Most AI product demos look impressive in controlled environments. The challenge usually begins when teams try to connect those features to the systems their customers actually use: CRMs, ERPs, cloud warehouses, internal databases, and older on-premises applications that were never designed for modern AI workflows.
That gap between a promising prototype and a production-ready AI feature is where many SaaS product teams start running into infrastructure problems. It's also why conversations around enterprise connectivity are becoming more common at AI-focused product conferences like ProductCon.
If you're attending ProductCon New York 2026, stop by CData's booth — especially if your roadmap includes copilots, AI assistants, or agentic workflows that need live access to enterprise data.
What the ProductCon 2026 agenda signals about enterprise AI
The VIP pre-conference on May 19 includes a session titled AI Is Only as Good as What It Can See, which captures a growing reality for enterprise AI teams. As AI features become easier to prototype, the bottleneck increasingly shifts toward data access, governance, and operational reliability.
The May 20 main program goes further: CPO's Playbook of Tough Calls in an Agentic Marketplace, How Winning SaaS Companies Are Transitioning to AI-Native, and Turning Unstructured Content into Data That Power Products all point back to a similar operational question:
How do you build AI features that work consistently against real customer environments?
In practice, that usually means dealing with fragmented systems, inconsistent schemas, changing authentication requirements, and customer-specific infrastructure constraints that don't show up in a demo environment.
A product team might begin with a handful of integrations and a straightforward roadmap. Six months later, enterprise customers are requesting support for additional systems, legacy ERPs, region-specific compliance requirements, or internal APIs that were never originally scoped into the project. The integration work expands quickly.
CData Connect AI Embed is designed for that environment. It provides embedded connectivity across hundreds of enterprise data sources—including SaaS applications, databases, cloud platforms, and on-premises systems—without requiring product teams to build and maintain connectors internally for every source.
Why AI initiatives stall at the data layer
Enterprise data environments are rarely standardized. A CRM, billing platform, and support system may all represent the same customer differently, with different object models, permission structures, and API behaviors. Even basic concepts like account ownership or account status often vary across systems.
Those inconsistencies become much more visible once AI agents start querying multiple platforms at the same time.
For example, a sales copilot might pull pipeline data from Salesforce, contract details from a billing platform, and escalation history from a support system. But if those systems define accounts differently, refresh data on different schedules, or expose only partial context through their APIs, the AI layer inherits those inconsistencies immediately. Teams often discover the problem when copilots return conflicting answers that are technically correct within each individual system.
Many SaaS companies initially respond by building integrations internally. For an early release, that can be a reasonable tradeoff. But maintaining connectors tends to become a separate engineering responsibility over time. APIs evolve, authentication requirements change, customers customize workflows, and enterprise security reviews introduce additional governance and compliance requirements.
What begins as a lightweight integration effort can gradually turn into long-term infrastructure work.
For product teams trying to ship AI functionality quickly, that creates a persistent tension between roadmap velocity and the operational cost of maintaining integrations across increasingly fragmented customer environments.
Why MCP connectivity matters for agentic AI
Model Context Protocol (MCP) is quickly emerging as a standard way for AI systems to interact with external tools and enterprise data sources. Major platforms like OpenAI, Microsoft, and Google are already incorporating MCP-style patterns into their agent and tool ecosystems.
For product teams, MCP reduces the need to build and maintain custom integration logic between models and systems. But it doesn’t solve the key challenge of securely governing access to enterprise systems across many customers and data sources.
Connect AI Embed addresses this through a centralized connectivity layer between AI agents and enterprise systems. Instead of building connectors for each deployment, teams can expose governed MCP access across hundreds of supported sources from a single platform.
This becomes critical once AI products move beyond proof-of-concept and into customer production environments, where access control and data boundaries matter.
How to govern AI access to enterprise data
A lot of AI governance efforts still focus on the model layer—prompt controls, output filtering, safety testing, and red-team exercises.
While those controls are important, they don’t address upstream access questions, especially in enterprise environments where permissions, data residency, and auditability are enforced at the system level.
In practice, the more critical control point is often the connection layer: what data an AI system can retrieve from systems like CRMs, data warehouses, or ERPs before a query ever reaches the model.
Connect AI Embed takes an identity-first approach. Rather than introducing a separate permissions model, it uses identity passthrough so existing source-system RBAC policies continue to govern access. Existing SSO and OAuth workflows remain intact, and least-privilege controls limit retrieval scope for individual agents and workflows.
