How the fastest growing SaaS uses AI
(11 use cases in their GTM & outbound motion)
Read time: 6 min
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This week, I listened to an episode of the Revenue Leadership Podcast hosted by Kyle Norton.
The guest? Nate Follen, Head of Business Systems Ops at Ramp, breaking down how they use AI for their GTM team.
First, who Is Ramp?
Hit $100M ARR in just 24 months.
Keith Rabois called them the “best-run private company on the planet.”
Scaled from 130 to 400+ sales reps in 2.5 years since Nate joined.
Early AI adopters, they move fast on new tech.
Here are my takeaways from the episode on how the GTM team uses AI:
How Ramp’s GTM Team Uses AI
1. Build vs. Buy: They Don’t Just Chase Every AI Tool
Ramp is strategic with AI. They decide when to build in-house and when to use third-party solutions based on growth priorities.
2. AI Isn’t a Replacement
AI automates the boring stuff so reps can focus on building real relationships. It streamlines GTM operations, making processes faster instead of adding complexity.
3. AI Keeps Them Scalable at Hypergrowth Speed
When you triple your sales team in 2.5 years, manual processes break. AI eliminates repetitive tasks so systems don’t slow down as they scale.
4. AI Makes Sales Reps More Efficient
AI automates lead management so reps aren’t wasting time chasing bad leads. It cleans up data so they don’t deal with missing or messy info. It simplifies workflows so reps spend less time on admin work and more time closing.
Now, let's see AI use cases:
11 AI use cases for GTM and outbound
#1 - Letting AI fill CRM fields
Ramp uses Momentum to automatically update MEDDPICC fields in Salesforce, no more forcing reps to fill in every little detail manually.
Instead of making reps approve every AI-suggested update, they let AI autofill the fields and give reps a manual override if something looks off.
Way better data without slowing down sales.
#2 - Understanding Why Ramp Win or Lose Deals
For years, they’ve had the same old process in Salesforce:
Reps fill out a “reason lost” field. Maybe add a sub-reason. At the end of the month, they pull a big report with a pie chart that doesn’t really tell us much.
But now? AI gives us way more context.
Instead of just a checkbox saying “lost due to timing,” they get:
What part of the deal broke down.
If there were actual product gaps or if it was just bad timing.
If a competitor stole it or if the buyer was never serious in the first place.
And the best part? They can layer AI on top to pull all this back into structured fields, so it’s not just messy notes.
Way better than just another useless pie chart.
#3 - Catching deal risks before it’s too late
They’ve always tracked why they win and why they lose deals. But by the time they do a post-mortem, the deal is already dead.
So instead of waiting, they built a real-time deal risk alert for managers.
Here’s how it works:
If a buyer stops engaging, AI flags it.
The manager gets an instant Slack alert to jump in.
Instead of finding out later, they can step in now and fix it.
How they built it
The AI looks at real signals from calls and emails.
If a buyer seems disengaged (camera off, vague responses, pushing meetings out months), it triggers an alert.
It also scans emails, breakups happen in email, not on Zoom.
This helps managers:
Know when a deal is slipping (instead of finding out too late).
Step in early to multi-thread or re-engage.
Decide when to stop wasting time on a deal that’s already lost.
Instead of waiting until the end of the quarter to see what went wrong, they catch it while there’s still time to fix it.
#4 - Answer product questions in real-time
Instead of “Let me check and follow up later,” they use a built-in house Slackbot (focused on product knowledge) to get instant answers about their product, right in the middle of a call.
Here’s how it works:
If a prospect asks, “Does your product do X?”
Instead of saying, “We’ll get back to you,” they just ping a Slackbot.
AI responds instantly: “Yes, it does. Here’s a resource.”
Boom: they answer it on the call, no need to loop in an engineer.
This saves everyone’s time, the rep, the prospect, and the engineering team.
#5 - AI Cold email (Without Becoming an AI-Only Shop)
Their growth team and GTM engineers jumped on AI early.
Now, everyone’s talking about AI for lead scoring and automated emails, but they’ve been doing this for a while.
