Traditionally, RevOps teams have operated with what Atassi describes as a "very static approach" to account data management. Teams would typically revamp their account data annually at most, often working with stale information that failed to account for mergers, acquisitions, company growth, or changing market positions.
"We are shifting from a kind of rigid segmentation where we have really fixed and rigid rules on how to assign the different accounts into a more kind of dynamic approach," Atassi explained during the session.
This shift represents a fundamental change in how RevOps teams think about data management and analytics. Instead of annual data refreshes, teams are moving toward continuous, AI-powered updates that reflect real-time market changes.
One of the most significant challenges RevOps teams face is the limitation of traditional data providers. As Krohn pointed out, "The world you should imagine that you're in is a world in which, where if you can do it in your CRM or on the internet, then it can now be done at scale."
The problem with traditional data providers is that they sell the same data to everyone, which doesn't necessarily reflect how individual companies actually sell. This creates a disconnect between the data in your CRM and your actual go-to-market strategy.
"You should not depend on how other people see the market. You should take the way you see the market and bring it into your CRM and then into your planning process," Krohn emphasized.
This approach aligns with broader RevOps thought leadership around customizing systems to match business requirements rather than forcing business processes to fit generic tools.
During the session, Krohn demonstrated how AI can be used to build custom corporate hierarchies that reflect actual buying centers rather than just legal entities. This is particularly crucial for enterprise sales where understanding the decision-making structure within large organizations can make or break deals.
"What's unique about using AI for this is that we can start to think about whether something is a buying center or not, which in our case requires the presence of a sales organization or a rev ops organization," Krohn explained.
This capability addresses a common pain point discussed in recent territory design conversations – ensuring that account assignments reflect real business relationships rather than arbitrary legal structures.
Atssi shared insights into Pigment's approach to territory planning, which exemplifies the shift toward more dynamic, AI-enhanced processes. Their methodology involves:
"We define a scoring based on as many data points as possible refreshed as frequently as possible," Atssi explained. This scoring system analyzes recent wins, territory performance, and various account characteristics to create a more nuanced understanding of account potential.
Rather than rigid geographic or alphabetical assignments, Pigment uses AI to optimize territory distribution based on account scoring, rep capacity, and market dynamics. This approach helps address the territory equity challenges that many RevOps teams struggle with.
"We're switching more and more to a kind of ongoing monitoring of how we are covering our TAM," Atssi noted. This represents a significant departure from the traditional annual territory planning cycle.
This dynamic approach to territory management reflects broader trends in AI and automation in territory design, where RevOps teams are moving beyond static spreadsheet-based planning.
Despite the potential of AI in planning processes, the webinar revealed that adoption is still in early stages. When polled about AI usage in planning, most attendees indicated they use AI "sometimes" or "never" in their planning processes.
This hesitation is understandable given the complexity of planning processes and the need for accuracy. As discussed in recent RevOps podcast episodes, many teams are still navigating the balance between AI capabilities and human oversight.
The discussion revealed important distinctions in how AI should be applied across different market segments:
For enterprise accounts, AI excels at understanding complex organizational structures and relationships. "It's much more into understanding this architecture of a company, the different entities, who is deciding for what," Atssi explained.
This includes analyzing 10-K filings, understanding subsidiary relationships, and mapping decision-making hierarchies – tasks that would be impossible to do manually at scale.
For mid-market accounts, AI is more valuable for identifying signals and insights. "It's much more a world of insights," Atssi noted, focusing on growth indicators, job postings, team expansions, and other signals that indicate buying intent or capacity.
This segmented approach aligns with sales forecasting best practices that emphasize different methodologies for different account types.
One of the most important topics addressed was the concern about AI accuracy and hallucinations. Krohn emphasized the difference between consumer-scale AI usage and enterprise-scale applications:
"There's a huge difference between the use of AI at consumer scale and then the use of AI at enterprise scale. Once you reach scale, the magnitude of the errors also increase."
To address this, enterprise AI applications require:
This approach to AI governance reflects broader RevOps systems management principles around balancing automation with control.
Despite AI's capabilities, both speakers emphasized that human context and oversight remain essential. "You have to be very intentional when you are using AI," Atssi cautioned.
This is particularly important in:
The most successful implementations combine AI's scale and speed with human judgment and relationship understanding, similar to approaches discussed in customer onboarding best practices.
A key theme throughout the discussion was the concept of "CRM confidence" – ensuring that sales teams trust and actively use the data in their CRM systems. AI can contribute to this confidence by:
This focus on CRM confidence addresses common challenges discussed in recent RevOps content about whether bad data requires system replacement or better processes.
For RevOps teams looking to implement AI in their account data and planning processes, the speakers offered several practical recommendations:
Begin with data enrichment and account scoring before moving to more complex applications like territory optimization or quota setting.
Don't rely on generic AI tools. Ensure your AI applications understand your specific market, sales process, and business model.
Implement approval workflows for high-impact decisions and maintain the ability to override AI recommendations when business context requires it.
"If it only improves every month, every quarter, you're training in the right direction," Atssi advised. Focus on continuous improvement rather than perfection.
Looking ahead, the integration of AI into account data and planning processes represents a fundamental shift in how RevOps teams operate. Rather than being reactive data managers, RevOps professionals can become strategic advisors who leverage AI to provide deeper insights and more accurate predictions.
This evolution aligns with broader trends discussed in RevOps career development resources about the increasing strategic importance of the RevOps function.
The transformation of account data and planning through AI represents one of the most significant opportunities for RevOps teams to drive strategic value. By thoughtfully implementing AI while maintaining human oversight and business context, RevOps leaders can build the foundation for more accurate forecasting, fairer territories, and ultimately, more predictable revenue growth.
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