71% of enterprise AI projects stall before a single feature ships. Not because the model failed. Because the data wasn’t ready. If that sounds familiar, find CData at booth 313, Tobacco Dock at The AI Summit London 2026.
Why enterprise AI stalls before the model runs
Most AI deployments don’t fail because the model is wrong. They fail because the data the model needs is locked in systems that weren’t built to talk to AI.
Enterprise data lives across CRMs, ERPs, cloud warehouses, on-premises databases, and custom internal applications. Each source returns data in a different shape. Each team enforces access differently. When an AI agent queries two systems expecting consistent context, it often gets inconsistent results — not because the model is poor, but because the data layer wasn’t resolved before the query ran.
The data layer — not the model — is the limiting factor on how fast enterprise AI can move.
What CData Connect AI does
CData Connect AI is a managed Model Context Protocol (MCP) platform that sits between your enterprise data sources and your AI tools, agents, and models. It doesn’t move or replicate your data. It normalizes access — so AI can query live enterprise data through a single, governed interface, regardless of where that data lives or what shape it’s in. MCP has become the de facto standard for AI-to-enterprise connectivity, with adoption from Anthropic, OpenAI, Google, and Microsoft — and CData Connect AI is purpose-built to bring that standard to production environments, with the governance, authentication, and source breadth that most MCP offerings in the market don’t provide.
Here is what that means in practice:
Query hundreds of data sources without writing connector code. Connect AI ships with connectors for hundreds of sources — cloud and on-premises, modern and legacy — and adds more than 20 new connectors per year. No custom integration work to build or maintain.
Give AI agents a consistent interface. Connect AI exposes enterprise data through MCP, so every AI tool and agent gets a consistent interface regardless of the underlying source.
Deploy AI with enterprise security, without compromise. Connect AI enforces governance at the connectivity layer — honoring your existing source system permissions through identity passthrough, OAuth 2.1, PKCE and SSO integration, least-privilege and down-scoping controls, and full audit logging across every AI data query — without configuring it separately per source.
Connect without replication. AI operates on live data. There’s no staging layer, no pipeline to maintain, and no lag between source and model.
What’s on the agenda — and where CData fits
The AI Summit London 2026 returns for its 10th edition as the headline event of London Tech Week, with 5,000+ attendees, 300+ speakers, and 14 tracks across 10 stages. This year’s programme is squarely focused on the shift from AI experimentation to enterprise-scale execution — with dedicated tracks on agentic AI, a new Data Excellence Stage, and sessions on AI governance, enterprise readiness, and scaling AI responsibly in production.
Those conversations map directly to where CData operates. The agentic AI track is asking how agents access and act on enterprise systems — that’s a data layer problem. The Data Excellence Stage is addressing how organizations make data AI-ready — that’s a normalization and governance problem. And the governance sessions are asking how enterprises control AI at scale — that’s an access policy problem at the connectivity layer. CData’s answer to all three starts in the same place: resolve the data layer before the model runs, and the rest of the stack performs as designed.
How to govern AI access to enterprise data
Most AI governance efforts focus on the model: prompt guardrails, output filters, red-teaming. Those matter, but they don’t address the root risk — AI querying data it shouldn’t have access to in the first place. Governance that lives only at the output layer can be bypassed. Governance that lives at the connectivity layer cannot.
CData Connect AI takes an identity-first approach to AI governance. Rather than building a parallel access control system, it honors your existing source system permissions through identity passthrough — so the same RBAC rules already governing your enterprise data automatically apply to every AI query. OAuth 2.1, PKCE, and SSO integration ensure MCP usage is governed by existing user permissions, while least-privilege principles and down-scoping further restrict AI access to only what each agent needs. Every interaction is captured in a comprehensive audit trail, giving security and compliance teams full visibility across every data query from AI — the foundation for complete compliance, governance, and security monitoring. If you’re a Head of IT or AI/ML Lead blocked on AI deployment because of data access risk, this is the structural fix — not an output filter applied after the fact.
How to reduce hallucinations in enterprise AI
Hallucinations in enterprise AI are almost always a data problem, not a model problem. When an agent queries stale exports, retrieves from mismatched sources, or receives inconsistently defined fields across systems, it produces confident answers built on bad inputs. Prompt engineering and output filters can mask the symptom — they don’t fix the cause.
