A narrative about what’s next.
Before DevOps, the way software was delivered looked very different from today. Traditional IT organizations were monolithic, highly manual, and deeply constrained. Every server was provisioned by hand. Every environment was configured manually. Releases happened only a couple of times per year, and each deployment was an orchestrated event that carried significant risk. When a system needed to change, the effort required was large, slow, and labor-intensive. Learning cycles were long because feedback arrived only after substantial work had accumulated. Experimentation was rare because the cost of change was high.
Traditional IT wasn’t “broken.” It was simply built for an earlier era – one with simpler systems, fewer dependencies, and slower expectations. It continued to function, but at great cost and with clear limitations. As software became more distributed, more dynamic, and more central to product innovation, the old operating model could no longer keep pace with what modern engineering demanded.
The turning point arrived in 2009, when John Allspaw and Paul Hammond delivered their now-famous “10 Deploys a Day” presentation. At a time when most organizations were deploying software only a few times per year, the idea was almost unthinkable. But the presentation revealed something far more important than deployment speed: there was a fundamentally better way to work – one that Traditional IT could never achieve without reimagining the work itself.
In that moment, everything changed.
DevOps wasn’t a tool or a job title; it was a paradigm shift. It redefined how engineering work was done. Automation was not a side effect – it was a critical enabler of smaller, safer, more frequent changes. But the deeper transformation was methodological. DevOps reinvented the processes, roles, responsibilities, and feedback loops that made modern software development possible. It represented a complete rethinking of how teams should operate in a world defined by speed, complexity, and continuous change.
The impact of this shift has been enormous. Instead of releasing only a couple of times a year, organizations like Amazon release on average every ~11.7 seconds. DevOps didn’t just improve engineering – it unlocked trillions of dollars in enterprise value by enabling the pace of innovation that modern digital businesses depend upon.
Today, RevOps stands in the same place Traditional IT once did.
RevOps has played an essential role in modern go-to-market organizations. It brought alignment across Marketing, Sales, and Customer Success. It imposed structure on increasingly complex systems and processes. It ensured consistency, enabled reporting, and made organizations far more operationally disciplined.
But the operating model of RevOps is still fundamentally manual and labor-intensive. Nearly every workflow, scoring model, routing rule, field structure, data pipeline, and report must be built and maintained by hand. GTM insights must be assembled manually. Data must be cleaned manually. Errors must be diagnosed manually. And every strategic or tactical shift in the business ripples across dozens of interconnected systems that require human intervention at every step.
None of this means RevOps is failing. It simply means that a far better reality is now possible.
AI enables organizations to move beyond manual, monolithic approaches to GTM operations and toward continuous, adaptive, intelligent systems. The opportunity isn’t to make RevOps “more efficient.” It’s to rethink the work entirely – just as DevOps redefined how software delivery worked. Organizations can continue with the traditional RevOps model, just as many continued with Traditional IT. But they will be surpassed by those that adopt a model built for the complexity, speed, and expectations of the modern GTM environment.
This new model is “AIRops” - AI Revenue Operations.
AIRops is not RevOps with more automation. It is a reinvention of the work itself. Where RevOps teams do work, AIRops teams design and build the systems that do work. GTM teams then rely on these AI-enabled systems, agents, and products to achieve massive productivity and scale. Instead of manually constructing workflows, AIRops oversees workflow engines that generate and optimize them. Instead of assembling dashboards, AIRops maintains real-time intelligence layers that interpret data continuously. Instead of responding to GTM requests, AIRops builds AI agents and products that empower Marketing, Sales, and Customer Success to operate with unprecedented speed, clarity, and autonomy.
Traditional IT could never deliver the speed and scale required of modern engineering – DevOps does.
Similarly, RevOps cannot deliver the speed and scale required of modern go-to-market organizations – AIRops will.