Here’s how it works:
AI scores warm leads and flags the best ones.
It reads email replies and sorts them:
High intent? Route it ASAP.
Low intent? Put it in the nurture bucket.
Neutral? Keep an eye on it.
One of the best use cases? Scoring responses so reps know who to focus on first.
What Do they Actually Do With the Scores?
The scores sync into Front (their shared inbox) so reps don’t have to dig.
They A/B test AI-generated emails against manually written ones.
They’re not an “AI-only” shop. AI helps, but reps are responsible for their own emails, they just use AI to make them faster and better.
#6 - Outbound Scoring
One tool they’ve been using for AI-powered lead scoring is Actively.
It analyzes past email convos, deals, and Salesforce data (plus some external signals).
It builds an outbound scoring model that gets better over time.
If it sees a prospect who, say, said 9 months ago they’d renew another contract soon, it flags them and routes the deal to the right rep.
The best part? It’s self-learning, it looks at who’s actually converting, then retrains itself to prioritize better leads over time.
#7 - AE to CSM handoff
One of the biggest gaps in handoffs is that CSMs often start with zero context when onboarding new customers.
Now? AI fixes that.
Instead of guessing, CSMs get everything they need upfront:
MEDDPICC
Who’s involved in the deal
Key call notes from the last six months
What products they committed to using (or what they think they will)
Where they are in the application process
Any relevant emails, calls, and discussions
It’s all pulled into Slack and Salesforce automatically.
CSMs don’t have to dig through emails or ping AEs for details. They get a real-time dossier in Slack or Salesforce, with all the important details.
Right now, they’re testing deal rooms, but for now, shared DMs between the CSM and AE keep everyone aligned.
#8 - SDR to AE handoff
They’ve been using DMs (with Momentum) to make the handoff between SDRs and AEs way smoother, especially at the top of the funnel.
Old way?
SDR books a meeting → hands it off to the AE.
The AE works the deal, but they have to constantly ping each other to check in.
A lot of back and forth, lost context, and wasted time.
Now? With Momentum, they just drop everything into a shared DM in Slack:
SDR + AE are both in the loop.
They post context from the call right away.
Next steps? Handled directly from Slack, even moving a deal to SQL with a single button.
#9 - Getting International Coverage Without Wasting Seller Time
They’ve been testing another AI use case that’s been super helpful, figuring out where their prospects actually operate without forcing sellers to fill out useless forms.
Here’s the problem:
Their team cares a lot about whether a company operates in multiple countries or just one.
But reps don’t always ask this on every call.
Forcing them to fill in bad data just because a field is required? That’s useless.
So now, AI automatically pulls this info:
If it’s explicitly discussed on the call, the system logs it.
If not? It doesn’t ask reps to guess, it just leaves it blank until it comes up later.
#10 - Visualize your book of business
They’ve been testing Rox, a newer tool on the go-to-market side, and so far, it’s been a solid addition.
Here’s why:
It’s actually user-friendly. Unlike most CRMs that feel like they were built in the early 2000s, Rox makes it easy to visualize your book of business.
It’s customizable. Reps can add their own columns, like marking which accounts are public, without needing admin approval or a degree in Salesforce.
It has built-in AI. You can run natural language searches and get real insights instead of just staring at endless rows of data.
#11 - Track Sales Performance in Real Time
They also use Atrium, which is an AI-powered sales manager tool.
It tracks step changes over time. You can actually see the patterns shift, like if your team suddenly starts sending way more emails or if something in the process changes. It catches things you wouldn’t notice otherwise.
At first, it might feel like just another dashboard. But then, one day, you see an anomaly you didn’t expect, maybe a drop in meetings booked, or a change in response rates.
Tools mentioned in the episode
Momentum.io
Clay
Rox.com
Actively AI
Atrium
That's it.
How are you using AI with your GTM and outbound team?
If not, what's holding you back? Hit reply and let me know, I'd love to hear your thoughts!
See you in the next newsletter.
Cheers,
Elric
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