CData’s New AI Developer Tools Build on an Established SAP BDC Partnership

CData has been an SAP Business Data Cloud (BDC) partner since 2025, embedding its connectors so the platform can reach non-SAP data and accelerate insights across AI, analytics, and operations. What changed in June 2026 is the company’s investment in the layer that makes that partnership matter for AI. On June 23, 2026, CData shipped a set of AI developer tools, and earlier in the year, it expanded the leadership team that runs its data layer. For SAP customers trying to activate data that lives outside the SAP perimeter, both moves sharpen an existing relationship.

SAP BDC aims to unify governance, analytics, and AI workloads on a single platform, and CData’s contribution is connectivity: governed, real-time access to the hundreds of non-SAP sources that enterprise architectures inevitably include. The June 2026 product releases and leadership changes are about CData hardening that connectivity layer to handle AI agent workloads on top of the same governed foundation it already supplies to SAP BDC.

What CData Shipped and Who Is Now Running It

The June 23 launch centers on the Model Context Protocol, an emerging open standard for giving AI models access to real-time enterprise data. CData introduced a free Connect AI Developer Edition designed to lower the barrier for AI teams that need governed data access without standing up a full enterprise integration stack. It paired that with an open-source Connect AI Python SDK for developers building agentic workflows, and a new CData CLI that lets developers query enterprise data sources directly from the command line. The stated goal is to simplify AI development by replacing the ungoverned, brittle pipelines that slow most AI projects.

Earlier in June, CData expanded its executive bench to support that strategy. The company appointed Raviv Levi as Chief Product and Technology Officer, Amit Naik as VP of AI Architecture, and Craig Sanchez as SVP of Embedded Sales, framing the hires as part of an expansion of its leadership to power the AI data layer. Naik’s title alone signals that AI architecture is now a product-level priority rather than a feature. CData’s full vendor profile is available on SAPinsider.

Why SAP Professionals Should Care

The data fragmentation problem that CData addresses is well-documented within the SAP customer base. According to SAPinsider’s SAP Business Data Cloud Use Cases and Adoption 2026 research, only 3% of organizations have achieved a unified, governed data layer, while 38% remain in a siloed or ad hoc integration state. AI and agent use cases are among the top drivers pushing organizations toward SAP BDC, cited by 26% of respondents, yet the infrastructure to support them is rarely in place.

The integration picture compounds the problem. SAPinsider’s Enterprise Integration for SAP 2025 research found that only about 20% of organizations have fully integrated systems with real-time data flow, and the average SAP organization integrates 36 applications using four or more tools. Any AI workload touching enterprise data will almost certainly cross non-SAP sources. Governed connectivity at that scale, delivered through a protocol purpose-built for AI agents, is a materially different offer than another point-to-point integration. That is the gap CData’s embedded role in SAP BDC is meant to close.

What This Means for SAPinsiders

SAP BDC investment depends on solving the non-SAP connectivity problem before go-live. SAP BDC’s value scales with the amount of data estate it can see. If 38% of organizations are still in siloed integration, an SAP BDC deployment that ignores the non-SAP perimeter will reproduce the same fragmentation on a new platform. Because CData connectors are already embedded in SAP BDC, governed connectivity to external sources can be part of the architecture from the start. CIOs building a multi-year data strategy around SAP BDC should assess that connectivity layer before finalizing platform scope.

MCP is a protocol worth adding to the organization’s integration standards now. The Model Context Protocol (MCP) is emerging as the coordination layer for AI agent data access. CData’s decision to build Connect AI on MCP rather than a proprietary API is worth tracking. With only 20% of SAP organizations achieving real-time integration and AI workloads demanding exactly that, enterprise architects need a governed path for real-time, multi-source data without having to rebuild every pipeline. Evaluating MCP-based connectivity as part of the integration standards provides AI teams with a sanctioned alternative to ad hoc API calls.

Free, governed tooling changes the calculus of prototyping. A consistent blocker for AI teams in SAP environments is the time required to get clean, governed data into a proof of concept. CData’s free Connect AI Developer Edition, built on an open-source Python SDK, removes the procurement barrier for early-stage AI development. For AI leaders trying to move faster than IT provisioning cycles allow, that combination of zero cost and MCP-based governance creates a credible path to prototyping. The practical question is whether your enterprise data governance policies can extend to cover MCP-sourced data before prototypes reach production.