The Goats of Growth

Scott Hanson says chatting with ChatGPT in a browser isn't AI fluency. To him, AI fluency means using AI to build a tool that another person uses to support themselves. He says it's far more attainable than most people realize.

Scott is the founder of Tenfold Labs, which builds AI systems for Fortune 500 teams. Before that he co-founded Pop Bookings, the "Uber of event staffing," as COO and a non-technical founder. The app has roughly 400,000 active staff. He started building apps with AI in 2022 and was one of the first power users of Rork.

His central argument: keep a human in the loop. He skipped black-box agent setups like OpenClaw and works in Claude Code and the CLI, watching what the agent does until he trusts it. He uses agents for narrow jobs inside plain, repeatable software, and doesn't let them own whole workflows end to end.

The story: one conversation about AI at his Austin gym led to a contract with a $50 billion Berkshire Hathaway subsidiary in distribution. Its reports took weeks to compile and reached the C-suite one to three months late. Scott built real-time "transparency layers" across hundreds of initiatives. He says Deloitte would likely have charged around $1 million and taken eight months to a year. He delivered in two to three months, as a solo developer, for a fraction of that.

WHAT WE COVER

• Why he skipped OpenClaw and went human-in-the-loop in the CLI
• What a CLI is, explained for non-developers
• Watching the agent work: the mechanic metaphor
• A personal AI harness for email, calendar and finances
• Real-time reporting for a $50B company
• How AI exposes the dark areas of a company, and what that means for middle management
• His definition of AI fluency, and starting with the work you hate
• Turning workflows into simple software instead of black-box agents
• Matt Pocock's "grill me" skill and a Fathom-to-CRM workflow
• Rapid Fire Five, Allyson Barr's Mars question, and Scott's question for the next guest

CHAPTERS

0:00 Intro and Common Room

1:12 From Pop Bookings to AI

3:43 Early ChatGPT and building his own apps

5:50 Finding Rork

7:42 Why he skipped OpenClaw

8:38 What a CLI is

9:59 Human in the loop vs. the black box

12:20 Build trust by watching the agent work

16:12 Building his own AI harness

17:38 The conversation that became Tenfold Labs

20:46 The $50B client and real-time transparency layers

24:24 Hundreds of initiatives, and what it's worth

26:45 AI, headcount and middle management

28:49 What AI fluency actually means

29:44 Start with the work you hate

31:02 Breaking down a workflow

36:37 When a workflow should become software

37:50 Let Claude interview you

39:15 Go basic: do one thing

42:38 Rapid Fire Five

46:35 Bonus question: would you go to Mars?

47:37 Scott's question for the next guest

49:02 Where to find Scott

Connect with Scott Hanson on LinkedIn, or learn more about Tenfold Labs at tenfoldlabs.io.

This episode is sponsored by Common Room (https://www.commonroom.io/?utm_source=goat-podcast&utm_medium=partner), the AI GTM Intelligence Platform trusted by teams like Okta, Anthropic, and Fireworks AI.

New conversations with go-to-market leaders every week. Subscribe to The GOATS of Growth on Apple, Spotify, or YouTube.

Steve Wooledge didn't build a bigger marketing team. He built a shared AI operating system, and Collate no longer has a social media hire or a customer case study writer. The contractors who covered that work came in house.

Steve is CMO at Collate, the semantic intelligence platform built on open metadata. He has spent 20 years in data and analytics marketing at Alation, Alteryx, Hazelcast and Teradata. Collate is the commercial backer of the Open Metadata Project, and he explains why a context layer matters when AI agents query company data.

His central argument: AI is only as trustworthy as the context it can see. He says Claude Opus pointed at a data warehouse gets roughly 60% of answers right, and Collate's benchmarks with context reach 94 to 95%. The same idea runs his own marketing org. The team keeps its skills, messaging and memory in one GitHub repo and updates it through pull requests with peer review, about 50 in the last six months.

The concrete number: Steve puts AI at roughly 20% of his marketing budget saved, and thinks 30 to 33% is reachable. He's clear that's a gut estimate, not a measured one. We also get into where he reinvests it (organic over paid search and social), how his SDRs use Common Room signals, and the 7:30 a.m. email his AI second brain sends him from every call.

WHAT WE COVER

• Why AI answers on structured data run about 60% accurate without context
• How Collate's context layer gets benchmarks to 94-95%
• A team GTM operating system in GitHub: skills, messaging, pull requests
• Cursor for non-coders, and why he keeps his "brain" local
• The roles Collate didn't hire after bringing content in house
• The 20% budget saving, and why he pushes it toward 30%
• How he evaluates AI tools: referrals, not demos
• Fathom call summaries feeding a daily action-item email
• Rapid Fire 5, Jessica Storry's bonus question, and Steve's mulligan question for the next guest

CHAPTERS

0:00 Intro and Common Room

1:26 How Collate uses Common Room signals

4:16 What Collate does: a context layer for AI

7:11 From AI noob to the "AI CMO"

10:22 Cursor, explained for non-coders

13:41 Whisperflow and Friday team AI sessions

16:14 Claude Code from your phone

17:41 What AI did to marketing velocity and hiring

20:03 Quantifying the savings: 20% to 30%

24:19 Where the savings go: organic over paid

26:05 Sales cycles and deal velocity

26:58 How Steve evaluates AI tools

29:13 Why human content matters more now

30:09 The Fathom second brain and 7:30 a.m. email

32:50 The shared GTM OS: pull requests and peer review

35:32 Rapid Fire 5

42:36 Bonus question: starting over, not in tech

44:08 Steve's question for the next guest

45:47 Where to find Steve

Connect with Steve Wooledge on LinkedIn, or learn more about Collate at getcollate.io.

