4 reasons why enterprise teams are replacing Adobe's CDP for composable solutions

Many enterprise teams face a frustrating reality: their data infrastructure doesn’t enable the speed, autonomy, or confidence marketers need, and their tech costs keep rising. We and our cloud partners hear about this often, especially from teams using Adobe Experience Platform (AEP) and its customer data platform (CDP) capabilities. 

Their experience proves that even with a world-class data infrastructure, where you store your data matters. 

The 2026 AI and Marketing Performance Index found that using a marketing suite to store the single source of truth (SSOT) creates more manual work and makes it harder to measure impact compared to using a data cloud like BigQuery or Snowflake as the SSOT. 

These hurdles exist because packaged suites create data silos and require heavy data team support to service marketing requests and enforce governance. And with activation-based pricing, every action fuels ever-increasing fees, which is why many teams are making a change. 

The optimal foundation for enterprise scale is to position the data cloud as the SSOT and connect it to downstream systems. This preserves real-time data access, enforces global data security, provides self-service capabilities for marketers, and minimizes overall cost. 

Let’s examine why enterprise organizations are rethinking their data infrastructure, including the specific marketing challenges and technology costs they can eliminate by stepping away from a packaged ecosystem. 

Data fragmentation restricts marketing agility

It’s no secret that marketers need rich, unified customer data to orchestrate highly personalized campaigns across channels. They also need visibility into how campaign experiments perform and which interventions influence customers’ actions. 

Despite the wealth of data enterprise organizations can leverage, data fragmentation remains a major issue, causing a cascade of competitive drawbacks. Per the 2026 AI and Marketing Performance Index:

• Siloed data is now a top-five barrier to accelerating marketing growth (faced by 27% of respondents), reflecting the greatest increase since last year’s research.

• Data latency, fragmented data and tools, and identity resolution issues hurt personalization efforts for over one-third of teams.

• Just 20% of teams deliver consistent insights that guide better decisions from their experimentation efforts.

Where marketing suites fall short

Monolithic stacks like Adobe correlate closely with these challenges; they don’t enable the speed or flexibility required for an effective SSOT. Packaged ecosystems create data silos, require regular data syncs, and incur considerable maintenance costs, all of which present significant operational challenges. 

Google’s Tarun Rathnam, global director of AI and cloud for marketers, underscores why composable solutions beat siloed platforms: “Packaged suites position the data cloud as a data source, but never the source of truth. This creates data fragmentation, restricts agility, and locks marketers into channel tools that may not be their ideal choice. A composable model avoids these challenges to enable ongoing data-driven marketing success, so data teams can focus on business intelligence, not stack upkeep and governance.”

Traditional CDPs also hold teams back from activating their largest untapped data volume: non-text data types, including images, audio, and video. Data clouds like BigQuery can interpret multimodal data, so organizations act on their full customer intelligence. 

Taige Eoff, Google agentic data cloud lead, reinforces why multimodal data is the missing piece for marketing success: “Roughly 15% of enterprise customer data actually reaches the campaign engine; the rest sits in storage, support tools, and product analytics. Google BigQuery connects this multimodal data so every insight can drive better product decisions, marketing tactics, and overall growth.” 

Why Adobe Experience Platform isn’t working for enterprise teams

Teams commonly share the following frustrations about AEP and its packaged CDP, which is driving them to rebuild around the data cloud and a composable CDP:

Activation-based pricing and data duplication raise total cost of ownership

Adobe uses traditional activation-based pricing, which is ideal for low-volume needs. However, enterprise teams commonly sync tens of millions of customers daily. Adobe’s CDP charges per user profile, its optimization and orchestration systems charge for activation, and each tool may duplicate capabilities that teams already pay for elsewhere.

A data cloud is the optimal, scalable foundation for data storage and management. Because it has complete, up-to-date profiles — instead of stale or incomplete copies scattered across channel tools — it is the ideal location to build audiences and orchestrate campaigns through composable martech. 

Enterprise teams often use a composable CDP as their main data and campaign activation platform. Composable CDPs are built to work directly in the cloud, enabling marketers to segment customers, create audiences, and reconcile profiles directly in the SSOT. This improves overall data quality while replacing the need for Adobe’s CDP.

The modern, composable infrastructure presents a clear cost advantage for enterprises. GrowthLoop’s composable CDP, specifically, charges based on the number of customer records and total number of products, unlike Adobe’s activation-based pricing. Teams can activate records to any channel as often as they need without surprise fees. GrowthLoop also requires no data duplication, which presents inherent cost savings and security benefits. 

Data ingestion to AEP consumes data team resources

AEP requires ETL and data must map to an Experience Data Model schema. The strict schema requires engineers to manually map thousands of data fields or create custom APIs for ongoing data ingestion, and the mapping can break if data fields change. These challenges resurface whenever the marketing team selects a new tool for channel activation. 

Data clouds, however, typically have an easier, more flexible process. Google BigQuery, for example, automatically detects schema and allows ELT, so teams can load unformatted data and clean it later. 

Adobe’s AI is restrictive and limited to correlation

Adobe’s AI is distributed across its products and metered using AI credit pools that frequently require mid-cycle top-ups. Given its setup, Adobe’s AI delivers the same challenges and drawbacks of generic, channel-specific AI.

AI applied to channel tools or incomplete data generates low-value insights, leading to minor improvements in that specific part of the journey. AI is best applied to your data cloud, not siloed tools. 

These challenges are amplified by the AI’s learning model. Marketing suites employ AI built on correlation, which uses broad assumptions to suggest interventions. The teams achieving the greatest success with AI are moving toward causation, which requires an AI solution designed to capture causal data. 

Model maturity is also a major concern with packaged suites; vendors commonly update their AI quarterly or monthly, whereas AI-first data clouds and martech platforms advance more quickly. Google Cloud, for example, releases weekly AI updates. 

GrowthLoop’s composable CDP and AI Decisioning work directly in the data cloud, so every real-time activity can be analyzed. Our solution allows model flexibility, provides transparency into its decisioning so teams can fine-tune parameters as they see fit, and creates an agentic context graph for rapid learning and optimization.  

Data governance, access control, and compliance are cumbersome

Large organizations often navigate global data privacy regulations and strict access control requirements. Adobe enables role-based access control, however, configuration and ongoing management require heavy upkeep, especially when managing multiple regions and languages.  

Adobe’s data silos further complicate enterprise governance considerations, because security and engineering teams must oversee every system. Applying controls at the highest level possible (the data cloud), instead of within each channel tool, makes governance easier and more effective.

Composable, cloud-native martech deliver sustainable marketing growth for enterprises

Every foundational change can feel scary for organizations, especially considering the investment made in the current system. However, packaged solutions like Adobe are a major financial strain if used as an all-in-one suite. Those costs will only continue to grow in both direct tech fees and opportunity loss without a future-ready architecture. 

Most teams can reposition their existing tools for greater efficiency by making their data cloud the SSOT. This can augment their existing Adobe setup for immediate benefit, with the ability to replace it entirely after initial implementation. 

Enterprise teams using GrowthLoop’s composable CDP with their data cloud commonly launch their first campaign within 45 days and begin measuring ROI immediately — providing an intuitive hub for self-service marketing features while streamlining overall engineering support needs.

Dive deeper into how Adobe CDP and GrowthLoop compare for enterprise teams, and request a personalized GrowthLoop demo today to see how composable architecture can fuel your ongoing success.