Revenue operations (RevOps) is undergoing a seismic shift. At the heart of this transformation is the rise of agentic AI—autonomous and semi-autonomous AI agents poised to redefine how RevOps teams drive go-to-market (GTM) success.
As expectations from chief revenue officers and GTM executives escalate, RevOps leaders are increasingly looked upon to harness these new technologies, not just for incremental improvements, but as a foundation for strategic advantage.
Agentic AI is fundamentally different from traditional workflow automation or even the latest generative AI assistants. Rather than simply automating routine tasks, these AI agents are designed to perceive their operational environment, reason through complex analytical and administrative challenges, and act with increasing autonomy to achieve predefined business goals. The implications for RevOps are profound.
The Shift from Manual to Autonomous Analytics
The promise of agentic AI is not just automation, but acceleration. By 2028, Gartner predicts that 75% of RevOps tasks in workflow management, data stewardship, revenue analytics, and RevTech administration will be executed by AI agents. This rapid adoption will be essential for organizations seeking to stay competitive in a landscape where speed and accuracy of insight are critical differentiators.
Historically, RevOps teams have been bottlenecked by the labor-intensive nature of data preparation, report generation, and complex analytics. These manual processes often delay the delivery of insights, slowing organizational response to rapidly changing market conditions. While generative AI assistants have begun to alleviate some of this burden—enabling natural language querying and automated report writing—agentic AI takes this evolution several steps further.
RevOps’ Role in AI-Ready Data Governance and Stewardship
Analytic AI agents will autonomously collect, integrate, curate and analyze data from disparate sources such as CRMs, marketing automation platforms, and data warehouses. This means routine data wrangling and report generation can increasingly be handled with minimal human intervention, freeing analysts to focus on higher-value strategic work. More importantly, these agents will operationalize predictive models for forecasting, lead scoring, and churn analysis, surfacing key outputs directly into daily workflows. The result: faster, more reliable insights and higher quality customer data that empower GTM teams to make data-driven decisions with unprecedented agility.
With AI agents in play, RevOps is uniquely positioned to take ownership of unified, AI-ready commercial data. The strategic side of the RevOps leader’s role is expanding with a greater emphasis on the stewardship of this business-critical data. Data steward agents, for example, can handle the complexities of real-world customer data—merging duplicates, correcting errors, enriching records, and classifying unstructured data—all while learning and improving through feedback.
This shift enables RevOps to lead the data governance of generative and agentic AI across GTM functions, ensuring that data quality, privacy, and compliance standards are met. As AI agents become more adept at managing data and executing analytical tasks, RevOps teams can dedicate more capacity to strategic contributions, such as identifying operational issues invisible to the human eye and generating insights earlier than ever before.
AI Agents Assist in Revenue Technology Administration
Additionally, agentic AI is transforming how RevOps manages and administers the complex revenue technology stack that supports go-to-market efforts. Traditionally, overseeing a wide range of tools—from sales automation to marketing and support systems—has saddled RevOps with manual, time-consuming administrative work.
Now, vendors are introducing AI agents that automate these tasks, integrating data, managing workflows, and ensuring interoperability across platforms. This shift reduces inefficiencies and frees RevOps to focus on strategic priorities. As AI features rapidly enter the market, RevOps leaders must distinguish true capabilities from hype and ensure AI adoption aligns with business goals. Ultimately, agentic AI turns RevTech administration from a bottleneck into a catalyst for agility and innovation.
Building the Roadmap for AI-Driven RevOps
As organizations evaluate the integration of agentic AI into their RevOps functions, several best practices are emerging:
- Audit the revenue tech stack for data interoperability, ensuring integration across marketing, sales, and customer success platforms.
- Assess the AI-readiness of customer data in five areas (accuracy, consistency, completeness, timeliness, tagging) and pilot AI agents for data curation tasks.
- Define a long-term roadmap for an AI-first sales organization, linking the adoption of AI agents to evolving business needs.
- Identify high-impact use cases for AI-assisted analytics that align with GTM priorities and measurable business outcomes.
- Set priorities and goals for agents, ensure they are sufficiently trained, and oversee operational performance.
The rise of agentic AI marks a turning point for RevOps. No longer confined to the back office, RevOps teams are becoming the architects of GTM strategy, leveraging AI agents to deliver insights faster, govern data more effectively, and drive the business forward. For organizations willing to embrace this transformation, the rewards will be significant: greater agility, smarter decision making, and a sustained competitive edge in the market.
The future of RevOps is not just automated—it’s autonomous, strategic, and powered by AI agents ready to redefine what’s possible.
Steve Rietberg, Daniel O’Sullivan and Alan Lopez are experts in Gartner’s Sales, Customer Service and Marketing Practices, covering data & analytics, strategy and AI.
