AI and Revenue Operations
Revenue Operations (RevOps) leaders face a choice: adapt to the AI platform shift now, or get replaced. Most are stuck asking the wrong question (”What tool should we add?") when they should be asking, "What can we automate next?"
This isn't an incremental improvement. This is a fundamental platform shift that will redefine what RevOps teams do and how they deliver value.
AI as a Platform Shift
According to Benedict Evans in his AI eats the world talk, every major platform shift follows the same progression: Absorb → Innovate → Disrupt
Absorb: Make existing workflows more efficient
Innovate: Build new products and processes
Disrupt: Redefine the problem entirely
It’s unclear what AI’s ceiling is for its capabilities. Some leaders believe AGI-level leaps are close. Others think multiple breakthroughs remain necessary.
AI will likely create multiple waves of opportunity for RevOps, including:
Automation of repetitive data tasks (happening now)
Agentic workflows that handle complex, multi-step processes (emerging)
Real-time multimodal intelligence for decision support (near future)
This requires RevOps to build for optionality.
The RevOps Mandate: Architect for Optionality
I believe the winning Revenue Operations teams will treat AI as a continuous capability rather than a one-time project. They will design modular, replaceable AI components that deliver value today while positioning themselves to exploit step-change improvements in accuracy and automation (e.g., master Wave 1 and position to capture value in Waves 2 and 3). The teams that architect for flexibility will compound gains. The teams that hard-code rigid AI solutions risk having to rebuild from scratch when capabilities advance.
Start Where the Value Concentrates
AI’s first wins come in high-volume knowledge workflows. Here's the mental model that changed how I think about AI in RevOps: AI gives you infinite interns—near-zero marginal cost for repetitive cognitive labor. Any task you'd delegate to a junior analyst can now be automated and scaled infinitely.
Concrete examples include:
Account research at scale for every deal, not just enterprise
Data validation running continuously, not quarterly cleanups
Performance reports generated automatically when metrics shift
Meeting notes synthesized and actioned
This isn't incremental. This is RevOps delivering insights that were previously impossible—not because you lacked data, but because analysis was too expensive to run at scale.
When the cost of something drops dramatically, demand often expands to fill capacity (see Jevons paradox). View AI as an accelerant. Your goal isn't to do the same work with fewer people. Your goal is to deliver 10× more value with the same team. When a task costs 95% less to perform, what becomes possible? Plan for expansion, not reduction.
Concrete outcomes include:
CSMs handle 2–3× the account load with higher quality
RevOps produces 10× deeper/vast insights
Marketing ships more campaigns and variants
AEs prospect more, but with far more precision
The Human-in-the-Loop Requirement
AI makes mistakes. Accept this. The question isn't whether errors occur—it's which errors matter and how you catch them. RevOps teams must build guardrails, assume imperfect outputs, and test continuously.
Potential design principles include
Validation layers for data accuracy
Confidence scoring with clear thresholds for human review
Strategic decisions always have humans in the loop
Logging and observability for automated and agentic actions
The teams that get this right will move faster than teams that expect perfection.
When Automation Becomes Invisible
Nobody thinks about elevator attendants. Nobody thinks about telephone switchboard operators. The work shifted to machines and became invisible. The same will happen in RevOps. Much of what RevOps teams did in the past will be replaced by smarter automation and agentic actions, and those tasks will become "just the way revenue operations works". Winning RevOps teams will lead this transition rather than react to it.
Final Thoughts
The natural tendency is to treat AI as another tool in the stack, like adding another SaaS app. The platform shift to AI isn't about adding new tools to the RevOps tech stack. It's about fundamentally expanding the value RevOps delivers to the business. The teams that understand this will build for optionality, embrace the multiple waves, design modular systems, and plan for Jevons Paradox. The teams that treat AI as just another tool in the stack will fall behind.
Have thoughts on this topic? I'd love to hear from you! I'm @RickLindquist on X.