AI can’t fix what your data is breaking

This year, Openprise partnered with RevOps Co-op and MarketingOps to conduct a survey on data quality. This collaborative effort resulted in over 150 responses from operations professionals, giving us new insights into how people define data quality, what holds businesses back from achieving better data quality, and what patterns differentiate teams that achieve good data quality from the rest. Get the full report here.

You’re at a company all-hands when the CEO announces the latest strategic initiative.

“We’re investing heavily in AI to transform our go-to-market operations,” she says, outlining plans for predictive lead scoring, automated personalization, and intelligent forecasting.

It sounds impressive. But you and the ops team know what leadership hasn’t considered: your customer data is 30% incomplete, filled with duplicates, and scattered across systems that barely integrate.

According to the 2025 State of RevOps Survey, 80% of operations professionals are already experimenting with AI. But the teams seeing real results aren’t the ones with the fanciest algorithms. They’re the ones who solved their data quality problems first.

The survey reveals a clear pattern: organizations with poor data quality face significantly more barriers to AI adoption, while those with clean, integrated data are using AI strategically to solve real business problems.

If your company is rushing into AI without addressing data quality, you’re not building a competitive advantage. You’re just automating your existing problems.

Why AI initiatives fail without good data

According to the 2025 State of RevOps Survey, organizations with the poorest data quality face significantly more barriers to adoption:

• 53% struggle with limited access to high-quality data (compared to 27% with acceptable data quality)
• 46% face security and compliance restrictions (vs. 27% with better data)
• 53% cite cost and budget constraints (compared to 39% with good data)

The data tells a clear story: teams with poor data quality face significantly more hurdles to adopting AI effectively.

That’s because most organizations approach AI backwards. Instead of starting with business problems and working toward solutions, teams get swept up in AI hype and start with the technology.

This backwards approach leads to predictable failures:

• Dirty data, flawed predictions: AI learns from inconsistent inputs, producing unreliable outputs that teams quickly learn to ignore.
• Integration nightmares: Sophisticated AI tools can’t access the data they need when it’s locked in siloed systems.
• Trust erosion: Sales teams discard AI recommendations that conflict with their field experience, undermining adoption.
• Compliance surprises: Legal departments shut down personalization engines that inadvertently violate data privacy regulations.

One frustrated survey respondent captured this perfectly:

“No matter how much I stress the importance, leadership believes they can sprinkle some money, and a fairy will just clean it all up.”

How high-performing teams make AI work in RevOps

Unsurprisingly, the 2025 survey revealed that companies are most interested in using AI to personalize sales and marketing outreach (39% of respondents) and predict customer fit, intent, and churn (33%).

But successful implementation requires a fundamentally different approach. Here’s what teams with better data quality do differently:

They build the foundation before adding AI

Before implementing any AI solution, successful teams ensure they have:

• Complete, standardized data: Lead-to-account matching accuracy above 95% and consistent field completion across critical data points
• Integrated systems: Automated data flow between CRM, marketing automation platforms, and data warehouses
• Unified definitions: Shared data quality standards across departments that eliminate confusion and inconsistency

Our survey found that organizations with acceptable data quality are 49% more likely to use designated platforms with automatic integration, the foundation that makes AI initiatives successful.

They let business problems guide AI selection

Instead of asking “How can we use AI?” successful teams start with “What business problems are we trying to solve?”

The most effective AI implementations in RevOps focus on specific, measurable challenges:

• Automating lead scoring and prioritization (used by 39% of survey respondents)
• Improving customer segmentation and targeting (37%)
• Enhancing sales forecasting and pipeline management (30%)
• Analyzing and predicting customer behavior (33%)

As one respondent noted:

“We have many disagreements about what data we should use as our guiding light. The data that marketing needs is different from sales data, and this causes frustrations across the two organizations.”

They prioritize operational excellence over innovation

The unsexy truth about successful AI in RevOps is that it’s often about doing basic things better, not revolutionary breakthrough moments.

“We are doing a lot of manual work, and segmentation is a mess. It is hard to look at the data without doing a lot of manipulation, cleansing, etc.”

Organizations with good data quality focus on operational excellence first. They use AI to:

• Automatically clean and standardize incoming data from multiple sources
• Route leads accurately to the right sales reps without manual intervention
• Flag data quality issues before they compound across systems
• Generate consistent reports across departments using unified metrics

This approach aligns with findings from our previous research on leadership alignment: the most successful organizations treat data quality as a fundamental business practice, not a one-time technical fix.

Data excellence is your AI insurance policy

The survey data is unambiguous: Teams with better data quality are more successful with AI. They’re less likely to face cost, security, and access hurdles, and more likely to see measurable ROI from their AI investments.

Here’s how to position your organization for AI success:

Implement data automation: Integrated RevOps data automation provides the clean, verified inputs that AI systems need to function effectively.

Build verification systems: AI hallucinates and makes errors. Your organization needs automated systems to verify AI accuracy and error remediation workflows to correct mistakes before they impact business decisions.

Navigate compliance proactively: If your InfoSec team is blocking AI adoption, take a closer look at your AI tools to ensure they meet security standards. Avoid “shadow AI” that sends sensitive GTM data to public APIs, and design workflows that protect personally identifiable information.

Think ecosystem, not tool: Current AI can’t deliver “lights-out automation.” It needs a supporting ecosystem of technologies to provide clean data, verify outputs, correct mistakes, and monitor performance over time.

Don’t build an AI strategy. Build a data strategy.

Without clean, connected data, AI is just an expensive experiment. The real winners are the teams using AI strategically to solve defined business problems, and they all started by getting their data house in order first.

As we’ve explored in our research on data quality foundations, organizations succeeding with data quality share four key characteristics:

1. They have a shared definition of data quality across departments
2. They use designated platforms with automatic integration
3. They’ve established processes for continuous data improvement
4. They leverage custom solutions tailored to their unique needs

These aren’t companies with bigger budgets or more resources—they’re simply organizations where leadership treats data quality as a strategic priority.

Make sure your data foundation is ready before you invest in AI. The teams that get this right will use AI as a competitive advantage. The ones that don’t will just be automating their existing problems at a much higher cost.

Want to see how top-performing RevOps teams are approaching AI and data quality? Download the 2025 State of RevOps Survey to discover the data quality foundations that make AI initiatives successful.