ZoomInfo

Vercel v0 Integrates ZoomInfo's GTM.AI

ZoomInfo announced an integration that allows applications built in Vercel v0 to read verified ZoomInfo go-to-market data via GTM.AI. GTM.AI exposes the GTM Context Graph — identity-resolved data accessible through an API and a Model Context Protocol (MCP) endpoint — which ZoomInfo says contains records on 100M companies, 500M contacts, and billions of signals. Vercel v0’s natural-language-to-app capability can call GTM.AI so apps are grounded in live, verified company, contact, buying-signal and intent data. Calls inherit ZoomInfo authentication, access control, data lineage, AI policy and audit logging. The release positions GTM.AI as a context layer for AI app building and lists completed integrations with multiple sales, CRM and AI platforms. (Published 2026-07-01.)

ZoomInfo Named 'One to Watch' in Snowflake Report

ZoomInfo announced at Cannes Lions 2026 that Snowflake recognized it as a Data & Identity “One to Watch” in The Modern Marketing Data Stack: Governing the Agentic Enterprise. Snowflake’s fifth annual report — based on input from over 11,500 Snowflake customers and partners across 13 categories — highlights a shift toward AI-driven, agentic marketing systems built on governed data foundations. ZoomInfo described GTM.AI and its GTM context layer as making ZoomInfo data, audiences and agents directly callable inside Snowflake AI Data Cloud without exports or separate identity reconciliation. Quotes from Dennis Sevilla (ZoomInfo CMO) and Denise Persson (Snowflake CMO) emphasize integration, governance, lineage and agentic use cases for revenue teams and AI-first go‑to‑market stacks.

AI Automates Workflows, Redefines Marketing Operations

The article argues that marketing operations (MOps) are shifting from human-defined workflows to AI-driven execution. Legacy MarTech vendors (Salesforce, HubSpot, Marketo/Adobe) are adding AI features, but a new generation of AI-native tools — including AI-native CRMs, predictive scoring, dynamic enrichment, and orchestration agents — is being built with autonomous execution as a foundation. Examples highlighted include Clarify AI (ambient CRM behavior), MadKudu/6sense/Pecan AI (predictive scoring), Clay/Clearbit/Coresignal (dynamic enrichment), and Relevance AI/Lindy (AI agents/orchestration). As systems take over execution and process logic, the MOps role should shift from building and maintaining workflows to interpreting model outputs, defining success metrics, and aligning AI decisions with business strategy.