Go-to-market has always been different in vertical. There’s no one playbook—some founders swear by sellers from industry, while others just hire great producers period. ACVs are all over the place. Enterprise selling means something very different. But there are some common threads every founder think about, whether you’re a 20+ year-old SaaS player or an idea-stage Vertical AI startup.
This week, we’re in the trenches with Kyle Norton of Owner.com, a unicorn powering independent restaurants’ marketing & ordering nationwide. With $183M raised to date from backers including Headline, Meritech, Redpoint, Menlo, and Altman Capital, they’ve been on a tear. Kyle joined Owner around $2M ARR and ~30 people… 3 years later, they’ve grown to ~$50M ARR and still scaling ~2.5x YoY.
In this episode, Kyle lends his expertise to the early-stage Vertical GTM playbook in the AI era: how he evaluates talent, why vertical SMB needs “systems engineer” thinking in the CRO seat, and how to instrument a revenue factory, Owner-style.
Stick around until the end to get Kyle’s take on AI’s #1 impact for GTM leadership.
I) Vertical Market Pulse
1. Mapping vertical GTM patterns
GTM Motions Deployed by Leading Vertical SaaS | Luke Sophinos @ Linear
Luke dug into how top vertical software companies actually go to market, and how their playbooks shift as they scale. Outside sales monsters like Palantir live on massive seven-figure ACVs and long cycles; downmarket, you’ve got public companies with sub-$1K ACVs and highly leveraged product- or marketing-led engines (e.g. LegalZoom, Autodesk).
Two surprising takeaways from his research:
Almost any motion “can work” somewhere on the ACV spectrum—but you can’;t mismatch motion and economics (e.g., field sales at $10K ACV is a knife fight).
Founders’ backgrounds (e.g. sales vs. product) show far less correlation with the eventual GTM motion (e.g. enterprise vs. PLG) than intuition might suggest.
Founder implications: pick your motion based on your market, ACV, sales cycle, and CAC / implementation complexity—not your hero companies or your own resume. Make sure pricing, hiring, and product scope are all congruent with that choice.
2. A Palantir Approach to Enterprise Vertical AI
Brain Co. launches with $30M Series A | Press Release / Yahoo Finance
Brain Co. emerged from stealth with a $30M Series A co-led by Gil Capital and Affinity Partners to build an AI platform for “the world’s most important institutions” across government, healthcare, energy, and other regulated sectors. The company, incubated by Elad Gil, Jared Kushner, Luis Videgaray, and Eric Wu, pitches itself as an institutional AI layer: deeply embedded, high-stakes workflows, delivered as evolving “living systems” that upgrade as models improve.
On paper, it’s a bit of a Palantir play: long, complex implementations with huge contract values, plus a services-heavy early posture to help institutions actually deploy AI rather than just “experiment.” The bet is that institutions don’t want to sift through dozens of point solutions (nor do they have the IT firepower to do so). They want a strategic partner that deploys & adapts AI for their use case.
Founder implications: if you’re building for large institutions, consider a services-first wedge (wherein your main product is AI / software). Implementation, data integration, and ongoing adaptation may be more than “nice-to-haves” at this point in the cycle—at early stages, they may be a core part of the product.
3. B2C Wedges for B2B Vertical AI
Doctronic Raises $20 Million Series A | Press Release / PR Newswire
Doctronic is positioning itself as a 24/7 “digital doctor”—an AI-native front door that handles triage, diagnosis support, and treatment guidance, with optional handoff to human physicians. The company raised a $20M Series A led by Lightspeed Venture Partners, following a $5M seed from Union Square Ventures and Tusk Ventures.
The public wedge looks B2C: anyone can show up, describe symptoms, and get guidance plus a path to a clinician. But the long-term ambition seems to be squarely B2B: becoming the AI-native system of record that sits alongside or even replaces parts of traditional EHR infrastructure for front-door primary care. That means blending consumer growth mechanics with deep infrastructure ambitions… a pattern we’re starting to see across vertical AI.
Founder implications: vertical AI “front doors” that feel like consumer apps but eventually target a deeper B2B use case are going to be a powerful pattern. If you pursue it, you need to pay attention to B2C metrics and may be valued similarly early. Conceptualize your pivot point to B2B—and why you have an asymmetric advantage vs. startups going at it head-on. Also, keep an eye on retention: big model churn is moderately improving but long-term PLG AI churn is very much TBD.
II) Vertical Titan
Kyle Norton — CRO @ Owner.com
The backstory
Kyle “fell into” sales in a boiler-room environment—four years of pure cold calling and selling low-value conferences. 14 months later, after a brutal 2008 downsizing, he found himself managing his first sales team. He’s been building and rebuilding go-to-market teams ever since, with a focus on vertical GTMs.
