{"id":16439,"date":"2025-12-08T10:05:56","date_gmt":"2025-12-08T10:05:56","guid":{"rendered":"https:\/\/www.salesforce.com\/?p=16439"},"modified":"2025-12-08T10:42:27","modified_gmt":"2025-12-08T10:42:27","slug":"tableau-model-context-protocol","status":"publish","type":"post","link":"https:\/\/www.salesforce.com\/in\/blog\/tableau-model-context-protocol\/","title":{"rendered":"How to Make Agentic AI Work Responsibly with Enterprise Data"},"content":{"rendered":"\n

India stands at an inflection point. The intersection of Digital India initiatives, massive IndiaAI Mission investments, and a booming startup ecosystem creates fertile ground for enterprise-grade AI to flourish.<\/p>\n\n\n\n

According to a recent EY India survey, 36% of Indian enterprises<\/a> have already allocated budgets and begun investing in generative AI, while another 24% are actively testing its potential. The question is no longer whether<\/em> to adopt AI, but how<\/em> to deploy it responsibly and securely.<\/p>\n\n\n\n

For the modern CXO, the dilemma is about velocity vs trust: Generative AI can analyse, predict, and produce business intelligence faster than ever; but speed means nothing if the insights generated aren\u2019t accurate or trustworthy. <\/p>\n\n\n\n

Large language models (LLMs) are powerful interpreters, but without a well-governed and authoritative data feed, their outputs are prone to hallucination, compliance failures, and flawed decision-making. The cost of acting on erroneous AI-generated intelligence can quickly undermine any efficiency gain.<\/p>\n\n\n\n

This is the gap filled by the Tableau Model Context Protocol <\/a>(MCP<\/a>) framework.<\/p>\n\n\n\n

From passive reporting to agent-orchestrated decision-making<\/h2>\n\n\n\n

Simple conversational interfaces are rapidly giving way to agentic AI \u2013 autonomous systems that are capable of reasoning, acting, and collaborating across siloed business functions. The Digital Enterprise 2025 report <\/a>notes that 27% of companies already have AI agents in production or at scale, with another 31% in proof-of-concept stages.<\/p>\n\n\n\n

For executives, this shift represents a profound opportunity to engage with business intelligence in new ways. Instead of asking “What happened?” (a dashboard function), executives can focus on “What should we do next?” (an autonomous action).<\/p>\n\n\n\n

Imagine an ecosystem where dedicated AI agents don’t just communicate with users, but also with each other, grounded in a single source of truth.<\/p>\n\n\n\n

This empowers leaders to ask hyper-specific, strategic questions \u2013 and receive real-time, verified answers. No more struggling with manual data analysis, or  latencies inherent in static dashboards. <\/p>\n\n\n\n

Agentic AI delivers instant responses to questions like:<\/p>\n\n\n\n