Sales Enablement
What is sales ops? The 3 profiles you need to know (tech, data, business)
Gaultier Beauchesne
CSO & Co-founder @Eagr
🎧 From the Eagr to sell podcast episode with Lyes Boukeroui. Watch on YouTube.
Key takeaways
Sales ops builds and runs the sales machine: CRM, process, data, automation, and the tools reps use every day.
There are three profiles: tech (tools, automation), data/process (analysis, forecasting), and business (field, enablement).
One person who holds all three is rare. Mature orgs build a team that combines them.
The market is real. By 2025, 75% of high-growth companies will run a RevOps model (Gartner), and aligned orgs generate 36% more revenue (Forrester).
AI mostly threatens the data profile. The tech profile stays protected by the complexity of data architecture.
The role landed late in a lot of markets, around 2019 to 2020.
It showed up first inside big, mature companies like Criteo, through the CRM. Now the title is everywhere.
Behind the same job title, the day-to-day looks wildly different from one company to the next. Hire the wrong profile for your problem and it costs you: a tools expert when what you needed was someone who reads the field.
Lyes Boukeroui, ambassador of a 3,000-plus community of ops and the first RevOps hire at Uptoo, sorts the field into three profiles.
Worth knowing them, whether you're hiring, structuring a team, or positioning yourself.
What is sales ops, exactly
Sales ops (sales operations) is the function that builds and runs the sales machine: CRM, sales process, data, automation, and the tooling reps work with.
In a lot of small and mid-sized companies, the first lever is simple. You put a CRM in place so you can track performance at all.
The tooling is only part of the story. The function has turned strategic.
According to Gartner, by 2025, 75% of the highest-growth companies will have deployed a RevOps model.
And orgs that align teams, process, and tools generate 36% more revenue, per a Forrester study (Nancy Maluso).
What a good sales ops looks like shifts with what the company is trying to solve. That's where the three profiles come in.
The 3 sales ops profiles
There are three sales ops archetypes, split by where their strength sits: the tech, the data, the business.
Finding all three in one person is rare. Most mature orgs build a team that mixes them.
Tech · Strengths : Tools, automation, complex workflows, CRM integrations · Limits : Often far from the field · Closeness to the business : Low · Effect of AI : A productivity tool; the role stays protected by data complexity
Data / Process · Strengths : Analysis, forecasting, incentives, recommendations · Limits : Weaker on pure engineering · Closeness to the business : Medium · Effect of AI : Most exposed: managers now pull their own analysis
Business · Strengths : Field, CRM fitted to the sales cycle, enablement, playbooks · Limits : Less equipped for heavy data work · Closeness to the business : High · Effect of AI : Barely exposed: the field and the human stay hard to automate
Lyes sums up the three in a single line:
"You've got sales ops with a tech veneer, very good at tools and automation but pretty far from the business. Others are more data and process: analytical, into forecasting and incentives, a middle ground between the tech and the business. And then there's the business sales ops, very close to the field and the sales manager, often on enablement and playbook work. Having all three in one person is rare."
Lyes Boukeroui, RevOps expert (ex-Uptoo)
The tech profile shines on tooling and automation.
Complex workflows, CRM integrations, wiring tools together. Its blind spot: it can sit far from the business.
It tends to see reps as people who ping it on Slack, without always grasping what they're up against.
The data/process profile is the analytical one. Forecasting, incentives, sharp analysis, real recommendations.
It understands the business without being the strongest on pure engineering. It sits between the tech and the field, a data partner to the sales teams.
The business profile lives closest to the field.
A CRM glued to the real sales cycle, hands-on work with the sales manager. Often enablement work: ramping reps, playbooks.
It's the profile closest to what wins or loses a deal.
Which profile to hire first by maturity
Your first hire depends on how structured you already are, not on some absolute ranking.
You're starting from zero, no clean CRM. Begin with a tech or CRM-oriented data profile.
As long as the data is dirty and there's no process, everything else falls over. Lay the foundations first.
You have a CRM but you're flying blind. Aim for a data/process profile.
Reliable forecasting, conversion rates by stage, where the pipe leaks. That's what makes your sales decisions factual.
You have the data and the visibility, but the team has plateaued. Hire a business profile leaning on enablement.
The lever isn't the tool anymore, it's ramping the team. You've got enough opportunities, you're closing badly.
