The Dawn of Agentic AI & Salesforce : A Paradigm Shift in Enterprise Data

The enterprise technology landscape is undergoing a profound transformation. As businesses race to leverage artificial intelligence (AI), the demand for integrated, interoperable data platforms has surged. At HCLTech, we’ve observed a clear trend: companies are moving beyond siloed customer relationship management (CRM), enterprise resource planning (ERP), or data stacks. Instead, they seek unified platforms that enable cross-enterprise AI workflows—often termed “Agentic AI.” Salesforce’s strategic acquisition of Informatica signals its ambition to build a comprehensive data platform that meets these needs and positions itself as a leader in the enterprise AI race, particularly against competitors like SAP. I want to explore this vision, its implications, and how it aligns with the evolution of the modern AI data stack.

To understand Salesforce’s strategy, let’s first examine the rise of advanced data platforms, which have become critical for enterprise AI. These platforms have gained significant attention for their ability to integrate seamlessly with existing data systems, delivering actionable insights through AI. The core of their success lies in their ontology—a structured framework that organizes and connects disparate data sources into a unified, machine-readable model. This ontology enables businesses to deploy AI workflows that span multiple systems, departments, and use cases.

This pattern is now being recognized by multiple leading companies: the imperative for businesses to construct and deploy AI workflows that span diverse business units, multiple enterprise applications, and even disparate data platforms. The reality for any Fortune 500 company is that uniformity in systems is a myth; you never have just "one of anything."The enterprise technology landscape is on the cusp of a profound transformation, driven by the emergence of "Agentic AI." This isn't merely about integrating AI into existing systems; it's about building an entirely new foundation for AI-driven workflows that transcend traditional silos. Companies like Palantir have grown rapidly by focusing not just on big data ; it's fueled by the compelling vision of an AI platform that seamlessly connects and operates across disparate data systems. Creating such an ontology, however, is a complex task. It requires extensive data cleanup, classification, and integration—what we in the consulting industry once referred to as middleware. This middleware layer is essential for harmonizing data from various sources, such as CRM, ERP, legacy systems, and cloud platforms, making it accessible to AI models. The success of leading data platforms lies in their ability to address this challenge, enabling organizations to scale AI across their operations.

This pattern is now being recognized by multiple leading companies: the imperative for businesses to construct and deploy AI workflows that span diverse business units, multiple enterprise applications, and even disparate data platforms. The reality for any Fortune 500 company is that uniformity in systems is a myth; you never have just "one of anything." We’ve seen how Fortune 500 companies grapple with data fragmentation. Large enterprises rarely rely on a single CRM, ERP, or data platform. Instead, they manage a complex ecosystem of systems—Salesforce for CRM, SAP or Oracle for ERP, Snowflake or Databricks for data warehousing, and more. The challenge is to unify these systems into a cohesive AI-driven ecosystem. Advanced data platforms have set the standard for this, and Salesforce, with its acquisition of Informatica, is positioning itself to follow a similar path while building a competitive moat against rivals.

Salesforce, long recognized as the world’s leading CRM provider, is expanding its ambitions far beyond customer management. Its acquisition of Informatica, a leader in data integration and management, marks a strategic shift toward building a comprehensive data platform that supports enterprise-wide AI applications. By combining Salesforce’s Data Cloud with Informatica’s expertise in data integration, governance, and quality, Salesforce is creating a modern AI data stack capable of powering cross-enterprise AI workflows and establishing a competitive edge over SAP’s Business Data Cloud.

Informatica’s strength lies in its ability to handle the critical tasks of data management—cleaning, classifying, and Jimmying and integrating data from diverse sources. This capability is essential for creating an ontology akin to those powering advanced data platforms: a unified data model that enables AI to operate across multiple business units and applications. For instance, a Fortune 500 retailer might want to use AI to optimize its supply chain, personalize customer experiences, and forecast demand within a single workflow. This requires integrating data from ERP systems (e.g., SAP), CRM platforms (e.g., Salesforce), and external sources (e.g., market trends or weather data). Informatica’s tools, now integrated with Salesforce, enable this by providing robust data integration and governance capabilities.

Salesforce’s Moat Against SAP’s Business Data Cloud

SAP, a key competitor, has also entered the data platform arena with its Business Data Cloud (BDC), launched in 2025, which aims to harmonize data from SAP and non-SAP systems to support AI-driven processes. The BDC promises a unified semantic layer, preconfigured interfaces, and AI-powered Insights Apps, targeting efficiency in data extraction and aggregation for enterprise AI applications. However, Salesforce’s acquisition of Informatica positions it to create a competitive moat by offering a more flexible, interoperable platform that integrates with a broader range of systems, including Snowflake, Databricks, AWS, and even Microsoft 365. Unlike SAP’s BDC, which is closely tied to its ecosystem and may face challenges integrating with non-SAP architectures, Salesforce’s Data Cloud, enhanced by Informatica’s capabilities, aims to be a neutral orchestration tier across heterogeneous environments. This interoperability, combined with Salesforce’s Agentforce AI agents, positions it as a versatile solution for enterprises seeking to avoid vendor lock-in and leverage their existing investments in diverse data platforms, giving Salesforce a strategic advantage in the race to dominate the modern AI data stack.

