The most advanced GTM teams are abandoning traditional CRMs like Salesforce as their primary interface. Instead, they use data warehouses (Snowflake, Databricks) for flexible data storage and push curated insights to reps directly within their workflows (Slack, email, Notion), eliminating the need for manual data entry and retrieval.
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Despite promises of a single source of truth, modern data platforms like Snowflake are often deployed for specific departments (e.g., marketing, finance), creating larger, more entrenched silos. This decentralization paradox persists because different business functions like analytics and operations require purpose-built data repositories, preventing true enterprise-wide consolidation.
A company solved its sales team's information gap by treating 25,000 hours of recorded Gong calls as the ultimate source of truth. This existing internal data, previously ignored, became the foundation for a company-wide AI automation strategy that transformed their go-to-market operations.
The critical flaw in most sales tech is its failure to correlate rep behavior with performance outcomes like quota attainment. The real value is unlocked not just by knowing what reps do, but by connecting those actions to who is succeeding, thus identifying true winning behaviors and separating A-players from C-players.
Top-performing companies are abandoning traditional metrics like MQLs. They now focus on understanding the entire prospecting process—from lead creation to BDR/SDR engagement—to generate stronger pipeline, higher win rates, and more revenue with less wasted effort.
A key differentiator is that Katera's AI agents operate directly on a company's existing data infrastructure (Snowflake, Redshift). Enterprises prefer this model because it avoids the security risks and complexities of sending sensitive data to a third-party platform for processing.
Companies are replacing traditional, siloed sales assembly lines with a centralized "GTM Engineer." This technical role uses AI and automation tools to build revenue systems, absorbing the manual research and prospecting work previously done by individual reps. This allows for rapid learning and scaling of creative ideas across the entire team.
The go-to-market tool market is fragmented because sales tactics have a short shelf life, quickly rendering point solutions obsolete. The future belongs to integrated platforms that act as an "IDE" (Integrated Development Environment), allowing teams to rapidly experiment, iterate, and execute new GTM strategies.
The core problem for many small and mid-market businesses isn't a lack of software, but an excess of it, using 7 to 25 different apps. This creates massive data fragmentation. The crucial first step isn't buying more tools, but unifying existing data into a single customer profile to enable smarter, automated marketing.
Companies struggle to get value from AI because their data is fragmented across different systems (ERP, CRM, finance) with poor integrity. The primary challenge isn't the AI models themselves, but integrating these disparate data sets into a unified platform that agents can act upon.
To build effective GTM automation, hire people who understand both the technology and the sales process. Vercel found success by transitioning its technical sales engineers—who were already former developers—into GTM Engineer roles. This ensures automated workflows are grounded in proven, real-world sales best practices.