Reducing hallucinations at the source requires three things: live data access (not cached snapshots), schema consistency across sources (so “customer” means the same thing in CRM and billing), and query-time access enforcement (so the agent only retrieves data it’s authorized to see). CData Connect AI addresses all three, normalizing schema and field definitions across sources before data reaches the model. Queries run against live systems — not replicated exports — so agents always reason over current state. The result is fewer hallucinations — not because the model improved, but because the data it reasons over is clean, consistent, and current.
What to expect when you meet the CData team
The CData team will be at booth 313 for both days. Bring your architecture, your blocker, or your use case — the demo is live, not scripted, and the team can usually show you where Connect AI fits in under 20 minutes.
To schedule a dedicated 1:1 before the event, use the link below.
Frequently asked questions
Who should meet CData at The AI Summit London 2026?
CData is worth a visit if you're a CTO, CDO, Head of IT, or AI/ML Lead dealing with any of the following: AI agents that need governed access to production enterprise systems, RAG pipelines blocked by inconsistent or inaccessible source data, governance gaps preventing AI deployment at scale, or engineering backlogs from building and maintaining custom connectors. CData Connect AI is exhibiting at booth 313 at Tobacco Dock on June 10–11, 2026, as part of London Tech Week.
How do I govern AI access to enterprise data?
Governing AI access means enforcing your existing security boundaries at the connectivity layer — before data reaches the model. CData Connect AI takes an identity-first approach: it honors source system RBAC through identity passthrough, integrates OAuth 2.1, PKCE, and SSO so MCP usage is governed by existing user permissions, applies least-privilege and down-scoping to further restrict AI access, and captures every data query in a comprehensive audit trail for compliance and security monitoring.
What is MCP, and why does it matter for enterprise AI?
The Model Context Protocol (MCP) is an open standard that defines how AI agents connect to external systems and data sources. Instead of custom integration code for every model-to-system pair, MCP provides one protocol that any authorized AI tool can use to access any connected system. It has become the de facto standard for agentic AI integration, with adoption from Anthropic, OpenAI, Google, and Microsoft. CData Connect AI is a managed MCP platform purpose-built for enterprise deployment — adding governance, source breadth, and production-grade infrastructure that most MCP offerings in the market don't provide.
How do I reduce hallucinations in enterprise AI?
Hallucinations are almost always caused by poor data inputs — stale exports, inconsistent field definitions across sources, or agents retrieving data outside their authorized scope. Fixing hallucinations means fixing the data layer: query live systems instead of cached snapshots, normalize schema across sources so the same concept means the same thing everywhere, and enforce access controls so agents only retrieve what they're authorized to see. CData Connect AI resolves all three — normalizing enterprise data across hundreds of sources through a single governed interface so AI models receive accurate, consistent, and in-scope inputs on every query.
How do I improve enterprise AI agent accuracy?
Enterprise AI agent accuracy degrades when agents query inconsistent, stale, or out-of-scope data. The fix is architectural, not model-level: connect agents to live systems of record instead of cached exports, normalize field definitions across sources so the same concept means the same thing everywhere, and enforce access controls so agents only retrieve data they're authorized to see. CData Connect AI normalizes enterprise data across hundreds of sources — cloud and on-premises — through a single governed interface, so agents receive consistent, current, and authorized inputs on every request.
Can CData Connect AI connect to on-premises systems?
Yes. CData Connect AI connects to on-premises data sources through Connect Gateway — a capability that establishes secure access to on-premises systems, including legacy ERPs, on-premises databases, and custom internal APIs, without exposing them directly to the internet. Connectivity is governed through the same centralized policy layer regardless of where the source lives.
Book a meeting with CData at The AI Summit London 2026
CData Connect AI is exhibiting at booth 313 at Tobacco Dock, London, on June 10–11, 2026 — part of London Tech Week. If you’re evaluating how to connect enterprise data sources to your AI agent stack, stop by for a live demo or book a dedicated 1:1 in advance.
Your enterprise data, finally AI-ready.
Connect AI gives your AI assistants and agents live, governed access to hundreds of enterprise systems — so they can reason over your actual business data, not just what they were trained on.
