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A lot of RevOps teams are either in SKO season or just coming out of it. The big question: are you planning territories, or just redistributing bad data? Most of the problems I see come down to entity identity in your CRM. If you don’t know which company an account actually is, everything else breaks down. In the video, I explain the hierarchy trade-off and why your data strategy needs to follow your go-to-market strategy, not the other way around. Even post-SKO, it’s not too late to have these conversations and set up your CRM for better territories and higher revenue potential. Watch the full video to learn more, and drop me a DM if you have any questions.

Most CRMs don’t reflect where buying actually happens. That’s where territory plans start to drift. In the first episode of Confessions of a CRM, Gerry Cunningham shared how Gong stopped planning purely around HQ location and started mapping actual buying centers inside complex enterprise families. That shift led to: - Less time wasted on subsidiaries that can’t purchase - Fewer dead-end sales cycles - More focus on accounts that can actually deploy Gerry explained how Gong uses agentic entity data from Kernel to identify these buying centers, giving them a hierarchy-aware foundation to design territories more efficiently. If you’re scaling regional hubs or managing complex accounts, you’ll want to hear Gerry’s approach and the practical lessons Gong has learned. Full episode linked in the comments.

Inaccurate revenue data does not just break planning. It breaks trust. When revenue numbers are wrong, RevOps takes the heat, and reps end up fighting over the same accounts. Territory design falls apart before the quarter even starts. When I speak with RevOps teams, I see traditional revenue enrichment fail in two big ways: 📈 Public companies have many valid revenue figures, but enrichment tools rarely explain which one they picked or why, leading to inconsistency. The enrichment also becomes stale fast. 💸 Private company revenue is largely inferred, with limited coverage, low accuracy, and no visibility of how accurate the estimate is or what the source is. Zina LARBI explains the damage traditional enrichment providers cause by sharing zero context, using unspecified revenue definitions, and relying on a hidden waterfall of sources and heuristics. RevOps is left guessing which number is real, which year it applies to, and whether it should be trusted. That leaves teams forever fixing symptoms instead of solving the root problem. At Kernel, every revenue point comes with context, clear reasoning, the reported year, and the sources behind it. Reps understand the data. RevOps plans with confidence. Account disputes disappear. We call this Agentic Company Data. When something looks off, RevOps can flag it directly in the Kernel platform. The issue is resolved within 48 hours, creating a clear source of truth for reps and removing reactive data hygiene from RevOps’ schedule. Agentic company data combines transparent reasoning with built-in feedback loops to build confidence in accurate and fresh company data. Revenue estimates are tailored to how your organization sells, and when something looks off, it can be explained or corrected with the full context. Kernel owns data accountability, so RevOps can plan confidently without manual data correction work. If this resonates, we go deeper into how Agentic Company Data works in practice with Jordan Crawford and my co-founder, Marcus Henglein. ⬇️ Link in the comments ⬇️

Want to see what Kernel’s CRM and account data actually look like? In last week’s session with Ramy A. from Pigment and Matthew Volm from RevOps Co-op, we walked through how enterprise RevOps teams are rethinking planning and account data management in the age of AI. The clip below shows how we use Kernel to map complex corporate hierarchies inside our own CRM and build the account data foundation that makes it possible. A big theme from the discussion was the shift from static to dynamic account data. Most teams still rely on rigid, once-a-year segmentation and firmographics that go stale quickly. The market changes continuously, and your planning needs account data that keeps pace. AI can help with this, but only if it starts with accurate entities, identities and buying centers rather than generic data from traditional providers. We also spent time on the power of custom hierarchies and real buying centers. Legal entities rarely match how a business actually decides or buys. Using AI to model the true structure of an organization gives RevOps a far more accurate base for planning, territory design and forecasting. This shift is already happening inside the enterprise RevOps teams we work with at Gong, Navan, Zip and Remote. It was especially valuable to hear how Ramy and the Pigment team use Kernel to build the account data foundation they need for accurate planning. If you missed the full conversation, the link is in the comments. Don't miss our next event with Jordan Crawford next Tuesday 📅

Legacy enrichment tools treat firmographic data as a checkbox exercise. Sales reps spot the obvious mistakes, and RevOps teams take the blame. At Kernel, we treat each data point as its own product. Our product team uses proprietary tooling, search agents, and reasoning agents to constantly improve core firmographic accuracy over time. For every core firmographic data point, we also assign a dedicated product owner. Headcount is one of the most critical data points to anchor the CRM, territory planning, and market segmentation around. Katie Peachey owns it end-to-end: structured and unstructured data sources, heuristics, exceptions, edge cases, and explainability. Our process replicates how a RevOps professional would establish firmographic data accuracy with unlimited time, based on foundational AI-native master entity data, account identity resolution, agent searches, and our reasoning agents. What does this approach prevent? ➡️ Entity confusion. Mosaic, the VC, vs. Mosaic, the biotech is an example of where generic AI tools merge or mix entities. ➡️ Single-source heuristics. Relying on LinkedIn alone can be wildly misleading. Upwork is a perfect example. ➡️ Stale databases. Fast-growing companies like Anthropic can double headcount long before a static vendor refreshes. ➡️ Missing primary data. Most systems do not have the workflows or expertise to find and validate it at scale. 🚨 Sales reps messaging RevOps with obvious mistakes and undermining credibility in CRM data. Here's how this accuracy translates into value at enterprise scale: ✅ Accuracy you can trust across hundreds of thousands of accounts, not just a handful of hand-checked logos enables confident planning at scale. ✅ Fewer escalations from reps who spot obvious errors in territories or TAM. ✅ Explainability. Reps can see why a headcount number is correct and review the underlying evidence and data sources. ✅ Better planning. Cleaner segments, cleaner territories, and fewer missed high-value accounts caused by incorrect data. When core firmographics are developed with ownership, data source evaluation, and feedback loops, RevOps teams finally get the data quality they need to plan with confidence. Our reasoning agent avoids the black box experience that's at the root of broken trust between sales and RevOps. If you want to see the difference for yourself, DM me and we can benchmark Kernel headcount data against whatever you are using today, whether it's your own CRM data, your enrichment vendor, or both. 👉 https://lnkd.in/dRkyzHNw Save your seat for my conversation with Jordan Crawford on the topic next Tuesday: 👉https://lnkd.in/dw7UqBWY

If you’re in planning mode, here’s how your CRM needs to look to ensure confident planning at scale. At Kernel, we use our own product inside our Salesforce to build custom account hierarchies, define custom verticals, and power high-accuracy firmographics that RevOps teams depend on for territory design, book cutting, and planning. In this clip from our masterclass with Ramy Atassi, Senior RevOps Manager at Pigment, you’ll see exactly how clean, trustworthy account data transforms planning, from reducing misfires and missed accounts to enabling true revenue predictability. If you want to learn how to: ✔ Avoid missed ICP accounts in planning ✔ Build accurate books of business without manual cleanup ✔ Eliminate wasted cycles caused by bad firmographics ✔ Make planning decisions with total confidence Click through to watch or download the full conversation with RevOps Co-op. https://lnkd.in/eucwqSE2