A bit more on his CV:
VP Sales at League, taking the company from $0 to ~$25M ARR in enterprise benefits and healthcare.
Three years at Shopify, where he first ran GTM for point-of-sale and then owned GTM for the entire Canadian market across all products and segments.
Now leading all things revenue at Owner.
Kyle originally did not intend to go back to true early-stage. He’d just had his second child, loved his role at Shopify, and had promised himself he wouldn’t do another small startup. But after taking a closer look, and meeting CEO Adam Guild, he realized he might regret not joining. When he signed on, Owner was ~30 people at ~$2–3M ARR. Roughly three years later they’re at ~$50M ARR and still growing at ~2.5x year-over-year.
Today, Kyle’s remit spans sales, partnerships, new customer onboarding, RevOps, enablement, and tight collaboration with data and growth marketing. He’s architecting Owner’s entire revenue factory, which requires collaboration across much more than just “the sales org.”
The hardest part
Running a high-velocity SMB motion like an actual system. Most CROs are trained as deal jockeys; vertical SMB demands someone who thinks like a growth engineer.
Instrumenting everything without overwhelming reps. You need detailed dispositions, show/no-show tracking, and funnel instrumentation—but also simple workflows reps will actually follow.
Hiring for talent density in an unglamorous segment. Convincing president’s-club performers to leave more “brand-name” SaaS roles for small-ticket restaurant sales takes belief, comp, and culture.
Balancing founder magic with scaling reality. Founder “tractor beams” are real—but so is the risk of founders who can’t let go. Keeping that tension healthy is a constant job.
Making change management a superpower instead of constant chaos. If you’re tweaking pricing, messaging, routing, and process every month, you need structure or you’ll burn the org out.
Memorable lines from Kyle
“You can be a great enterprise leader by being a deal jockey. In vertical SMB, you have to love the numbers and the factory.”
“I’d rather pay top-of-market and have almost everyone hit 140–150% of OTE than set fantasy quotas and pretend we’re efficient.”
“Change management is a competitive advantage. If you can ship more improvement with less chaos, you compound faster than your market.”
How CROs choose founders
Kyle evaluates founders through a four-part lens:
Force of nature: unnatural drive, charisma, and almost psychotic obsession with the customer’s problem.
Supercomputer: raw intelligence plus learning speed—someone who can tear through a book overnight and come back with advanced questions.
Founder–market fit: a personal reason they should win this market—for Adam, saving his mom’s dog-grooming business and seeing the impact of getting local marketing right.
Humility: enough self-awareness to keep learning, hire senior leaders, and actually let them own things as the company scales.
He’s seen a consistent pattern: without that last trait, many companies unravel around Series B. The founder insists on making every decision, can’t accept tough feedback, or hires senior people but doesn’t let them lead. At small scale you can “go full founder mode.” At scale, that same behavior breeds chaos. Owner worked for Kyle because Adam was uniquely strong on all four dimensions—and willing to “disagree and commit” in both directions.
Revenue leader–market fit in vertical SaaS
Kyle’s view: in vertical SaaS and AI—especially SMB—you don’t just need “a great sales leader”, you need a systems thinker.
Traits he optimizes for:
Data obsession: sees the world as volume × conversion at every micro-step; wants instrumentation before anecdotes.
Operational rigor: happy to live in BI tools, dashboards, cohorts, and funnel math—not just in pipeline reviews.
Factory mindset: thinks in terms of a revenue manufacturing line or “revenue factory,” where each stage has clear inputs, outputs, and experiments. He credits Winning by Design for the framing and runs with it.
Partnership with data & marketing: at Owner, RevOps, data, and growth are effectively a GTM brain trust, not adjacent functions. Tools like Sigma on top of Snowflake give them a shared source of truth.
In practice, this means Kyle invested early in senior RevOps and data leadership—long before many companies at $3–4M ARR would. The payoff is the ability to run highly instrumented Monthly Business Reviews (MBRs) and choose high-leverage problems instead of reacting to noise.
Building the revenue factory: from dispositions to MBRs
Kyle’s “revenue factory” approach is about breaking the journey into micro-stages, then making each stage measurable and improvable:
Instrumentation first:
Track booked vs completed vs rescheduled meetings.
Capture cold-call outcomes with proper dispositions (connected / not, gatekeeper, decision-maker, outcomes).
Use call-transcript AI (Momentum) to auto-enrich CRM with fields reps would never fill reliably on their own.
MBR as the control room:
Monthly, the team walks through every step of the funnel—leads, connects, books, shows, stages, win rates, early churn.
Data team pulls a week’s worth of deep dives into Sigma, highlighting where volume is high but conversion is weak.
The goal is to avoid “incrementalism” and instead pick the 1–2 moves with the biggest combination of payoff and probability.
