For most of the last five years, the fix for a stalled sales team was another tool: a new contact database, a separate signal feed, a standalone call recorder, a bolt-on forecasting dashboard. Two case studies published this week by ZoomInfo customers point to a different pattern taking hold across revenue organizations. Instead of adding another point solution, sales enablement and revenue operations teams are consolidating onto a single AI-native data layer, and the productivity and cost numbers are starting to justify the switch.
The Stack That Stopped Paying Off
The old model was additive. A sales engagement platform handled outreach cadences. A separate vendor recorded and transcribed calls. A third tool scored intent signals. A fourth tracked forecast variance. Each tool did its narrow job well, but reps paid the tax: toggling between browser tabs to reconstruct a single account’s history, re-entering the same contact record in three systems, and reconciling numbers that never quite matched.
Revenue operations teams at companies like Tegus, the investment research platform acquired by AlphaSense, ran the audit that a lot of RevOps leaders have been putting off. Tegus found forecasting software, sales engagement tools, conversation intelligence, and third party data providers doing overlapping jobs, some of it duplicated outright. According to a case study published by ZoomInfo, consolidating conversation intelligence onto the data platform it already used for contact and company records cut that specific spend by 50 percent compared with its previous vendor, while the team went on to exceed its sales targets for two consecutive quarters, hitting 109 percent and 101 percent of goal. The company migrated three years of recorded calls into the new environment without losing data, according to the case study.
The pattern is not confined to enterprise revenue teams either. LINAK US, a Danish manufacturer of linear actuators running a much smaller B2B marketing operation, made a similar move for a different reason: its previous platform could show activity but never revenue impact. After switching, the company ran a single targeted display campaign through ZoomInfo’s demand-side platform and generated quotes within three weeks, its first measurable marketing return in years, according to a ZoomInfo case study. The detail that matters for revenue operations leaders is not the dollar figure but the mechanism: sales and marketing worked from the same account list and contact data instead of two separate research processes, which is the same underlying shift showing up at Tegus and Seismic at a larger scale.
What Changes When the Data Layer Is Shared
The mechanism behind both case studies is the same: once contact data, buying signals, and call intelligence sit on one platform, an AI layer on top can act on all of it at once instead of stitching together exports from separate systems. ZoomInfo has been pushing this consolidation aggressively, building infrastructure aimed as much at AI agents as at individual sellers.
Seismic, the sales enablement platform used by more than 2,000 organizations, is the clearer test case for what that unified layer can do for pipeline generation rather than just cost. According to a ZoomInfo case study, Seismic layered an AI copilot on top of the buying-signal and contact data its sellers already trusted, combining that external data with its own CRM records to generate account and persona specific messaging automatically. The result, per the case study: 39 percent of Seismic’s active pipeline is now attributed to opportunities identified or influenced by those signals, the team reported a 54 percent increase in productivity, and reps saved an average of 11.5 hours a week that had previously gone to manual research. Toby Carrington, Seismic’s chief business officer, said in the case study that pairing the two data sets “helped us craft very specific account- and persona-based messages” and cut the time needed to assemble account context that a veteran rep would otherwise build by hand.
The Skeptic’s Read
Both figures come from the vendor whose platform is being credited with the improvement, so they deserve the same scrutiny a sales leader would apply to any case study: self-reported, single-customer, and published as marketing content rather than independently audited. A 39 percent pipeline attribution rate or a 50 percent cost cut is a meaningful data point, not proof that the same math holds for a different sales motion, deal size, or vertical. What the two cases do establish, independent of the specific numbers, is a direction: the value in revenue AI is increasingly coming from unifying data before applying intelligence to it, not from adding another isolated model on top of an already fragmented stack.
What It Means for the Sales Leader
The practical takeaway is less about switching vendors and more about how to evaluate the next tool request that lands on a RevOps leader’s desk. Three questions follow directly from these two cases:
Where is the redundancy already hiding?
Tegus found its overlap through a straightforward stack audit: listing every tool with access to contact, call, or pipeline data and asking which ones are solving the same problem twice. That audit, not a vendor pitch, is what surfaced the savings.
Does the AI layer sit on unified data, or on top of a fresh integration project?
An AI copilot that requires reps to first reconcile three separate data sources before it can act will struggle to produce the kind of time savings Seismic reported. The productivity gain came from removing steps, not adding a smarter step on top of the same fragmented process.
What would this look like in your own data, not the vendor’s case study?
Tegus’s own advice, cited in its case study, is to ask any consolidation vendor to demonstrate results against a company’s actual data before signing, turning a leap of faith into a bounded pilot with a clear before-and-after comparison.
Neither case study is a mandate to rip out an existing stack. But both are evidence that the next wave of sales productivity gains is more likely to come from unifying the data underneath an AI layer than from adding one more specialized tool on top of an already crowded stack.
Source: ZoomInfo
