“Garbage in, garbage out”: the best description of RevOps teams’ experience of using AI to improve go-to-market efficiency in enterprise CRMs.
CRM data problems were always important, but never urgent. The assumption was that reps can just work around it. Eight duplicates of Vodafone, missing subsidiaries, wrong company names, the list goes on.
But now CEOs and CROs are pushing AI initiatives to improve go-to-market efficiency across core processes like territory allocation and mapping addressable markets.
To deliver on the promise of AI in large-scale RevOps processes, companies must fix the commercial landmine hiding in plain sight: the CRM is full of untrustworthy data that confuses AI as much as it confuses reps.
Anyone who has ‘played around with AI’ in an enterprise setting will know that consistency is key. It won’t show up in your P&L if you just give reps access to ChatGPT and ask them to ‘try this prompt template.’
RevOps teams have the data expertise and mandate to work across the entire go-to-market team to ensure consistency at scale. But they have an impossible job when it comes to correcting the CRM data that AI and reps need as context for decision-making.
Scroll through the LinkedIn feed of a RevOps person, and you’ll see beautiful agentic flows ‘solving’ problems with AI. ‘Comment below to get the prompt!’
Shadow an enterprise RevOps team, and you’ll find highly qualified professionals spending hours manually correcting data and fighting waves of complaints from reps about inaccurate headcount, revenue, and corporate hierarchies.
Dun & Bradstreet (D&B) had a monopoly (until now :-)) on enterprise-grade entity mapping and hierarchies. In theory, a must for any enterprise to have an accurate representation of its market in the CRM.
(Note: If you want to see why we didn’t mention ZoomInfo hierarchies, you should try it.)
D&B is tied to generic revenue and patchy headcount estimates, based on rigid legal entities. This locks your CRM into how D&B sees the market, not how you see your market. Reps are left to ‘fill in the gaps’ such as finding buying centers or deeper account research.
In response, the first wave of AI enrichment tools, such as Clay, promise to deliver the custom data that reps would otherwise research manually.
These tools are transformational in experimental settings and for smaller businesses. In an enterprise setting, they often end up creating more work than value, draining the time of RevOps teams without addressing root causes.
Here are the symptoms that bad CRM data is killing your GTM efficiency:
AI enrichment tools lack a clear notion of corporate entities, whereas D&B has a rigid notion of entities. Incorrectly mapped entities (and duplicates) derail entire data enrichment strategies and leave RevOps teams where they started, just with a much bigger AI credit bill.
RevOps teams are left with two options:
Until now.
Think of Kernel as an AI-native alternative to Dun & Bradstreet, built for your business.
Imagine being able to bring the intuitions of your best rep and the best RevOps person in the world to every record in your CRM. The result is a CRM that is a trusted view of your market as you see it.
It all starts with Kernel’s company universe:
Like D&B, Kernel has a database of entities sourced from registries, web crawling, and public LinkedIn profiles. Every entity is assigned a unique ID.
Unlike D&B, Kernel’s entities are paired with the structured and unstructured context associated with them (jobs, Wikipedia, website, etc.) optimized for AI to extract custom and accurate insights. This unlocks human-level mapping to patchy CRMs, custom account enrichment, such as estimating subsidiary headcount, and defining custom buying centers within corporate hierarchies. All paired with sources and human-readable reasoning explainers to build trust in the data and make it improve with feedback.
Mapping your CRM onto the Kernel company universe, like a RevOps team would with unlimited time.
One of the primary sources of data errors in CRMs is inaccurate mapping between enrichment providers and the CRM itself. Legacy enrichment providers use URLs, addresses, etc. to attempt this mapping, but for enterprise CRMs, these attributes are riddled with errors, leading to the failure of all downstream enrichment.
Kernel ingests the data in your CRM (URL, notes, contacts, etc.) and uses it to infer the correct entity in the Kernel company universe. This leads to significantly higher match rates. Exactly like a RevOps person with unlimited time would do it.
Using AI to turn your CRM into a trusted view of your market as you see it.
Once you have a truthful definition of every account in your market, paired with the context about the account, you can use AI to turn your CRM into the source of truth for your market.
Zip partnered with Kernel to remove 20,000 dead and duplicate accounts, enrich and prioritize over 100,000 accounts, and find over 20,000 new accounts in its expanding target markets (case study).
Navan partnered with Kernel to deliver precise travel spend intelligence to over 300,000 accounts and assign territories across more than 300,000 CRM accounts using a reliable headcount figure verified from primary data (case study).
Remote used the Kernel to find all high-quality accounts globally. Remote then split the accounts into 50+ territories based on region and product focus. As part of this, Remote grew their target account list by 15,000 high-value accounts that were previously missing from their CRM. (case study)
In the age of AI, enterprise decision-making will only be as good as the truthfulness of the context fed to AI. Garbage in, garbage out.
When you add context, data is no longer a passive record; it becomes a dynamic reflection of reality that AI can extract insight from.
Closing the gap between recorded data and real-time context will fundamentally rewire how every enterprise makes decisions.
Our mission is to build the source of truth for enterprise AI. Our starting point is empowering RevOps teams to turn their CRM into the source of truth for their market as they see it.
