RevOps, Clean Data Are Keys to Sales Success with AI

More than half of sales leaders identify poor data quality as the biggest barrier to successful artificial intelligence adoption, and revenue operations are an often-overlooked function ready to tackle this challenge, according to a new report from G2.

“While AI will streamline processes, provide insights, and unlock productivity gains, it’s not a magic pill that resurrects your broken go-to-market (GTM) motions,” says Eugene “Blue” Bowen, a research principal at G2.

“The key to utilizing AI technologies is understanding where in your organization you are experiencing bottlenecks or pain and where you are willing to take risks with innovative technology to combat these pain points,” Bowen continues.

So companies should see the technology as a teammate, not a substitute, just as sports leagues have added video replay to assist, not replace referees, Bowen says.

With 53 percent of sales teams saying data quality is their top AI challenge, clean data enables better AI coaching, forecasting, and pipeline prioritization, not fewer sales jobs, he concludes.

“Data cleanliness as is not a new problem, but it’s further heightened with agentic AI,” Bowen continues. “It’s important that you have processes in place for enrichment and cleansing the data. Data changes a lot and frequently, so ensuring you have the right processes in place to continuously enrich and update that data is paramount.”

Cleaner data means more effective use of AI. Sales reps can use AI-powered tools to scan company and contact information to find unique data relevant to their offerings and priorities in real time, Bowen says. “Spray and pray is no longer effective or efficient. True personalization is relevant. It should be based on data that is unique to [prospects’] pain points or company initiatives and that aligns with the value of your solution (not personal details irrelevant to your offering).”

The report also found that the most common AI use cases that support (not replace) sales reps are these:

• sales development tasks (44 percent);
• personalization (43 percent); and
• research and planning (42 percent).

RevOps is now the strategic lever for GTM efficiency, according to Bowen. “The old playbook is broken. Hiring doesn’t equate to more revenue growth. Just like AI, it’s all about the data and processes, which is why RevOps will be the force multiplier and key pillar for success in GTM in the coming years.”

Bowen recommends that businesses, particularly those in the mid-market and enterprise segments, increase their investment in RevOps teams by 20 percent.

Such an investment will ensure that systems and processes, from the customers’ first touch point to the renewal and beyond, are tracked, analyzed, and optimized, according to Bowen.

“With more investment into RevOps, organizations can begin to treat their ideal customer profiles and territory planning as a dynamic and agile guide, shifting resources and focus as it evolves in real time rather than the current state of a ‘set it and forget it’,” Bowen says. “RevOps members have an exponential impact on revenue, as compared to [a sales development rep]. RevOps will develop the systems and processes that accelerate an efficient GTM motion from marketing to customer success.”

Though tech systems are important to the success of RevOps, many companies need RevOps investment in personnel according to Bowen. “Most RevOps departments are understaffed. Headcount in RevOps should be an investment area, as they are one of the few pillars that an organization has. They have their eyes on different departments, so they enable people to be more proactive in their work.”

Bowen further recommends that companies look to more granular target marketing. Look at the top customers and identify their commonalities, but be unique. “The fact that they are all in the technology industry is not a differentiator. Perhaps they all have a freemium offering, and you sell a product analytics platform that can help them identify when freemium customers are more likely to convert to paid.”