Annie Duke–inspired triage:
For each possible project, they look at possibility, probability, payoff, and perspiration.
They then prioritize the highest-payoff, highest-probability projects with reasonable sweat cost—and de-prioritize shiny objects.
Critically, Kyle is self-aware about his own gaps: he’s strong on diagnosis and creativity, less excited about project management. So he’s built around himself a RevOps leader, enablement leader, and data leads who loves Asana / Notion / Wrike. That humility is part of what makes the system work.
Change management as a competitive advantage
Owner pushes a lot of change: pricing tweaks, messaging updates, routing logic, AI workflows, tooling upgrades. To avoid chaos, they built a simple—but strict—change-management framework:
Traffic-light model
Green-light changes: small tweaks; a Slack message and maybe a short loom/video are enough.
Yellow-light changes: moderate impact; require a playbook, enablement touch, and some reinforcement.
Red-light changes: big rocks like pricing; require formal training (with certification), live rollouts, internal video, Slack comms, and a tiger team that meets bi-weekly to monitor impact.
Embedded in the operating system
When a PM or sales leader spins up a project in Notion, they choose a scope (green/yellow/red).
That choice auto-generates the corresponding enablement projects and tasks for the enablement team.
The result: instead of random bursts of change, Owner can reliably ship big shifts without burning out reps. Kyle’s view is that if you can consistently change faster than competitors with less organizational drag, that compounds into a durable moat over time.
Hiring high-velocity vertical sellers
Owner’s early hiring strategy was deliberately aggressive: pay at the top of the SMB market and go steal president’s-club AEs from adjacent vertical companies—especially those already selling to blue-collar or local-services SMB (dental, home services, etc.).
Key lessons from their hiring retrospectives:
Deal size experience matters more than domain experience. If a rep has successfully closed 50–100 opportunities / month at $5–15k ACVs, they can probably adapt from dental to restaurants. (Though admittedly, this may be different for enterprise.)
Sales craft is overrated at this stage. They found that interview “sales presentations” were weakly correlated with performance. What really mattered was drive, learning velocity, and discipline.
Organization beats charisma. Top performers tended to be checklist people: they lived in their calendars, followed up relentlessly, and managed 50+ deals/month without dropping balls.
To make the math work, Owner sets OTE where it looks merely “good” on paper—but then designs quotas and territories so that the average AE hits ~145–150% of OTE. That means the best reps “clean up,” word spreads, and they can keep pulling in top talent from other VSaaS orgs.
Owner’s GTM wedge vs Toast
Owner’s motion is fully inside-sales: no door-knocking, no field reps. Kyle views door-to-door as a legacy motion that made sense when data and targeting were poor; today, strong data and dialers beat walking a strip mall.
The wedge is simple and hyper-aligned with what restaurant owners care most about: revenue.
Owner calls out how well-reviewed restaurants are sometimes outranked on Google by worse competitors—and pins that to a broken digital presence.
They sell a set of outcomes: higher Google rankings, more direct online orders, branded mobile apps, and automated marketing that feels like what big brands run.
Kyle shared the analogy Owner : Toast :: Shopify : Lightspeed POS:
Toast will likely stay more operationally sophisticated (reporting, staff controls, etc.) for a long time.
Owner is comfortable being “less deep” operationally if it can demonstrably grow revenue faster via SEO, online ordering, and retention.
And the data backs it: Kyle notes that, for restaurants moving their sites from POS-provided pages to Owner, they regularly see step-change improvements in organic traffic and online sales.
AI as a revenue leadership operating system
When Kyle talks about AI, he very intentionally avoids “the one killer use case.” Instead, he frames it as a change in how the company solves problems:
Production use cases today include:
Momentum: AI-assisted call transcription that auto-fills CRM fields and dispositions, massively reducing rep admin while improving data quality.
AI sales simulators (Avara) & training tools: reps can practice against realistic buyer personas instead of generic scripts.
Post-sale automation (Trig): driving onboarding and lifecycle workflows off events and usage instead of manual human follow-up.
Internal agents (“OneMind”) and data tools (“data lane”): helping reps and managers query playbooks, metrics, and territory insights in natural language.
But for Kyle, the bigger unlock is cultural:
They budget real dollars and headcount for GTM AI.
They treat AI projects as first-class initiatives in the MBR process, not science projects.
They hold leaders accountable for experimenting with AI when they encounter new problems, not only when someone from “AI” taps them on the shoulder.
His stance for other GTM leaders: stop waiting for the perfect AI playbook. Start by resourcing one, two, or three serious initiatives, accept that some will fail, and focus on shifting the org’s reflex from “who can I hire?” to “how could we design this system differently with AI?”
III) Vertical Playbook
Vertical SMB GTM
Why it works
Vertical SMB GTM is unforgiving:
ACVs are modest (~$10K in Owner’s case).
