outputs, and no shared context. The alternative is to embed AI consistently across core systems or ensure it can exchange knowledge and context between them. This way, insights flow seamlessly across planning, content, personalization, and analytics, strengthening the ecosystem instead of creating new silos.

   2. Integrate AI into the fabric

      Just as integrations knit the ecosystem together, AI must flow through the same connective tissue. AI models cannot be limited to one tool’s context. They must integrate data, learnings, and workflows across systems. A copilot trained only in a CMP cannot understand what is happening in the CMS, DAM, or personalization engine. When integrated, AI brings cross-team context into every recommendation, avoiding disconnected or conflicting outputs.

   3. Evaluate AI as part of the stack

      AI is no longer optional. It should be part of the evaluation framework when designing your stack. Organizations must assess not only which tools to consolidate, but also how AI will embed across them. The key question is whether the ecosystem enables AI to function as a platform capability, shared across roles and technologies, or forces teams into fragmented, tool-specific use cases. AI must be treated as a first-class architectural criterion, not an afterthought.

   4. Govern AI like everything else

      AI cannot be left to run unchecked. Governance must define how AI is trained, where it can access data, and how outputs are validated. Policies around permissions, audit trails, and bias detection are essential. With governance in place, AI augments workflows without introducing compliance risks or eroding trust.

   AI is not a sidecar to composability. It is the multiplier. When embedded across the ecosystem, AI accelerates the flow of content, sharpens personalization, and closes the loop between data and decision-making. When fragmented, it recreates the very silos that composability is meant to solve. The future will not be shaped by who adopts AI the fastest, but by who embeds it with the most discipline.

   

Section 5

Putting it all together

Designing a composable stack does not have to feel overwhelming. A clear, structured evaluation process can guide both practitioners and executives toward intentional, risk-aware choices. The goal is not to build everything at once. It is to create a framework that balances innovation, governance, and long-term scalability.

Example content ecosystem

To build a functioning content ecosystem, you need a strong set of core platforms:

• Content Marketing Platform (CMP): Governs workflows, calendars, and approvals. Serves as a single source of truth for creative and media assets.
• Digital Asset Management (DAM): Serves as the single source of truth for creative and media assets.
• Content Management System (CMS/DXP): Delivers structured content across websites, apps, and other channels.
• Agentic AI Platform: Activates workflows and shares intelligence consistently across the ecosystem.
• Personalization Platform: Orchestrates tailored experiences, often integrated tightly with experimentation tools.
• Analytics and BI: Measures performance, connects to enterprise data stores, and closes the loop back into planning.

These are the non-negotiables. The core platforms every content ecosystem requires to operate with scale, consistency, and impact.

Specialist tools orbit around the core, extending capabilities in areas such as design, research, compliance, and channel activation. Large enterprise systems such as CRM, data warehouses, or event tracking may not be owned by marketing, but they must remain closely connected. Without alignment to these systems, the core ecosystem cannot deliver its full impact.

At the outer edge sit experimental add-ons. These cover emerging technologies for new formats, engagement channels, or audience insights. They are useful for test-and-learn pilots, but they should only be brought closer to the core once they demonstrate measurable value.

Embedded throughout this model is agentic AI. It is not a separate layer, but a capability woven into every stage, from planning and production to personalization and analytics. When applied consistently, it accelerates workflows, ensures coherence across the ecosystem, and multiplies the impact of every tool in the stack.

Building your content ecosystem

The logic for designing a content ecosystem is simple but rigorous:

1. Start from the lifecycle: Anchor tools to the stages of produce → deliver → personalize → analyze → Learn. If a system doesn’t clearly support a lifecycle stage, question its place.
2. Identify your gravity center: Core platforms are those without which the cycle would collapse. CMS, DAM, CMP, analytics, personalization, and agentic AI workflows belong here. These are long-life, low-change investments.
3. Integrate before you expand: Orbiting tools only create value if they connect cleanly to the core. A disconnected SEO tool or social scheduler adds complexity without contributing to outcomes.
4. Experiment at the edges: Innovation should be tested through add-ons that can scale up or be retired quickly, without disrupting the foundation.
5. Governance is the multiplier Clear taxonomies, standards, and ownership unlock the full potential of every tool. Without governance, even the best-designed ecosystem will fragment over time.

Agentic AI should run through every stage of this model. It is not an isolated layer, but the connective intelligence that accelerates workflows, closes gaps between data and content, and ensures the ecosystem adapts continuously. With AI embedded throughout, composability becomes more than a design principle. It becomes a system for growth.

Building your content ecosystem with Optimizely