The most immediate and impactful benefit customers see from improved CRM data is in territory planning. This critical RevOps function effectively allows the team to 'steer the entire P&L' for a period. Accurate data on hierarchies, headcount, and location transforms this process from a manual, error-prone exercise into a strategic advantage.
Kernel's product strategy is to go deeper into company data challenges (e.g., complex APAC or government hierarchies) before going broader. This 'earn the right' approach builds customer trust by solving the core problem exceptionally well, creating pull for future product expansions rather than pushing a bloated, mediocre feature set.
Tools like Kernel differentiate from multi-provider waterfall solutions (e.g., Clay) by taking direct responsibility for data accuracy. Kernel provides a 48-hour data-fix SLA, eliminating the customer's burden of managing and validating multiple data sources. This shifts the model from a simple tool provider to an accountable data partner.
Contrary to the common 'instant results' narrative for AI, implementing an enterprise-grade data solution like Kernel.ai takes around four weeks. The process involves structured configuration, running large sample sets, and enabling actions like merging accounts. Complex enterprise CRMs require time and a dedicated process to properly integrate and clean.
The company's initial attempt to build an AI Sales Development Representative failed because CRM data was too inaccurate. They realized that any AI application built on faulty data is wasted effort, leading them to focus on solving the foundational data problem first, as AI cannot discern data quality on its own.
To conceptualize what's possible with modern AI data tools, RevOps leaders should frame the problem at the micro level. Instead of thinking about macro data fields, they should imagine having unlimited time and resources to fix one account record. This mental model helps identify high-value, manual processes that AI can now automate at scale.