Volume is high.
Buyers are time-poor and skeptical.
In that environment, you can’t rely on heroic selling. You win by:
Precisely matching motion to ACV and product complexity.
Instrumenting every step of the funnel so you can find systemic bottlenecks.
Running structured change so the org can absorb constant improvement.
Kyle’s factory model turns GTM from a set of tribal rituals into an engineering problem: define stages, measure, hypothesize, test, repeat.
How to run it (Owner.com-inspired blueprint)
1. Commit to RevOps + data early
Before you “feel ready,” hire a real RevOps lead and secure part of a data team.
Stand up a BI layer (e.g., Snowflake + Sigma) and make sure all GTM systems log there.
2. Instrument the journey
At minimum:
Lead source and routing.
Connect rates and detailed call dispositions.
Meetings booked vs completed vs no-show vs reschedule.
Stage-by-stage conversion and cycle time.
Early activation and early churn (esp. first 90 days).
Automate as much as possible with tools like Momentum, Salesforce, and Salesloft so you’re not relying on reps’ manual data entry.
3. Run a monthly revenue factory review (MBR)
One data owner builds a single source-of-truth deck in BI.
The agenda walks the funnel top to bottom.
For each stage, you ask: where is volume high and conversion low, and how has that changed over the last 3–6 months?
4. Prioritize with possibility / probability / payoff / perspiration
Brainstorm projects that could improve the metrics you care about.
Score each on:
Possibility: what could we try?
Probability: how likely is it to work?
Payoff: if it works, how big is the move?
Perspiration: how much effort and change management does it require?
Choose 1–2 big bets per month or quarter; ruthlessly de-scope the rest.
5. Make change management explicit
Adopt a simple green/yellow/red framework for all GTM changes.
For yellow/red changes, require: written playbook, training, certification (if needed), and a post-launch review cadence.
Bake this logic into your project tooling so the process runs itself.
6. Layer AI into this system, not on top of it
Start where you already have pain and data—call notes, CRM hygiene, training, onboarding.
Evaluate AI tools based on how well they plug into your existing factory (data, workflows, metrics), not just demos.
Make at least one GTM AI initiative a standing line item in your MBR so it doesn’t get deprioritized.
Founder litmus tests
Motion vs ACV: Is our motion (field / inside / PLG / partner-led) actually congruent with our ACV, implementation complexity, and payback period?
Data foundations: Could we, today, answer basic questions like “Is our biggest problem meetings booked or meetings shown?” without hand-waving?
RevOps investment: Do we have anyone whose full-time job is to own GTM systems, reporting, and process—or is that secretly the founder/Head of Sales at night?
Change load: If you asked your reps how many things changed in the last 60 days, would they describe it as “energizing” or “chaotic”?
AI posture: Are we running one or more AI initiatives in production that materially change work—or are we still in demo-land?
If you’re < $10M ARR and answering “no” across the board, Kyle would argue your growth ceiling is self-imposed.
What we debated on-air
A few of the spicier threads from the conversation:
Does seller domain expertise matter?
Kyle’s view: for Owner’s wedge (marketing and growth for restaurants at ~$10K ACV), deal-size and velocity experience matters more than restaurant background.
Nic & Luke’s counter: in construction and more operationally complex verticals, reps who don’t understand the workflows can struggle unless paired with true industry experts (e.g., “industry engineers”).
The synthesis: match your hiring bar for domain to product complexity and who in the deal actually holds the pen.
Can you run high-touch motions at “too low” ACVs?
Owner’s data suggests: if your ACVs are in the ~$10K range and your cycles are short, high-velocity inside sales can scale—especially with AI assistance and strong RevOps.
But if you’re selling core operating systems with heavy implementation at $20–25K, you’re probably mis-priced and will drown in long cycles and PS-heavy launches.
Door-knocking vs digital outbound:
Kyle believes door-to-door is increasingly a relic for new Vertical AI, given modern data and automation.
Some legacy categories still make it work, but if you’re starting fresh, digital-first inside sales is likely a better long-term asset.
Nic & Luke’s counter: this is probably true for vertical SMB but trade shows / in-person are booming more than ever. AI has raised the bar for outbound.
AI: one big bet vs many small ones
There’s a temptation to look for the “one huge AI unlock.”
Kyle argues you’re better off embedding AI into many small parts of the system—CRM hygiene, training, research—while also running a few bigger, riskier bets in parallel.
Next week on Verticals
Tune in next week for a deep dive on vertical aggregators with Sam Youssef, the co-founder & CEO of Valsoft Corporation.
Thanks for reading! If you’re working on an idea in Vertical AI, Euclid Ventures would love to hear from you. DM us here or on LinkedIn.
