Snowflake moved beyond basic AI tools by building proprietary agentic models. One agent analyzes campaign data in real-time to optimize ad spend and ROI. A second 'competing agent' provides on-demand talking points for sales and marketing to use against specific competitors, solving a massive enablement challenge.
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VCs traditionally advise against early product expansion. But with agentic AI, which leverages existing metadata to solve new problems without building new screens, startups can rapidly add capabilities to meet customer demand for a single, unified agent, accelerating the compound startup model.
Don't just set and forget your lead scoring AI. Create a separate, time-based agent that analyzes recent closed-won deals. This "meta-agent" can then identify new success patterns and suggest updates to the primary scoring agent's prompt, ensuring your qualification model evolves with live data.
Beyond just generating creative, the future of AI in CRM is using "agentic AI" to build better strategies. This involves agents that help define audience segments, determine the next best product or action, and accelerate the implementation of complex campaigns, enhancing human strategy rather than replacing it.
Instead of a generalist AI, LinkedIn built a suite of specialized internal agents for tasks like trust reviews, growth analysis, and user research. These agents are trained on LinkedIn's unique historical data and playbooks, providing critiques and insights impossible for external tools.
Beyond simple analysis, Claude 4.5 can ingest campaign data and generate a shareable, interactive dashboard. This tool visualizes key metrics like LTV:CAC, identifies trends, and provides specific, data-backed recommendations for budget reallocation. This elevates the AI from a data processor to a strategic business intelligence partner for marketers.
AI tools are shifting power dynamics. By deploying AI agents for tasks like inbound lead qualification, CMOs can regain direct control over pipeline conversion—a function often managed by sales-led SDR teams. This elevates marketing from a cost center to a strategic, revenue-driving hero.
Snowflake's former CRO offers a pragmatic view of AI, calling it a 'task automator.' He stresses that for enterprise adoption, AI tools can't just be 'cool.' They must deliver a clear return on investment by either generating revenue or creating significant cost savings, like the 418 hours per week saved by their support team.
A powerful AI use case is running automated agents on sales call transcripts. These agents can perform tasks like extracting and populating MEDPICC data into Salesforce or summarizing competitor mentions for battle cards, saving sales teams hours of manual work per week.
Snowflake Intelligence is intentionally an "opinionated agentic platform." Unlike generic AI tools from cloud providers that aim to do everything, Snowflake focuses narrowly on helping users get value from their data. This avoids the paralysis of infinite choice and delivers more practical, immediate utility.
A custom AI system named Marilyn, built by the CMO and one engineer, has become the central nervous system for Wiz's GTM team. It answers complex questions on competition, product docs, and strategy, even translating content for global teams. This demonstrates the immense ROI of building custom internal AI tools.