This means security teams can audit AI-initiated access using the same controls they already rely on, while product teams avoid building a parallel governance layer.
In regulated industries, that distinction is increasingly a requirement as AI reviews are starting to focus as much on access governance and auditability as on model behavior.
Reducing hallucinations starts with data quality and consistency
Hallucinations are often discussed as a model problem, but in production, AI usually just inherits the mess of its underlying data environment. If a support bot is pulling from a stale data warehouse export, it’s going to give outdated answers. If a finance assistant is trying to stitch together messy, conflicting customer IDs across three different systems, it will contradict itself—even if the LLM is working perfectly.
You can't prompt-engineer your way out of bad data. Reliable enterprise AI needs real-time operational data, consistent schemas, and strict retrieval policies that respect user permissions.
Connect AI Embed fixes this at the connection layer by normalizing schemas across your systems and querying live sources under strict access controls. This provides the model with a clean, accurate context window from the start, ensuring reliable outputs across a complex enterprise stack.
Who should meet CData at ProductCon New York 2026
Product leaders evaluating AI roadmap execution
CPOs, Heads of Product, and VPs of Product are increasingly expected to deliver AI functionality without dramatically expanding infrastructure teams. For organizations trying to balance roadmap pressure with enterprise deployment realities, embedded connectivity can remove a significant amount of integration overhead.
Product managers managing integration backlog growth
Many AI roadmap delays are ultimately caused by dependency work at the integration layer. When enterprise customers request support for additional systems, teams often end up prioritizing connector maintenance over feature development.
A centralized connectivity layer changes that equation by reducing the amount of per-source engineering work required to support new environments.
AI engineers building copilots and agent workflows
While prototype AI systems are easy to assemble, production systems that operate securely across real customer infrastructure are a different problem.
Teams building copilots, assistants, and agentic workflows typically need governed access controls, support for heterogeneous enterprise systems, operational reliability across customer deployments, and visibility into query activity and audit logs.
These are the types of requirements Connect AI Embed is designed to handle in production.
Founders modernizing existing SaaS platforms
For established SaaS companies, adding AI features often surfaces infrastructure assumptions that were manageable before AI workloads entered the picture. Data fragmentation, inconsistent APIs, and customer-specific environments become more visible once agents start querying systems dynamically.
Addressing connectivity earlier tends to make future AI roadmap expansion significantly easier.
Frequently asked questions
Who should meet CData at ProductCon New York 2026?
Teams building AI features that depend on enterprise data access will likely find the conversation relevant—especially product leaders, platform teams, and AI engineers working through integration scale, governance requirements, or MCP adoption challenges.
What is Connect AI Embed?
Connect AI Embed is CData's embedded connectivity platform for software vendors and SaaS teams. It allows applications, copilots, and AI agents to access enterprise systems through a centralized, governed connectivity layer rather than through individually maintained custom connectors.
The platform supports cloud and on-premises systems, identity passthrough, OAuth workflows, access governance controls, and MCP-based AI connectivity patterns.
How do SaaS teams avoid spending years building integrations internally?
Many teams eventually decide that connectivity infrastructure is not where they want to invest core engineering resources long term.
Instead of building and maintaining connectors internally for every customer environment, platforms like Connect AI Embed provide prebuilt access across hundreds of enterprise systems while centralizing governance and authentication management.
How can AI agents access enterprise systems securely?
Secure enterprise AI access usually depends on enforcing permissions before retrieval occurs, rather than relying entirely on downstream filtering.
Connect AI Embed supports identity passthrough, existing RBAC enforcement, OAuth-based authentication flows, least-privilege access controls, and centralized audit logging for AI-initiated queries.
Why does MCP matter for AI product teams?
MCP provides a standardized way for AI systems to interact with external tools and data sources. As more AI products adopt agent-based architectures, standardized connectivity becomes increasingly important for interoperability and governance.
Connect AI Embed extends that model with enterprise-focused connectivity infrastructure, governance controls, and broad source coverage.
Can Connect AI Embed access on-premises systems?
Yes. Connect AI Embed supports secure connectivity to on-premises systems through Connect Gateway, allowing organizations to expose approved enterprise systems to AI workflows without directly exposing those systems to the public internet.
Meet CData at ProductCon 2026
CData Connect AI Embed is exhibiting at Metropolitan Pavilion in New York City. If you're evaluating how to connect enterprise data sources to your AI agent stack, stop by on Wednesday for a live demo or to book a dedicated 1:1.
