Together, they examined the hard truths behind bad data hygiene, the explosion of disconnected GTM tech stacks, and the critical actions revenue operators must take now to reclaim control of their systems, their reporting, and ultimately, their company’s ability to scale.
Before you can dream about “AI-powered forecasting” or “predictive lead scoring,” you need to look in the mirror.
When asked what holds RevOps teams back from executing their strategy, not a single webinar attendee selected “speed of execution.” Instead, the overwhelming consensus was data silos, messy integrations, and lack of trust in reporting.
“We all say we want automation and AI, but we can’t even trust a single report across marketing, sales, and success.” – Tara McLaughlin
Bad data isn’t just an annoyance—it’s a tax. According to Tara, poor data hygiene costs U.S. companies an estimated $3 trillion annually, from wasted productivity to misrouted leads and inaccurate forecasts.
ZoomInfo’s research shows that contact data decays at nearly 30% per year, meaning the longer you wait to fix your systems, the more expensive and error-prone your operations become.
And with generative AI now plugged into every tool, those errors don’t stay hidden—they scale exponentially.
“Garbage in doesn’t just mean garbage out anymore—it’s garbage amplified.” – Tara McLaughlin
For more on the importance of data with AI agents, check out Your AI Agent is Only as Smart as Its Data.
The average revenue organization now uses 34 separate GTM tools, each storing different pieces of the customer journey. It’s a Frankenstein architecture that breeds duplication, inconsistency, and reporting chaos.
Sales is pulling from Salesforce, Marketing from HubSpot, Finance from NetSuite, and Customer Success from Gainsight—all speaking different data dialects. The result? No one agrees on what an account even is.
A unified revenue engine can’t exist when your CRM, MAP, and CS platforms don’t share definitions. That’s why, as Tara emphasized, integration is not the same as unification.
For more on building a tech stack in the age of AI, check out AI in Sales: How to Build Your Go-to-Market AI Strategy.
Most leaders think that “integrating” their tools solves the problem.
It doesn’t.
Integration just moves data from System A to System B.
Unification means every tool tells the same story.
Take something as simple as “industry.” Marketing might call a company “Software,” Sales calls it “SaaS,” and Finance logs “Technology.” To a computer, those are three different things—three different pipelines, three different forecasts.
Unification requires governance, normalization, and documentation—three tasks RevOps often overlooks in the rush to deliver dashboards. Without them, every KPI loses meaning.
For more on building a data driven CRM strategy, check out 6 Steps in Developing a Data-Driven CRM Strategy.
Both speakers urged operators to reframe their data strategy around three sequential layers.
Skip a layer, and you’ll spend all year firefighting.
Once the foundation is solid, orchestration turns raw data into usable plays. This means automatically enriching, routing, and prioritizing records across systems.
The goal isn’t volume—it’s relevance.
“Not every intent signal deserves action. Look at context, strength, and timing before you route a lead to sales.” – Tara McLaughlin
Build playbooks that factor in signal hierarchy. Example: a company researching your direct competitors should get immediate outreach; someone browsing a high-level category page should feed into nurture.
Every automated workflow should answer two questions:
Centralized systems are great for data governance but terrible for adoption.
Push the right data and recommendations into the tools sellers actually use—Salesforce, Outreach, Slack, or their inbox.
For example, use workflow automation to surface “Next Best Action” notifications directly in CRM instead of burying them in analytics dashboards. Every click saved is a conversion gained.
For more on building a data framework in the enterprise, check out Enterprise Data Strategy Guide: Building a Comprehensive Data Framework.
Everyone wants AI. Nobody wants to clean their data.
But the speakers were adamant—AI isn’t a shortcut. It’s an amplifier of discipline.
Predictive models, forecasting assistants, and generative copilots are only as good as the accuracy of the inputs they’re trained on. When your CRM fields are unstandardized, AI-driven summaries hallucinate. When contact data is stale, AI-powered engagement tools spam the wrong buyers.
“You can’t automate dysfunction. Fix the foundation first.” – Matthew Volm
The path to AI readiness starts with data completeness, structured taxonomy, and confidence scores on enrichment sources. Build validation rules to grade data quality automatically—then use those grades to prioritize cleanup efforts.
Check out Keeping Your CRM Data Perpetually Clean and Current for specific tactics on how to keep your data clean.
Data hygiene isn’t a one-time project; it’s a cultural habit.
Create a data governance council with representatives from every GTM function—marketing, sales, CS, and finance—to agree on definitions and enforce compliance.
Key practices:
When governance becomes routine, forecasting accuracy improves, cross-team reporting aligns, and RevOps finally earns its credibility as the strategic center of the revenue engine.
Also check out What is Data Governance? How to Build a Governance Framework.
Executives rarely fund “data projects.” They fund outcomes.
To get budget and buy-in, translate the cost of bad data into metrics leadership cares about:
Show leadership one broken dashboard that contradicts Finance’s numbers, and the value of data stewardship sells itself.
“When everyone is arguing over whose number is right, nobody’s hitting their target.” – Tara McLaughlin
Start small: clean one critical dataset—like your top 1,000 accounts or renewal opportunities—and document the before-and-after impact on productivity. Success scales from there.
For more on choosing a B2B data provider, check out the ZoomInfo blog post Your Guide to Choosing a B2B Data Provider.
Incremental improvement compounds. A one-percent gain in data accuracy per week equals a 68% improvement per year—a true competitive advantage for organizations investing early.
Check out all the recommended Operations Plays from ZoomInfo here.
By 2026, high-performing RevOps teams will share a few things in common:
“Data doesn’t need to be perfect—it just needs to be trusted. That’s the foundation of a revenue engine that wins.” – Matthew Volm
For more on this topic, check out the ZoomInfo blog post Your CRM Data: Not Ready for AI Primetime.
RevOps isn’t about more dashboards or more automation. It’s about trust, transparency, and truth in your data. The future won’t reward teams that move fastest—it will reward teams that move on the right information.
Your 2026 playbook isn’t a new tool or a shiny AI integration. It’s a commitment to discipline:
That’s how you build a foundation that wins.
Explore the RevOps Co-op Blog for case studies, frameworks, and AI best practices from top operators across the community, and join the community here to get an instant network of 18k+ operators from across the globe.
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