Seamless Context Real-Time Data for AI Agents

The News

CData Software announced Connect AI, a secure connectivity solution that links AI assistants, workflow builders, and agent platforms to over 300 enterprise data sources using the Model Context Protocol (MCP). This solution provides governed, live, and context-rich access to business data without replication, directly addressing the core data access and governance issues that cause 95% of enterprise AI pilots to fail despite a significant $30-$40 billion market investment. To read more, visit the original press release here.

The AI Connectivity Gap for Application Developers

The current application development market is defined by a massive push toward AI integration, yet this ambition is constantly colliding with data complexity. According to theCUBE Research,  enterprises embedding AI into analytics workflows are twice as likely to derive measurable value from their data initiatives, highlighting the critical link between data access and AI success. The global custom software development market, already estimated at $43.16 billion in 2024, is projected to grow at a CAGR of 22.6% through 2030, driven by the demand for customized applications. 

Developers are under immense pressure to deliver new AI-powered features quickly, yet they are hampered by fragmented data infrastructure. With nearly 76% of professionals using or planning to use AI tools, citing increased productivity (81%) as a top benefit, the actual process of getting AI to work with live, secure enterprise data remains a significant, time-consuming hurdle. This bottleneck is primarily rooted in the challenge of data governance and integrating AI models with the existing system of record.

Impact on the Application Development Market

The introduction of Connect AI, utilizing MCP, shifts the data integration burden for AI-enabled applications. By providing a single, governed platform that connects AI to over 300 data sources in-place (meaning no data replication), it could abstract away a massive layer of complexity for developers. 

This move from moving and replicating data to accessing data live and preserving its semantic context is a game-changer. It could combat the key adoption barriers identified by MIT research: data access and governance. For the broader market, this type of vendor-agnostic connectivity is crucial as it allows companies to choose the best-of-breed AI model or agent framework without being locked into a single data ecosystem or spending excessive time building custom, fragile data pipelines for every AI use case.

The AI Challenge

So far we’re seen that when developers need to connect AI or analytic applications to complex enterprise data, they face a cumbersome, multi-step process:

  • Data Replication & ETL: Developers spend significant time building custom ETL jobs to copy data from operational systems (like ERPs or CRMs) into data warehouses or data lakes. This creates stale data, introduces latency, and doubles the storage and maintenance costs.
  • Security & Permissions Workarounds: Because data is moved, the existing security and authentication protocols of the source system are broken. Developers then have to build complex, often redundant, security and permission layers on top of the replicated data store to ensure compliance, creating governance gaps and security risks.
  • Loss of Context: Moving data strips away critical semantic metadata and relationships, forcing developers to manually recreate this context for AI models to understand the true meaning of the data, severely limiting the intelligence of the output. This time-sink keeps developers from focusing on core business logic.

Future Development Paradigm with Governed Connectivity

Connect AI could  address some of these challenges and change the workflow and focus. Instead of being data plumbing engineers, developers may now shift to higher-value tasks, empowering both themselves and their business stakeholders.

  • Focus on Logic, Not Plumbing: The goal is to have developers leverage the MCP to build intelligent agents and AI-powered features using live, governed data from 300+ sources via a single connection. This may reduce the need for custom data integration and data engineering, freeing up time.
  • Secure by Default: By inheriting the source system’s existing security and authentication protocols, the platform is looking to ensure that the AI’s access is automatically aligned with the authenticated user’s permissions. This means developers could build AI features that are secure and compliant by design without complex manual governance checks.
  • Agentic System Enablement: When building AI agents using frameworks, Connect AI aims to give those agents real-time, semantic-rich context, potentially allowing the agents to perform complex queries and take action across diverse systems.

Looking Ahead  

The application development market is moving away from building monolithic applications toward intelligent orchestration where autonomous agents and AI-powered systems handle complex workflows. The growth of low-code/no-code platforms, expected to grow at a CAGR of 37.7% to reach $388.6 billion by 2030, will only accelerate this trend by democratizing application creation and increasing the demand for professional developers to focus on the underlying system integration and governance. We have highlighted that 50% of enterprises will likely adopt unified DevOps platforms by the end of 2025 to simplify workflows. This pursuit of simplification must extend to the data layer to realize the full promise of AI.

CData’s Connect AI influences this market shift by solidifying MCP as a critical standard for AI-native connectivity. By offering a managed MCP platform with hundreds of connectors, CData is positioning itself to be one of the layers that enterprises will rely on to move AI projects from pilot to production. This move signals that their next phase of innovation is likely to focus on deepening the semantic intelligence offered via MCP, allowing AI to not just understand data relationships but also to apply sophisticated business logic and orchestration across those 300+ connected systems. 

Author

  • Paul Nashawaty

    Paul Nashawaty, Practice Leader and Lead Principal Analyst, specializes in application modernization across build, release and operations. With a wealth of expertise in digital transformation initiatives spanning front-end and back-end systems, he also possesses comprehensive knowledge of the underlying infrastructure ecosystem crucial for supporting modernization endeavors. With over 25 years of experience, Paul has a proven track record in implementing effective go-to-market strategies, including the identification of new market channels, the growth and cultivation of partner ecosystems, and the successful execution of strategic plans resulting in positive business outcomes for his clients.

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