That's exactly the diagnosis Lyes runs into on the job: someone asks him to automate a thing, and the real problem is a hole in discovery or closing.
Pumping a leaky pipe gets you nowhere. The pipe stays leaky, with even more drop-off.
Do you need a unicorn or a team?
The best sales ops depends on what the company is trying to solve, and finding all three in one person is rare.
In a small company, look for the most versatile person, with a dominant strength that matches your top priority.
In a mature org, build a team.
That's what Lyes did at Uptoo. Four people: one tech profile, one analytical, one on enablement, combined into a complete sales ops squad.
A spread of profiles is a real asset, as long as someone orchestrates it.
Which profile does AI threaten most?
AI hits all three profiles, very unevenly.
The data/process profile is the most exposed. More and more heads of sales now pull their own analysis through an AI assistant.
The old reflex, "let me ask sales ops for this month's conversions," is fading. Pure analysis work is now within the manager's reach.
The tech profile holds up better than you'd think. Wiring a CRM cleanly into another tool takes real knowledge of data architecture and data quality.
And in almost every company, the data is a mess. Handing that to the head of sales would just drag their job to where they have less impact.
AI becomes an accelerator here, not a replacement. Lyes has watched CRM integrations done too fast break data fields in production, with knock-on effects on BI and finance numbers.
The business profile is the least at risk. The field, the relationship with the sales manager, reading a sales cycle in detail, all stay hard to automate.
The lasting value is orchestrating commercial intelligence: turning business problems wrapped in a tech-and-data shell into decisions. AI on its own stops short of that step.
Common mistakes when you build a sales ops function
Five traps come up again and again.
Starting from the tech, not the need. Plenty of gorgeous ops projects never get used.
Built without the reps, out of step with how their week actually runs. Ask any sales ops: they'll name ten buried projects.
Hiring a profile that doesn't match the problem. A tools expert when you needed the field, or the other way around.
The profile matters as much as the raw skill.
Confusing symptom with cause. "We need more leads" often hides an 80% drop-off between the first and second meeting.
Ops answers the request, the real problem stays open. Diagnose before you act.
Letting AI redo everything with no clear role. Lyes has seen rushed integrations rack up heavy technical debt.
Everyone has their job. The head of sales has no business rebuilding the CRM.
Skipping change management. A great tool nobody adopts is worth nothing.
Observe, understand, act, evaluate, improve. AI nudges you to jump straight to "act," and that's where it breaks.
The guiding principle: start from the need, not the tech
Whatever the profile, the best sales ops starts from the team and from what's hard for them.
The tool comes after.
To equip the business profile on enablement, Eagr scores how well reps follow the playbook straight from their real calls, which helps replicate your best reps' performance across the whole team. See how Eagr works.
FAQ
What's the difference between sales ops and RevOps? Sales ops focuses on the sales machine. RevOps takes a wider view across the whole revenue cycle (marketing, sales, customer success). In many small and mid-sized companies, the two blur together in practice.
Which sales ops profile should I hire first? It depends on your maturity. No clean CRM: a tech or data profile to lay the foundations. Flying blind: a data/process profile. Team plateaued while the data is good: a business profile leaning on enablement.
Can one sales ops do everything? Rarely. People who combine tech, data, and business are hard to find. Mature orgs tend to build a team that mixes the three.
Will AI replace sales ops? It shifts where the value sits. Pure data analysis is now within reach for managers. Data architecture and orchestrating commercial intelligence stay full-blown jobs.
How much time does a sales ops spend on non-selling work? A lot. Per Gartner, sales or revenue operations teams spend 68% of their time on non-customer-facing work. Which is why automating what you can and framing the scope matters.
Does sales ops have a future? Yes. By 2025, 75% of high-growth companies will run a RevOps model per Gartner, and aligned orgs generate 36% more revenue per Forrester.
Sources
Gartner: 75% of the highest-growth companies on RevOps by 2025
Gartner: sales/revenue ops teams spend 68% of their time on non-customer-facing work
Forrester (Nancy Maluso): aligned organizations generate 36% more revenue
Eagr to sell podcast: episode with Lyes Boukeroui, RevOps expert (ex-Uptoo)
This article comes from an episode of the Eagr to sell podcast with Lyes Boukeroui. Watch the full episode on YouTube.
