Honey, AI Shrunk the RevOps Team
The 100:1 rep-to-RevOps ratio is coming. Get ready.
Last June, I wrote about the apparent reluctance of RevOps to embrace AI, citing ICONIQ’s 2025 State of GTM Report which showed RevOps teams lagging behind their GTM peers in AI adoption. After spending more words than I should have1 hypothesizing about why, I said that needed to change:
The last 6 months of AI development have put all the ingredients in place for a new phase of RevOps. Models can reason and analyze. They can reach into systems to get the data they need and take necessary actions. The technology is new and immature, but it’s here. Right now.
That was 10 months ago. AI, for most people, still meant chat (mostly ChatGPT). At best, you could connect a primitive MCP or upload a CSV and get some basic analysis back out. Then you could… well, you could paste that analysis somewhere else.
In retrospect, it’s not shocking that RevOps was slower to adopt AI. AI could output useful text, but it couldn’t actually do very much. The state of the art was remembering a few things between chat sessions and trying not to hallucinate too often. Forget about handling long-running multi-step tasks that safely access your files or modify external systems.
But, I can give myself a little credit for prescience. The basic ingredients were in place back then.
Fast forward to January of this year when Claude Code broke containment from its early life as a pure developer tool. AI could finally do useful tasks, access things and get better over time. Those shifts changed the relationship between RevOps and AI. I don’t have ICONIQ to back me up this time, but I’ve collected enough anecdata to be confident that RevOps is all-in.
And now it’s time for RevOps leaders to reckon with how their worlds have to change.
A different kind of 10x
I kicked off that RevOps post from June with a story about the RevOps org I ran at a publicly-traded company. We were a team of 50 supporting 600 sellers. This was 2019. AI agents were science fiction.2
Even then, that ratio of reps to ops felt bloated. It seemed obvious to me that we ought to be able to do the same work with a smaller team, but we couldn’t pull it off. Too much questionable data. Too many siloed systems. Too many ad-hoc requests. Too much “fuzzy” process that was 90% automation but fell apart without human engagement on the last 10%. We just had to have humans doing things.
Turns out I shouldn’t have felt bad about our ratio. Adam Schoenfeld two years ago published the broadest data I’ve seen on rep-to-RevOps ratios as part of his work at PeerSignal. Here’s what he found:
In aggregate we see a 12:1 overall ratio of Sales Reps (AE+SDR) to Rev Ops. That's 7,700 Rev Ops people supporting 91,000 sellers in our sample list of companies.
For those of you who don’t want to do the math3, 600:50 is precisely 12:1. We were right in line with the industry standard. And this was after PE-driven layoffs.
That 12:1 ratio was true for me in 2019 and true for Adam in 2023, but I think it’s due for a major revision.
GTM processes are still as ambiguous and exception-filled—as fuzzy—as ever. The difference is that AI agents mean this operational fuzziness no longer prevents automation. The proportion of work done by RevOps that can be done by AI-driven automation isn’t 100%, but it’s uncomfortably close.
Sales roles, on the other hand, have a much lower AI automation ceiling. I say this as someone who wrote a viral post called the 10x Sales Rep about how to get a mid-market rep to a $10M quota using AI.
The thesis of that post wasn’t wrong, per se. Sales reps can get radically more efficient than they are today, especially because most aren’t very efficient. However, humans still need to talk to humans and be in physical proximity to other humans. So while sales was faster to adopt AI than RevOps in the “chat-only” era, the proportion of total sales tasks that can ultimately be automated by AI is lower than for RevOps.
Put these different automation ceilings together and you get a recipe for radically different rep-to-ops ratios in the future.
Let’s say 90% of the work my 50-person team did in 2019 can be done by AI. That team shrinks to 5. Let’s say 50% of the work sellers do can be done by AI. That 600 person team becomes 300. That’s a 60:1 ratio.
That was for a mid-market sale. It wouldn’t be surprising to see 100:1 ratios for enterprise sales motions that retain a high “human factor”.4
A 100:1 RevOps org requires a different kind of team and a different set of skills.
How the work changes
Like all of us who are wrestling with how to harness agents, lots of RevOps practitioners will have to change their identity from being responsible for doing the work to being accountable for the quality of the work.
To make this concrete, in 100:1 RevOps orgs, agents will do the work of pulling together the data and preparing the GTM board slides—a human analyst won’t spend two weeks doing that. So far, so good. That stuff isn’t fun, anyway. But guess what? If the data’s inaccurate or those board slides are wrong, it’s your ass on the line, not Claude’s.
