The challenge

Rubrik is built for enterprise scale. The company has grown beyond $1 billion in revenue, serves the enterprise market, and supports hundreds of sellers working complex deals across multiple products and regions.

That scale created a familiar RevOps problem. Individual sellers and teams were working from different sources of information. Some relied on ZoomInfo. Others used Sales Navigator. Others had their own spreadsheets or self-sourced lists.

At a smaller company, that kind of patchwork might be annoying. At Rubrik’s scale, it created real operational drag.

Different teams could collide around different data points. Sellers could spend valuable time finding, checking, and cleaning contact data instead of generating pipeline. Ops was stuck in what Zach Hoogerland, Senior Director of Marketing Strategy and Operations at Rubrik, described as “firefighting galore.”

The team did not just need more data. They needed a better system for deciding which data to trust, how to apply it, and how to get it into the right workflow.

And with AI becoming a bigger part of the GTM conversation, the stakes were even higher. Rubrik knew that agentic selling and AI-assisted workflows would only work if the underlying GTM data was clean, governed, and usable.

You’re not getting to agentic selling unless you’ve got your fundamentals in place. — Zach Hoogerland, Senior Director of Marketing Strategy and Operations, Rubrik

The solution

Rubrik approached the problem with a simple rule: start with the business outcome.

Before choosing where enrichment should fit, the team looked at the cost of the current process. How much time were reps spending sourcing and checking data? What was the opportunity cost across hundreds of sellers? What would it mean for pipeline generation if sellers could spend that time on higher-value work?

That business case helped Rubrik align leadership around the need for a more centralized, trusted GTM data process. But leadership alignment was only half the work. Rubrik also needed sellers to understand what was changing and why it would help them.

The team built from both directions: top-down alignment with sales leadership and bottom-up adoption from sellers who could test, validate, and champion the new process.

Openprise became the governance layer

Rubrik already had a complex GTM motion with multiple product lines, personas, teams, and routing needs. Before scaling enrichment, the team needed a way to govern what happened after data entered the building.

Openprise became that orchestration layer.

With Openprise, Rubrik could normalize data, match leads to accounts, deduplicate records, enforce business logic, map leads to product personas, and route them to the right teams. Instead of treating enrichment as the finish line, Rubrik used Openprise to turn enriched data into data sellers could actually use.

Clay could help acquire and enrich the right data. Openprise made sure that data fit Rubrik’s business.

Clay powered the enrichment motion

Rubrik evaluated enrichment through an outcome-first lens. The first goal was not to enrich every possible field. It was to improve coverage for the data points that would help sellers move fastest: email addresses and mobile phone numbers.

The team tested data quality and coverage across global theaters, including AMER, ANZ, APJ, and EMEA, and worked with SDR teams to validate whether the data was actually useful in practice.

From there, Rubrik designed three enrichment waterfalls:

1.   Inbound enrichment to improve speed to lead and give SDRs better data from the start.

2.   Outbound enrichment to support seller-led and signal-based prospecting with higher-fidelity contact data.

3.   Re-enrichment feedback loops so sellers can flag bad data and give Ops a way to refresh it instead of letting poor-quality records sit in queues.

That third waterfall matters because Rubrik did not want enrichment to become a “throw it over the fence” process. If a seller says the data is bad, Ops needs a way to respond, validate, and try again within reasonable cost controls.

Rubrik is building toward a 24-hour feedback model where seller-submitted data issues can be reviewed, refreshed, timestamped, and returned with updated information.

How it works

For inbound, Rubrik starts by filtering for leads that are actually worth enriching. The team does not enrich every form fill because, as Zach put it, that would just burn money.

Once a lead qualifies, the process begins:

1.   Clay enriches the lead through a multi-vendor waterfall.

2.   Openprise applies Rubrik’s governance rules, maps the lead to a product persona, and routes it to the right SDR team.

3.   Salesforce and Outreach receive the data sellers need to act.

4.   SDRs can work from Outreach while leaders maintain visibility into lead status and follow-up activity in Salesforce.

That cleaner data flow also gives Rubrik room to test practical AI use cases. Because the team knows the person’s title, email, likely product interest, and routing context, they can start using AI to help draft relevant outbound sequences that connect enrichment, orchestration, Salesforce, and Outreach.

This is the difference between using AI because someone asked for “AI stuff” and using AI because the data foundation finally supports a real use case.

The impact

Rubrik is still on the journey, but the shift is already changing how the team works.

For sellers, the experience is becoming more trusted. When a lead shows up, they have cleaner data, clearer context, and a better chance of reaching the right person.

For sales leaders, the process creates visibility. Leaders can see which leads are being worked, which are in sequence, and how activity is flowing between Outreach and Salesforce.

For Ops, the work is moving from reactive to proactive. Instead of chasing every data complaint, the team can build systems that prevent problems, monitor quality, and create feedback loops when something needs to be fixed.

The mindset shift is the real story. Rubrik is moving from “firefighting” to what Zach called “carpentry”: building the foundation layers that make GTM data more reliable, repeatable, and scalable.

Why it matters for AI readiness

Like many enterprise teams, Rubrik is being asked how AI can change the GTM motion. But Zach’s approach is not to start with AI for AI’s sake. It is to start with the use case, map it to the business outcome, and identify the data foundation required to make it work.

That might mean starting with something simple, like using AI to translate or classify job titles. It might mean using enrichment and persona mapping to generate more relevant email sequences. It might eventually mean supporting more agentic selling motions.

But the principle is the same: AI cannot fix a broken data process. It will usually amplify it.

That is why Openprise’s deterministic governance layer matters. Openprise helps teams handle as much of the data work as possible through rules, logic, normalization, matching, routing, and controls. AI can then be used where it actually adds value instead of being forced into processes that are not ready for it.

In other words, Rubrik is not just preparing for better enrichment. It is preparing for smarter GTM execution.

Results

Rubrik’s Openprise + Clay enrichment motion gives the team a stronger operating model for GTM data at scale:

• More trusted seller data by combining enrichment with governance, matching, deduplication, and routing.
• Cleaner inbound handoff by enriching qualified leads, mapping them to product personas, and routing them to the right SDR team.
• Better operational visibility across Outreach and Salesforce so leaders can see what is being worked and what needs attention.
• A scalable feedback loop for re-enrichment so bad data can be flagged, refreshed, timestamped, and returned to sellers.
• A practical AI foundation that supports real use cases instead of layering AI on top of messy data.

Rubrik’s story is a reminder that the future of GTM is not just about finding more data. It is about making data usable.

Clay helps Rubrik get enriched data into the system. Openprise helps make sure that data is governed, routed, and ready to drive action.

Together, they give Rubrik the foundation to move faster today and build smarter AI-powered GTM motions tomorrow.