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As artificial intelligence reshapes the business landscape, the role of revenue operations (RevOps) is undergoing a profound transformation. Companies navigating the shift to AI-driven sales and customer engagement must rethink their go-to-market (GTM) strategies, ensuring they remain agile, data-driven, and aligned with the realities of an AI-powered economy.
To explore this evolution, we spoke with Roshin Unnikrishnan, Senior Director of Growth and Revenue Operations at Cisco. A recognized leader in RevOps, GTM strategy, and management consulting, Unnikrishnan has spent over a decade driving growth and operational excellence across North America, Europe, and Asia. His leadership has been pivotal in Cisco’s transition to a recurring revenue model, the redesign of a 17,000+ member sales coverage strategy, and the integration of major acquisitions—including Splunk’s $28 billion merger—into Cisco’s GTM approach. A former Engagement Manager at McKinsey & Company, he also contributed to groundbreaking research on automation and the future of work. Beyond his corporate influence, he is a member of the Forbes Business Council, where he shares insights on revenue growth and operational strategy.
"AI isn’t just an efficiency tool—it’s redefining how companies build revenue engines," says Unnikrishnan. “The organizations that adapt fastest will be the ones that treat RevOps as a strategic growth function, not just a support role.”
Traditionally, RevOps has been viewed as a back-office function, responsible for sales forecasting, analytics, and operational streamlining. However, with AI’s growing influence on GTM strategies, the function is becoming a revenue driver in its own right. AI-powered personalization has transformed customer engagement, allowing companies to tailor interactions with unprecedented precision. According to McKinsey, businesses leveraging AI-driven GTM strategies see a 10-20% increase in sales productivity, as machine learning models analyze customer behaviors and deliver real-time, context-aware engagement.
"AI is giving sales teams superpowers," Unnikrishnan explains. "We’re moving from a world where sales cycles were driven by intuition and experience to one where every decision is backed by predictive intelligence. This isn’t just about streamlining processes—it’s about completely redefining how we approach customers, pricing, and value delivery."
At the same time, AI’s role in revenue operations extends beyond customer engagement—it is reshaping sales strategy itself. Unnikrishnan explains that companies adopting AI-driven GTM models can anticipate customer needs, optimize pricing strategies, and automate lead prioritization with higher accuracy than ever before. A recent study by Harvard Business Review highlights how AI-driven sales enablement tools have helped businesses improve conversion rates by up to 50%, reinforcing the idea that organizations failing to integrate AI into their GTM motion risk losing ground to more adaptive competitors.
With AI transforming how revenue is generated, organizations must also rethink how their teams are structured. Unnikrishnan has been instrumental in leading Cisco’s transition to a data-driven GTM approach, particularly through the redesign of its sales coverage model. His work optimizing a 17,000+ member salesforce illustrates the importance of aligning revenue teams with AI-driven insights, ensuring that sellers, marketers, and customer success teams are working from a unified data foundation.
"In the AI era, GTM strategy can’t be static," he notes. "Sales teams need to be structured around dynamic insights, not rigid hierarchies. The future belongs to organizations that break down silos and ensure that marketing, sales, and customer success are all working from the same AI-driven playbook."
Another critical challenge lies in integrating acquisitions into an AI-driven GTM strategy. Cisco’s $28 billion acquisition of Splunk is a prime example of how major mergers require a strategic approach to revenue alignment. "When you integrate a company like Splunk, you're not just merging products—you’re merging data systems, sales methodologies, and entire revenue models," Unnikrishnan explains. "Success in post-merger integration depends on ensuring that all teams operate with a unified GTM strategy that leverages AI and automation for efficiency and scale."
This shift toward data-driven GTM models has been a recurring theme in Unnikrishnan’s insights, as highlighted in his feature on Benzinga. His analysis underscores how AI is not just automating existing sales processes but actively reshaping the very foundation of B2B revenue generation. Companies that leverage AI to drive revenue intelligence, rather than just efficiency, will be best positioned to capitalize on the evolving marketplace.
Companies would also need to consider operational changes dictated by AI, creating a whole new level of issues to overcome in data protection and governance. With AI tools exerting influence on decision-making processes in revenue intelligence systems, Unnikrishnan cautions organizations to keep their data hygiene integral to make revenue intelligence systems trustworthy. "AI is only as good as the data it learns from," he told. "If companies don’t prioritize data integrity and governance, they risk making critical revenue decisions based on flawed or biased information."
Looking ahead, Unnikrishnan believes that the most successful revenue organizations will be those that embrace RevOps as a proactive growth function rather than a reactive support role. Unnikrishnan who is also shaping global business excellence as a judge for the Globee Awards for Business, says "The companies that win in the AI era will be the ones that treat RevOps as a revenue-generating powerhouse, not just an operational cost center. This means investing in AI-driven intelligence, ensuring that sales and marketing are fully integrated, and continuously optimizing the GTM motion based on real-time data in an increasingly intelligent world.”
