The Only Metric That Tells You If AI Is Actually Working
In this issue:
ARR per FTE is the one metric that reveals if your AI investments are actually working
Why your next RevOps hire should be a GTM engineer, not another ops analyst
How to 10x your RevOps output with AI, without losing your soul
1. ARR per FTE is the one metric that reveals if your AI investments are actually working
RevOps Impact, Episode: The RevOps Review w/ Brandon Smith, Director of Revenue Operations & AI Strategy at QuotaPath (Oct. 24, 2025)
TLDR
QuotaPath measures AI success with ARR per FTE (full-time employee), a metric that shows if you're making your team more efficient, not just bigger
One tool, Momentum.io, saved their AEs 30 hours per month on Salesforce data entry by automatically capturing call insights and updating opportunity fields
The key: Start with one person being curious about AI, then create a pull model where others see results and want in
While AI-native companies like Harvey are hitting $100 million in revenue in 18 months with tiny teams, traditional SaaS companies are still trying to figure out if their AI investments actually work. Brandon Smith, Director of RevOps and AI Strategy at QuotaPath finds the answer by tracking ARR per full-time employee.
Why ARR per FTE beats vanity metrics
Most companies measure AI success by counting how many tools they've deployed or how many employees use ChatGPT. But these metrics miss the point. AI should make your existing team more powerful, helping average performers become great and great performers become extraordinary.
At QuotaPath, they watch one number closely: total annual recurring revenue divided by headcount. As they layer AI into different parts of the business, this metric reveals whether they're actually getting more efficient or just spinning their wheels. The goal is to generate more revenue with the same number of people (or the same revenue with fewer people).
The 30-hour time savings that proves the model works
QuotaPath's AEs typically juggle 15-30 opportunities at once. After each call, they'd spend hours reviewing Gong transcripts, extracting insights with ChatGPT, and manually updating Salesforce fields. Why the obsession with complete data? Because the handoff from AEs to Customer Success Managers needs to be seamless. If CSMs don't have the full picture, customer onboarding suffers.
The problem: AEs were spending up to 30 hours per month just filling out Salesforce. That's nearly a full week of work that wasn't spent selling. QuotaPath brought in Momentum.io, a tool that uses AI agents to capture conversations from emails, calls, and Zoom meetings, digest the information, and automatically populate Salesforce fields. The result? Those 30 hours get redirected to actual selling.
Starting small: one curious person beats forced adoption
Here's what doesn't work: Mandating AI adoption from the top down. Brandon's approach at QuotaPath is different. AI adoption needs a "pull model" where people opt in because they see value. It starts with one person – often in RevOps – getting curious, experimenting, and finding something that delivers clear benefit.
When Brandon found a tool that saved him hours per week, others noticed. They asked questions. They wanted access. That organic curiosity spreads faster than any companywide mandate.
The role of RevOps becomes facilitation: helping people explore AI tools that might solve their specific problems rather than forcing a one-size-fits-all solution.
The team-of-one challenge and the contractor solution
As a solo RevOps practitioner supporting sales ops, marketing ops, and CS ops, Brandon doesn't have the luxury of waiting for perfect solutions. He brings in contractors for specific Salesforce implementations and tech stack management. This hybrid approach – one core person with domain expertise plus specialized contractors – lets small teams punch above their weight.
The AI component adds another dimension. Brandon isn't trying to be an AI expert in every tool. He's trying to be the facilitator who helps the organization adopt AI where it makes sense. That means staying curious, testing tools constantly, and learning from the broader RevOps community on LinkedIn.
2. Why your next RevOps hire should be a GTM engineer, not another ops analyst
The B2B Revenue Executive Experience, Episode: Inside 1Password: How a RevOps VP Uses AI to Forecast, Scale, and Align GTM (Oct. 21, 2025)
TLDR
Enrichment isn't just about firmographics anymore; deal enrichment that captures why you're winning and losing is the real gold mine
AI lets you extract insights from unstructured data at scale, turning every customer conversation into actionable intelligence your entire GTM team can use
The emerging GTM Engineer role combines technical skills with GTM understanding to build the data infrastructure that makes AI actually work
Navin Persaud, VP of Revenue Operations at 1Password, leads a team of 30+ RevOps professionals supporting a GTM organization of more than 450 people. When he describes AI as "simultaneously the scariest and most freeing tech in ops today," he's speaking from experience at companies ranging from IBM to high-growth SaaS startups.
The enrichment gold mine hiding in your CRM
When most people hear "enrichment," they think of vendors that provide company size, industry, tech stack, and other firmographics. That data is table stakes now. The real opportunity? Deal enrichment. Why are you actually winning? Why are you losing? What questions did your best reps ask that your struggling reps didn't?
