Salesforce’s announcement that it intends to acquire Informatica is more than a portfolio expansion. It’s a strategic signal confirming that enterprise data infrastructure, and the ability to govern and activate it at scale, is central to how Salesforce delivers AI. It also reinforces what many in the ecosystem already sensed: Data Cloud isn’t just part of the Salesforce platform; It’s an integral component.

Informatica brings mature, enterprise-tested capabilities in ETL, data quality, cataloging, and MDM. That means more opportunity for Salesforce customers to bring rich, trusted data into the fold, particularly for organizations already using Informatica as a core integration or governance layer. We expect Salesforce to invest in tighter connections between the platforms, including native ingestion from Informatica-managed pipelines and possibly even activation back into Informatica-connected destinations.

That opens the door to broader, more flexible architectures, but also raises important questions that can’t be answered at the tooling level.

Salesforce now owns three data ingestion pathways: MuleSoft, Informatica, and native Data Cloud connectors. Each is powerful in its own right, but they serve different purposes, and the lines between them can blur quickly without a clear integration and data strategy. Which tool handles what? What’s the role of each in the long-term architecture? 

These are architecture-level decisions, and they don’t get solved by picking the fastest connector. They get solved by designing systems intentionally around business outcomes, governance requirements, and long-term scale.

At the same time, the ability to ingest more data with less friction brings a new risk: over-ingestion without context. We’ve seen this before — well-intentioned teams pull everything in, hoping to unlock value, only to create confusion, processing bottlenecks, or downstream AI behavior that doesn’t align with business needs.

With Data Cloud, this has real consequences:

• Excessive credit consumption tied to unused or low-signal data
• Performance impacts due to overly complex pipelines
• AI agents hallucinating or misfiring due to conflicting inputs without clear context

The acquisition of Informatica will make it easier to access more data from more systems, but architecture and data strategy become even more essential in that environment, not less. Data leaders will need to be deliberate about what’s ingested, how it’s modeled, and how that data supports identity resolution, segmentation, and AI decision-making.

Salesforce’s move is smart and aligned with its long-term vision. It strengthens the platform and expands what’s possible. But the so-what isn’t just about access to better tools — it’s about making smarter decisions with them.

For organizations aiming to scale AI with confidence, this is the time to reassess integration architecture, revisit data ownership, and reinforce the foundation that supports every AI initiative. The technology is getting stronger, and the architecture decisions we make now will determine how well it performs later.

Ready to optimize your data architecture for the Salesforce + Informatica era?

At Rosetree Solutions, we help organizations design intentional data strategies that maximize AI performance while avoiding common pitfalls like over-ingestion and architectural complexity. Whether you're looking to integrate multiple data pathways, strengthen governance frameworks, or prepare your Data Cloud foundation for enterprise AI, our team brings the expertise to guide your journey.

Don't let better tools become a bigger challenge. Contact Rosetree Solutions today to discuss how we can help you build a data architecture that delivers real business value, not just more data.