Infrastructure built for app-to-app integration, like Salesforce's MuleSoft, is being repurposed to govern, orchestrate, and secure AI agents. This 'agent fabric' provides a foundational control plane for managing complex agentic workflows across the enterprise, extending the value of existing integration investments.
Simply providing data to an AI isn't enough; enterprises need 'trusted context.' This means data enriched with governance, lineage, consent management, and business rule enforcement. This ensures AI actions are not just relevant but also compliant, secure, and aligned with business policies.
AI models fail in business applications because they lack the specific context of an organization's operations. Siloed data from sales, marketing, and service leads to disconnected and irrelevant AI-driven actions, making agents seem ineffective despite their power. Unified data provides the necessary 'corporate intelligence'.
Salesforce operates under a 'Customer Zero' philosophy, requiring its own global operations to run on new software before public release. This internal 'dogfooding' forces them to solve real-world enterprise challenges, ensuring their AI and data products are robust, scalable, and effective before reaching customers.
As unified data platforms become more low-code and no-code, the need for deep technical upskilling diminishes. Instead, data teams create more value by focusing on their specific business domain expertise (e.g., marketing, sales) and applying it through the platform's configurable tools.
According to Salesforce's Rahul Auradkar, many early Customer Data Platforms (CDPs) failed to deliver a holistic view, functioning instead as 'Marketing Data Platforms.' A true customer platform must unlock and harmonize data from all domains—sales, service, and marketing—to power genuine AI-driven insights and actions across the entire customer lifecycle.