Data platform vendor Informatica is enhancing its AI capabilities as the demand for generative AI continues to elevate enterprise requirements.

Informatica is well-versed in the realm of AI; in fact, the company launched its first Claire AI tool for data back in 2018. In today's era of generative AI, the company has augmented its technology with advanced natural language capabilities in Claire GPT, part of Informatica’s Intelligent Data Management Cloud (IDMC), introduced in 2023. The main goal is to make data access and usage simpler, faster, and more intelligent. This value proposition has made the company an appealing acquisition target, with Salesforce announcing in May its intention to acquire the company for $8 billion.

As the acquisition moves through approvals and regulatory processes, enterprises still face pressing data challenges. Today, Informatica announced its Summer 2025 release, highlighting how the company’s AI journey over the past seven years has evolved to meet enterprise data needs.

The update features natural language interfaces that can create complex data pipelines from simple English commands, AI-driven governance that automatically tracks data lineage to machine learning models, and auto-mapping capabilities that reduce week-long schema mapping projects to mere minutes.

AI Scaling Hits Its Limits

Power caps, rising token costs, and inference delays are reshaping enterprise AI. Join our exclusive salon to discover how top teams are:

• Turning energy into a strategic advantage

• Architecting efficient inference for real throughput gains

• Unlocking competitive ROI with sustainable AI systems

Secure your spot to stay ahead: https://bit.ly/4mwGngO

The release tackles a longstanding enterprise data issue that generative AI has made more urgent.

“The one thing that hasn’t changed is the ongoing fragmentation of data within enterprises, which continues at a rapid pace without any sign of convergence,” said Pratik Parekh, SVP and GM of Cloud Integration at Informatica to VentureBeat. “Thus, bringing all this data together remains crucial.”

From machine learning to gen AI for enterprise data

To comprehend what Informatica is currently doing, it's essential to understand its journey to this point.

Informatica’s initial Claire implementation in 2018 targeted practical machine learning (ML) issues that troubled enterprise data teams. The platform leveraged accumulated metadata from thousands of customer implementations to offer design-time recommendations, runtime optimizations, and operational insights.

The foundation was based on what Parekh describes as a “metadata system of intelligence” containing 40 petabytes of enterprise data patterns. This wasn’t abstract research, but applied machine learning aimed at specific bottlenecks in data integration workflows.

Over the years, this metadata system of intelligence has improved, and in the summer 2025 release, the platform includes auto-mapping capabilities that address a persistent data problem. This feature automatically maps fields between different enterprise systems using machine learning algorithms trained on millions of existing data integration patterns.

“Anyone who has dealt with data management knows that mapping is a time-consuming task,” Parekh said.

Auto mapping focuses on taking data from a source system, such as SAP, and using it alongside other enterprise data to create a Master Data Management (MDM) record. For enterprise data professionals, MDM is considered the ‘golden record’ as it aims to be the source of truth about a particular entity. The auto mapping feature can comprehend the schemas of different systems and create the appropriate data field in the MDM.

The results highlight the value of Informatica’s long-term investment in AI. Tasks that once required deep technical expertise and significant time now occur automatically with high accuracy.

“Our professional services have previously done mapping work that typically takes seven days to complete,” Parekh said. “This is now accomplished in less than five minutes,” Parekh added.

Copilot gets an upgrade to use metadata better

A key element of any modern AI system is a natural language interface, often accompanied by a copilot to assist users in task execution. In this aspect, Informatica aligns with other enterprise software vendors. However, its distinction lies in the metadata and machine learning technology.

The summer 2025 release enhances Claire Copilot for Data Integration, which became generally available in May 2025 after nine months of early access and preview. The copilot enables users to input requests, such as “bring all Salesforce data into Snowflake,” and have the system orchestrate the necessary pipeline components.

The summer 2025 release introduces new interactive capabilities to the copilot, including enhanced question-and-answer features that guide users in understanding how to use the product, with answers sourced directly from documentation and help articles.

The technical implementation involved developing specialized language models fine-tuned for data management tasks using what Parekh calls – Informatica grammar.

“The natural language translated into Informatica grammar is where our secret sauce lies,” Parekh explained. “Our whole platform is a metadata-driven platform. Thus, we have our own grammar that describes the mapping, data quality rules, and MDM assets.”

Market timing: Enterprise AI demands explode

The timing of Informatica’s AI evolution coincides with fundamental changes in how enterprises consume data.

Brett Roscoe, SVP & GM, Cloud Data Governance and Cloud Ops at Informatica, noted that a significant shift in the enterprise data landscape over recent years has been the scale, with more people needing greater access to data. Previously, data requests primarily came from centralized analytics teams with technical expertise; in the gen AI era, those requests originate from all sectors.

“Suddenly, with the advent of gen AI, your marketing and finance teams are all seeking data to drive their generative AI projects,” Roscoe explained.

The summer release’s AI Governance Inventory and Workflows capabilities address this challenge directly. The platform now automatically catalogs AI models, tracks their data sources, and maintains lineage from source systems to AI applications. This helps enterprises maintain visibility and control as AI projects expand beyond traditional analytics teams.

The release also introduces data quality rules as an API, enabling real-time data validation within AI applications rather than batch processing post-data movement. This architectural change allows AI applications to verify data quality at the point of consumption, addressing governance challenges that arise when non-technical teams initiate AI projects.

Technical evolution: From automation to orchestration

The summer 2025 release illustrates how Informatica’s AI capabilities have evolved from basic automation to advanced orchestration. The enhanced Claire copilot system can decompose complex natural language requests into multiple coordinated steps while maintaining human oversight throughout the process.

The system also offers summarization capabilities for existing data workflows, addressing knowledge transfer challenges that burden enterprise data teams. Users can ask the copilot to elucidate complex integration flows developed by previous developers, reducing reliance on institutional knowledge.

The release’s support for Model Context Protocol (MCP) and new generative AI connectors for Nvidia NIM, Databricks Mosaic AI, and Snowflake Cortex AI showcase how the company’s AI infrastructure adapts to emerging technologies while adhering to enterprise governance standards.

Strategic implications: Maturity wins in enterprise AI for data

Informatica’s seven-year AI journey, culminating in the enhancements for the summer 2025 release, underscores a fundamental truth about enterprise AI adoption: sustained domain expertise is crucial.

The company’s approach validates the strategy of developing specialized AI capabilities for specific enterprise challenges rather than pursuing general-purpose AI solutions. The summer release’s AI-powered lineage discovery and governance workflows represent capabilities that arise only from years of understanding how enterprises truly manage data at scale.

“If you didn’t have a data management practice before gen AI emerged, you’re struggling,” Roscoe noted. “And if you had a data management practice when gen AI appeared, you’re still scrambling.”

As enterprises transition from AI experimentation to production deployment, Informatica’s approach reaffirms a fundamental truth: in enterprise AI, maturity and specialization are more valuable than novelty. Enterprises should focus not only on new AI-powered features but also on AI capabilities that comprehend and resolve the intricate realities of enterprise data management.