Is your MAP a junk drawer? The case for composable architecture

We’ve all seen it. You open a desk drawer, and instead of just pens and paperclips, you find loose batteries, tangled charging cables, mystery keys, and a half-finished set of instructions for a gadget you no longer own. It’s a junk drawer—a collection of things that don’t really belong together, making it impossible to find what you actually need.

Unfortunately, many marketing teams are finding that their marketing automation platform (MAP) has become just that: a junk drawer.

I come from the agency world and got my start on Marketo. I love MAPs, and for many teams, they still deliver immense value. But over the last two decades, GTM motions have become significantly more complex. We’ve gone from simple email blasts to managing product usage data, intent signals, AI interactions, and community engagement.

Rather than building purpose-built systems to handle this new complexity, we’ve asked our MAPs to take on more jobs. What started as an engagement tool is now being forced to act as a data cleanser, a routing engine, a scoring machine, and an identity resolution service. This isn’t just messy. It’s an operational bottleneck. Small changes now carry huge downstream risks because dependencies are buried deep within the platform.

Decoupling data from engagement

The core problem is that we are using the MAP as the “center of gravity” for work that doesn’t belong there. To fix this, we need to decouple data operations from the engagement layer.

The MAP should be reserved for what it does best: engagement and activation—email, SMS, landing pages, and nurture flows. Data operations, the “plumbing” of your organization, should move to an orchestration layer. When you keep data prep (cleansing, normalization, enrichment, routing) inside the MAP, the platform itself can slow down, leading to queue backlogs and delayed campaign fires. By separating these, you allow the engagement layer to execute across channels without becoming the bottleneck for every workflow in the business.

AI creates a paradox in the data landscape

The natural instinct for many teams is to rush to enable the AI features provided natively within their existing MAP. But adding AI to an already cluttered architecture won’t fix your problems; it will exacerbate them.

It’s essentially like tossing a tube of glitter with a loose cap into your junk drawer. Everything looks a little sparklier, but you are still digging through a mess of tangled dependencies and hidden bottlenecks. Platform-native AI is often constrained by the limited view of the system it sits on. If you want to maximize the power of AI, it needs context from across the entire business. An orchestrated, composable model frees AI from that single execution environment, allowing it to reason over CRM data, product usage signals, and support interactions simultaneously. Ultimately, your AI is only as smart as the data it can see, and an unbundled, orchestrated stack ensures it has a clear, panoramic view.

Who should modernize?

So is this for everyone? No. We can plot readiness against your need for technical agility vs. your technical capability/bandwidth.

• Keep it simple: If you are a traditional organization with low digital complexity, stick with the monolith. Don’t build a science project you cannot maintain.
• Consolidate & cleanse: If you are “overwhelmed”—meaning you have high complexity but a small, startup-sized ops team—moving to a full composable stack now is operational suicide. Focus on simplifying your current MAP first by extracting ops-heavy flows one at a time.
• The sweet spot: The ideal candidates for a composable model are digitally sophisticated enterprises with the internal maturity to manage it.

2 ways to modernize your MAP

When you are ready to modernize, there are two primary paths:

1. The Hybrid Model: This is where the vast majority of teams will land. You keep the MAP for your activation and engagement, but you move the heavy lifting of data orchestration into a separate layer with technology like Openprise. It’s a step toward modularity without the risk of a full rip-and-replace.
2. Full Composable: This is the end game. The MAP is no longer the center of gravity. It’s retired as the operating system and becomes just one of many purpose-built tools. This requires a very high degree of sophistication and organization commitment.

3 migration phases

You don’t need an 18-month project to overhaul your stack; you can phase it.

• Phase 1: The Data Sidecar: Move your messiest, most painful hygiene tasks (normalization, deduplication, enrichment) out of the MAP first. This gives you an immediate quality-of-life win for your Ops team with zero disruption to the end-user experience.
• Phase 2: Externalize the Brain: Move your scoring logic, audience criteria, and lifecycle decisions into the orchestration layer. Instead of rebuilding rules in every tool, you calculate them once in the brain and sync the governed output.
• Phase 3: Right-Size Activation: Finally, audit your premium MAP features. If you are paying for attribution that you’ve already moved to your orchestration layer, you’re paying for it twice. You can now downgrade the MAP tier or move to a simpler point solution.

Stability and governance is the glue

A composable stack only works when the operating model is stable. This requires:

1. Consent and governance: centralized suppression.
2. Measurement and schema truth: to prevent reporting drift where your email tool, CRM, and warehouse all show different numbers.
3. Operational ownership: avoiding the “Spiderman meme” where teams point fingers when a lead doesn’t route correctly.
4. Activation discipline: syncing governed outputs rather than raw logic.

Moving to a composable model isn’t just about technical architecture. It’s an operating model decision. If you skip governance, you will find that costs rise, complexity grows, and trust erodes. But if you get it right, you gain faster innovation, vendor independence, and an agile GTM strategy.

Evolution, not revolution, is the path forward. Start with your data, stabilize your logic, and let your technology stack actually work for you, not against you.