Why data platforms matter for AI agents and MCP success

As organizations across industries race to adopt generative AI, AI agents and Model Context Protocol, or MCP, one requirement is becoming clear: Their success depends on a strong, scalable and agile data platform. Without a solid data foundation, even the most sophisticated AI models struggle to deliver meaningful outcomes.

The reality is that AI models are only as effective as the data they rely on. Whether it's large language models (LLMs) powering chatbots, AI agents coordinating enterprise workflows, or MCP systems managing dynamic model execution across tasks and data types, all depend on rich, diverse and well-managed data. Yet most organizations today operate within a complex data landscape.

Enterprise Strategy Group Research shows that over 60% of organizations manage between 100 and 499 data sources, and many are planning to develop more than 20 generative AI applications within the next two years.

This explosion of data sources -- ranging from structured databases to unstructured data like documents, images and videos -- presents a critical challenge: How can organizations harness this fragmented landscape into a coherent, AI-ready foundation?

That's where modern data platforms come in. Companies like Qlik, Glean, Domo, Cloudera, Couchbase, Cockroach Labs, Snowflake, Databricks, MongoDB, Google BigQuery and Oracle are leading the way by offering versatile, scalable platforms:

• Ingest and unify diverse data sources. Data platforms enable organizations to integrate structured and unstructured data from hundreds of sources -- whether CRM systems, data lakes, IoT streams, or content repositories -- into a single, trusted view. This unified foundation is essential for AI models to contextualize and generate accurate, relevant outputs.
• Enable real-time, scalable access. Generative AI, AI agents, and MCP systems thrive on fast, low-latency data access. Platforms like Couchbase, MongoDB and Cockroach Labs provide distributed, high-performance databases that can handle global-scale AI workloads, while Snowflake, Databricks, Cloudera, Google BigQuery and Oracle offer cloud data platforms and analytics engines for processing massive datasets at scale.
• Support context-rich AI applications. Tools like Glean and Qlik empower AI applications by embedding search, analytics and discovery capabilities into workflows, ensuring that AI agents and generative models can surface the most relevant, contextual information across the enterprise.
• Facilitate governance and compliance. As AI adoption expands, organizations must maintain data quality, lineage and security. Platforms like Oracle, Cloudera and Snowflake play a critical role in maintaining governance, enabling enterprises to build AI responsibly while meeting regulatory standards.
• Accelerate AI innovation. By providing a flexible, scalable data backbone, these platforms enable teams to experiment, iterate and deploy AI applications faster, turning ideas into operational tools.

In short, data platforms are no longer optional. They are the foundation upon which all successful AI strategies are built. Organizations that invest in modern, scalable and AI-ready data platforms will be the ones best positioned to unlock the full potential of generative AI, AI agents and Model Context Protocol, while those who don't risk falling behind.

Stephen Catanzano is a senior analyst at Enterprise Strategy Group, now part of Omdia, where he covers data management and analytics.

Enterprise Strategy Group is part of Omdia. Its analysts have business relationships with technology vendors.