Data Proliferation and Complexity: Large organizations generate vast amounts of data across multiple systems. A Fortune 500 company might use dozens of applications, each with its own data format and schema. Harmonizing this data is a prerequisite for effective AI deployment.

Demand for Real-Time Insights: Businesses increasingly require real-time decision-making capabilities. Whether predicting customer churn, optimizing supply chains, or detecting fraud, AI workflows must operate on fresh, integrated data.

Rise of Agentic AI: Unlike traditional AI, which focuses on narrow tasks (e.g., chatbots or recommendation engines), Agentic AI involves autonomous systems that execute complex, multi-step processes across departments. For example, an Agentic AI might analyze sales data, adjust inventory levels, and launch targeted marketing campaigns—all within a single workflow.

Cross-Enterprise Integration: Fortune 500 companies rarely have a single instance of any system. They operate multiple CRMs, ERPs, and data platforms, often across different regions and business units. A modern AI data stack must integrate these systems seamlessly.

Operational Efficiency: By breaking down data silos, businesses can streamline processes and reduce manual interventions. For example, a unified data platform can automate workflows that span sales, marketing, and supply chain operations.

Enhanced Decision-Making: AI-driven insights, powered by a comprehensive data ontology, enable faster and more accurate decisions. For instance, a retailer can use real-time data to adjust pricing based on demand, competition, and inventory levels.

Scalability: A modern AI data stack is designed to scale with the business. As enterprises adopt new applications or expand into new markets, the platform can integrate additional data sources without disrupting existing workflows.

Competitive Advantage: Companies that successfully implement cross-enterprise AI workflows can outpace competitors. For example, a financial services firm might use AI to detect fraud in real time while simultaneously personalizing customer offers.

Data Strategy and Assessment: We assess your existing data landscape, identify gaps, and develop a roadmap for building a modern AI data stack tailored to your business needs.

Data Integration and Governance: Leveraging Informatica’s tools, we help you clean, classify, and integrate data from diverse sources, ensuring a unified ontology that powers AI workflows.

AI Workflow Design: We design and implement Agentic AI workflows that span multiple business units, enabling automation and real-time decision-making.

Change Management and Training: We support your teams through the transition to a modern AI data stack, providing training and change management to ensure adoption and success.

Compliance and Security: We ensure your data platform complies with regulatory requirements and incorporates robust security measures to protect sensitive information.

The competition between Salesforce and other eneterprise software leaders highlight the stakes in the enterprise AI market. Any enterprise software based data platforms may arguably limit its appeal for enterprises with diverse, multi-vendor environments. Salesforce, by contrast, is betting on interoperability and flexibility, using Informatica’s capabilities to create a platform that integrates with a wide range of systems, including competitors like SAP, Oracle, and Microsoft. Salesforce’s pioneering zero-copy integration with Snowflake and Databricks positions it as a leader in the modern AI data stack and Agentic AI. By enabling real-time data querying without duplication, Salesforce’s Data Cloud unifies diverse data sources, breaking down silos for seamless AI workflows. This enhances Agentforce, Salesforce’s AI-driven platform, allowing autonomous agents to deliver accurate, context-rich insights across enterprise systems. With Informatica’s data management capabilities, Salesforce ensures robust governance and interoperability, creating a competitive moat against rivals. This strategic focus on data unification and AI orchestration establishes Salesforce as the strongest player in Agentic AI, driving enterprise efficiency and innovation.This strategy positions Salesforce to capture greater value by avoiding vendor lock-in and addressing the needs of enterprises with complex, hybrid architectures.

The modern AI data stack is a unified architecture that integrates and processes data from various enterprise systems to enable cross-functional AI workflows. This approach moves away from siloed systems like CRM, ERP, and data stacks, allowing businesses to create seamless, AI-driven processes across their organizations. Salesforce’s integration with Informatica is a strategic move to build such a platform, offering a competitive moat against other enterprise players by prioritizing interoperability and flexibility. As businesses demand more from AI, platforms like Salesforce’s are poised to redefine how enterprises operate, creating a future where AI workflows drive efficiency, innovation, and growth across the organization.

Labels: Agentic AI, Informatica, Salesforce