This massively changes the work we consider to constitute “RevOps”. I think the shift looks something like this:
This shift comes down to doing four jobs well: keep the context, govern access, orchestrate the agent fleet, and make yes possible.
Let’s look at each in turn.
Keep the context
RevOps has always owned the source of truth for data, process definitions, policies and—in concert with Sales Enablement—skills training for the GTM org. In AI agent parlance, that’s context. It’s always been necessary to maintain these things, but it’s often been possible to get by with it being fragmented and poorly documented because this information can be (lossily) transmitted from one person to another.
Agents need thorough, consistent, written context to do their jobs well. In a world with multitudes of GTM agents (both inside and outside of RevOps), someone needs to be sure they’re all singing from the same hymnbook. The job of maintaining, distributing and synchronizing context will naturally fall on RevOps.
Govern access
RevOps has long played a role in data governance, especially as it relates to CRM. The role RevOps plays with information security policies around authentication and authorization has been more limited. They rarely make the policies themselves—instead they enforce company-wide InfoSec rules in the GTM systems they control.
Those fundamentals probably don’t change much. However, the web of entities needing access to systems so they can read and write data will only get broader. What about the sales rep agent that needs to update Salesforce? Does it have the same privileges as a rep? The 6 agents that read and summarize Gong transcripts for different use cases? Where can they send that data? How about the finance data that goes into those board slides?
RevOps will have to partner more closely with the CISO and InfoSec teams to ensure an increasingly complex and interconnected set of agents with evolving access needs don’t rip gaping holes (or open silent back doors) in a company’s security posture.
Orchestrate the agent fleet
This is the most uncertain of all the areas. We have some principles of accountability and we have large human RevOps teams (and their management structures) as precedent, but we just don’t know how this area will evolve.
Maybe “bring your own agent(s)” (BYOA) will win out as the prevailing model. Or perhaps we’ll all buy pre-built agents from different companies. While I tend to favor BYOA, I’m sure the future will be at least somewhat hybrid.
Right now, there’s no clear technical winner in this orchestration problem.5 There are no standards and there are a lot of complex problems to solve. Nevertheless, demand is such that the fleet of agents will grow faster than the tools to manage them.
My guess is an agent orchestration approach will emerge first in software development (much like Claude Code did). RevOps must make it their mission to keep up with the state of the art and adapt quickly when a solution emerges.
Make yes possible
When I was running RevOps, I was frustrated at how often we had to say “no” to other parts of the business.
“No, we can’t change this process because it’ll break that process.”
“No, we can’t automate that because this system can’t talk to that system.”
“No, we can’t run that experiment because we don’t have that data.”
The root cause, I felt, was that our operations weren’t good enough. Years of underinvestment in systems and processes had left us in a place where every change was dangerous and every experiment might have unintended consequences.
I didn’t want to live like that. I wanted us to live in a world where we could say yes to more things. I wanted to adapt to change, run experiments and grow. To do that, we had to get better at our core operations mission. If we did that, we could make yes possible. “Make yes possible” became a mantra.
We’re in a time where experimentation is necessary for survival in GTM. A 100:1 RevOps org that successfully nails context, access and can orchestrate fleets of agents should be the ultimate yes machine—driving the cost of experimentation to near 0.
Wrapping up
The symbiosis of RevOps and agents is well underway. The logical consequence will be tiny RevOps orgs that support large sales teams at ratios that were simply impossible before. 100:1 rep-to-RevOps orgs are well within reason.
To get there, most RevOps professionals are going to have to learn new skills that operate at a different level of abstraction.
It’s not enough to know how to pull a report and build a chart. Can you enable an agent with everything it needs—from access to context—to pull the report and build the chart when you’re not around? Can you build an agent that interprets the report and automatically fixes the problem? Can you build an agent that spots and corrects potential problems before they happen so you no longer need the report and the chart?
This won’t be an easy transition. There will be fewer roles in RevOps with different responsibilities. I hate to say it, but not every operator will make this transition successfully. The ones that do will be in very high demand. I’ll share what I think this means for RevOps careers in a future post.
I have 99 problems and brevity is not one. Neither is too-timely pop culture references.
You know, like global pandemics.
My guess is that most of you already did the math. But then again, maybe AI is giving us all cognitive atrophy.
When the money gets big enough, flesh must be pressed, dinners must be had, golf must be played. I don’t think AI will change this.
Though there are some crazy attempts.