The Brief: Salesforce and Informatica announced an expanded collaboration designed to address one of the most persistent challenges in enterprise AI: the lack of shared business context.
The companies are aligning Informatica’s enterprise metadata, Master Data Management, cataloging, and lineage capabilities with Salesforce Data 360 and MuleSoft. These components form a unified data engine that provides AI agents with consistent, trusted, and real-time enterprise context.
The approach aims to help AI agents move beyond isolated data points and operate with a clear understanding of business definitions, relationships, and operational signals.
Analyst Perspective: The collaboration signals a shift away from treating AI enablement as a model-centric problem and toward treating it as a data context challenge. Instead of asking models to do more, Salesforce and Informatica focus on helping AI understand the business it operates in.
Shared definitions, clear lineage, and live operational signals give agents something they often lack—reliable context. That matters because automation rarely fails due to weak models; it fails when data is fragmented or inconsistent.
Bringing metadata, master data, and real-time signals together creates a more consistent way for AI to interpret everyday business events. The agents are not positioned as independent decision-makers but as systems that work within established rules and workflows.
For organizations concerned about governance, compliance, and predictability, that distinction is important. Prioritizing context over data replication also speaks to cost control and architectural discipline, both of which remain top concerns in large enterprise environments.
A central element of the announcement is the role of enterprise understanding.
Informatica contributes deep metadata intelligence that defines core business entities and their relationships. When combined with Salesforce Data 360, this creates consistent “golden records” not only for customers but also for products, suppliers, and other critical objects.
Data catalogs add another layer, giving visibility into data spread across cloud platforms, on-premise systems, and long-standing legacy environments. Data lineage then fills in the backstory, showing where information originates, how it changes, and how current it is.
These capabilities give AI agents more than raw data; they provide clarity. With clearer definitions and verified sources, agents can interpret events with confidence instead of guessing across disconnected systems.
MuleSoft plays a complementary role by connecting operational systems and exposing real-time signals to AI agents. These signals provide situational awareness, allowing agents to understand what is happening across applications and processes.
Beyond visibility, MuleSoft enables agents to trigger actions in a controlled and secure manner. This ensures responses align with existing workflows rather than bypassing them. The integration layer becomes a bridge between insight and execution, helping agents move from interpretation to action without introducing inconsistencies.
By embedding these capabilities into the broader platform, Salesforce positions integration not as a backend function but as a core enabler of agent-driven operations.
Data 360 brings order to what would otherwise be disconnected information. Combining Informatica’s metadata with MuleSoft’s operational signals enables it to create a single, reliable context for AI agents.
Through a Zero Copy approach, agents access enterprise data without replicating it, helping manage storage costs and data sprawl. This design allows multiple agents to operate from the same up-to-date view of customers, policies, and transactions.
Serving as the foundation of Agentforce 360, Data 360 helps agents reason consistently across channels, resulting in coordinated actions grounded in a shared context instead of isolated data.
Salesforce and Informatica are building on strengths their customers already recognize. Salesforce supplies the operational logic embedded across its enterprise applications, while Informatica anchors the data layer with governance, quality, and MDM capabilities.
When combined, these strengths directly address a persistent gap in AI deployments: systems that act without understanding how the business actually works. For enterprises with distributed data and regulatory requirements, this model favors reliability and traceability over raw speed.
The trade-off is complexity. Integrating metadata and lineage across environments takes effort, and teams must align around shared standards. Still, with phased implementation and defined governance, the approach offers a realistic path to AI that fits enterprise realities.
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