Your sellers often forget to update CRM fields with the details that matter. But those details live somewhere: in call transcripts, email threads, Slack messages. AI can now extract that unstructured data at scale and surface patterns you'd never spot manually. For example, you might discover that deals where you discuss a specific competitor close at 3x the rate. Or that opportunities mentioning "budget approved" in the first call are 5x more likely to close within 30 days.
This intelligence doesn't just help RevOps. It informs product roadmaps (here's why we're losing to competitors), shapes marketing narratives (here's the language that resonates), and guides sales coaching (here's what top performers do differently). The entire GTM motion gets smarter.
The GTM engineer: bridging technical and commercial worlds
A new role is emerging in RevOps organizations: the GTM Engineer. This person isn't a traditional software engineer who doesn't understand sales, and they're not a sales ops person who can't write code.
They sit in-between: technical enough to build data pipelines and integrate systems, commercial-minded enough to understand pipeline stages, sales methodologies, and what actually drives revenue.
The GTM Engineer builds the foundational data layer that makes AI effective. Because here's the truth: AI is only as good as the data you feed it. If your CRM is messy, your enrichment incomplete, and your systems disconnected, adding AI tools just amplifies the chaos. The GTM Engineer ensures you have clean, connected data before you layer on intelligence.
The trust and accuracy foundation
Navin quotes Spider-Man (yes, really): "With great power comes great responsibility." AI gives you incredible power to analyze data and make recommendations at scale. But you're responsible for ensuring accuracy and building trust. That means starting with small, low-risk use cases where you can validate AI outputs before they impact business decisions.
One in four Chief Revenue Officers are now responsible for integrating AI into their revenue engines. Yet only one in ten CEOs think their CRO is AI-savvy. That gap creates opportunity for RevOps leaders who can bridge technical capabilities with commercial understanding. The GTM Engineer role is one way forward.
3. How to 10x your RevOps output with AI, without losing your soul
RevOps Champions, Episode: The Human-AI Balance: 10X Your Output Without Losing Yourself with Bella Cowdin, Senior Consultant at Denamico (Oct. 1, 2025)
TLDR
AI agents can help your best people 10x their output by handling repeatable tasks that currently take 30 minutes in less than a second
The key is giving people the right tools to train and specify agents for their specific workflows, not generic automation
Marketing moves faster and gets more personalized as humans relinquish responsibility for repeatable tasks to AI assistants
RevOps leaders face a paradox: You need to move faster and scale more efficiently, but you also need to maintain quality, personalization, and the human touch that builds relationships. AI promises to help, but how do you actually get there?
The 10x output promise
At HubSpot's recent Inbound conference, one message stood out: AI agents aren't here to replace your team. They're here to help your best people multiply their impact.
Here's how it breaks down:
Marketing agents help marketing ops people do their job better
Sales assistants help salespeople sell faster
Service agents help customer service teams build better relationships
The emphasis isn't on replacing humans. It's on empowering your top performers to do more of what they do best. Think about it: A task that takes you 30 minutes to navigate through tools can now be done by an AI assistant in less than a second.
The new reality of personalization
Here's where things get counterintuitive. You might think AI makes things less personal. Actually, it's the opposite.
For 19 years, HubSpot built their entire business around inbound marketing and SEO. At Inbound 2024, they essentially announced that playbook is broken. SEO doesn't work the way it used to. The world has shifted to an AI-driven agentic web.
But this doesn't mean personalization dies. It means personalization becomes possible at scale. When AI handles the repetitive work – like personalizing a quote or customizing an email campaign – your team can focus on the strategic thinking and relationship building that truly requires human judgment.
Relinquishing the right responsibilities
The key word here is "relinquishing." Humans are giving up responsibility for specific types of tasks to AI assistants:
Repeatable tasks with clear patterns
Personalization that would take humans 30+ minutes
Data analysis and quote generation
Campaign customization based on known parameters
What humans keep: Strategic decisions, relationship building, judgment calls on complex situations, and training the AI to understand what good looks like for your specific business.
Training agents for your workflows
Generic AI tools won't cut it. The competitive advantage comes from training agents specifically for your team's workflows and processes.
This means your best people need time to:
Identify which tasks can be handed off
Define what "good" looks like for those tasks
Train agents on your specific requirements
Iterate as they see what works and what doesn't
Think of it like onboarding a new team member, except this team member can work 24/7 and handle an infinite number of repetitive tasks simultaneously.
The bottom line
The future of RevOps isn't about replacing your team with AI. It's about empowering your best people to 10x their output by giving them AI agents trained specifically for your workflows.
Disclaimer
The RevOps Leader summarizes and comments on publicly available podcasts for educational and informational purposes only. It is not legal, financial, or investment advice; please consult qualified professionals before acting. We attribute brands and podcast titles only to identify the source; such nominative use is consistent with trademark fair-use principles. Limited quotations and references are used for commentary and news reporting under U.S. fair-use doctrine.