This episode is sponsored by Common Room (https://www.commonroom.io/?utm_source=goat-podcast&utm_medium=partner), the AI GTM Intelligence Platform trusted by teams like Okta, Anthropic, and Fireworks AI.

New conversations with go-to-market leaders every week. Subscribe to The GOATS of Growth on Apple, Spotify, or YouTube.

Dustin Schwarz's team replaced PayReady's human concierge outreach with a fully AI solution. It underperformed: skilled people reach fewer residents, but they resolve far more of the conversations they do reach. So PayReady built a hybrid process where automation handles routing and simple conversations, and specialists take escalations and sensitive discussions.

Schwarz is chief customer officer at PayReady, the multifamily SaaS platform behind AI-driven rent collection, resident and payment portals, and CRM tools serving more than three and a half million units. He has spent 15-plus years in multifamily and prop tech, including time at RealPage.

His argument: AI is the worst it will ever be today, but "good enough" is a business judgment, and many companies will accept 85% of a human's effectiveness because the incentives reward cutting cost over serving customers. PayReady is betting on the opposite, a customer-centric 100%. That bet is backed by a chief risk officer, a vendor network that shares regulatory updates, and a view that SaaS built on a feature is dead while SaaS built on partnerships and judgment is not.

One concrete result: after buying Buzz.ai, an AI platform trained on property management behavior, PayReady saw variable margin improve roughly two and a half to three times, and one support agent can now serve about three times as many customers.

WHAT WE COVER

- Why a fully automated concierge replacement underperformed human specialists

- Where humans stay in the loop, and whether that share shrinks over two years

- State and local contact rules, junk-fee scrutiny, and legislative risk

- Why "85% good enough" wins in some companies and what that means for customers

- Build versus buy: acquiring Buzz.ai, and moving support to Intercom

- Hiring for adaptability, creativity and language skill over domain experience

- Where AI still fails: people, judgment and high-stakes analytics

CHAPTERS

00:00 Why this episode is out of order

00:57 Intro

02:21 What PayReady does

04:10 Buying Buzz.ai

06:41 Why the all-AI rollout fell short

09:24 The hybrid process

10:40 Will human-in-the-loop shrink?

12:15 Legislative and regulatory risk

14:30 Staying compliant

17:03 Why feature-based SaaS is dead

18:21 Why 85% is "good enough" for some companies

21:06 Business impact: margin and support

22:08 Build versus buy

27:05 Hiring for adaptability over domain experience

34:58 Where AI still fails

39:19 Rapid Fire Five

Connect with Dustin Schwarz on LinkedIn, or learn more about PayReady at payready.com.

This episode is sponsored by Common Room (https://www.commonroom.io/?utm_source=goat-podcast&utm_medium=partner), the AI GTM Intelligence Platform trusted by teams like Okta, Anthropic, and Fireworks AI.

Subscribe to The Goats of Growth on YouTube, Apple Podcasts and Spotify, and leave a rating or review so more go-to-market leaders can find the show.

Allyson Barr spent the first six months of this year watching her content team hand back sixty to seventy percent of every AI draft for rewrites. That number keeps dropping now — not because the AI got smarter on its own, but because her team built it an editorial memory: voice dossiers on every subject-matter expert, a running log of what not to say, and a habit of feeding every human correction back into the system.

Allyson is the Chief Marketing Officer at Active State, which has been securing open source software since 1997 and now sits squarely in front of the problem it was built for: AI coding assistants pulling unvetted packages into production. She joined to run a full commercial transformation, rebranding and relaunching the company in five months, after three years at AWS running sales planning across a hundred-billion-dollar business. She's also, as of this summer, co-founder of a second company, Culture Flow.

Her argument: AI didn't just speed up Active State's content output, it changed where marketing spends its time. Six months ago the company loosened its AI guardrails company-wide while tightening standardization around Claude as the primary assistant, and content volume jumped from roughly six pieces a month to about a hundred. The deeper shift, she says, is that the team's real job stopped being production and became curation.

The clearest example she gives is a cold email. A vendor pitching Active State built their entire outreach around the titles from a Taylor Swift album she'd posted about — a few minutes of real attention that landed harder than any AI-personalized blast, and the one she now points to when she talks about the line between a trust enhancer and a trust eroder in AI-assisted outreach.

WHAT WE COVER

Training AI on five separate voice profiles for Active State's subject-matter experts The February shift that loosened AI guardrails while tightening standardization Measuring AI's impact: content volume, SDR research time, and AE proof-of-value cycles Build vs. buy in an era of vibe-coded internal tools What she looks for in candidates now that "using AI" is table stakes Culture Flow, her side venture building a workplace-trust tool

CHAPTERS

0:00 Intro & Common Room

1:27 Active State: securing open source

3:26 Building AI trust in go-to-market

4:36 Training AI on the company's voice

8:44 The February AI philosophy shift

10:10 Measuring AI's commercial impact

12:15 Scaling content across channels

13:23 Automating workflows with Google Drive and Claude

16:47 Prioritizing the AI initiative backlog

21:41 Automating the podcast workflow: a real-world example

24:09 Cutting through tool noise with personalized outreach

27:15 Trust erosion vs. trust building in sales

28:37 AEO and build vs. buy

33:13 New skills, hiring, and the AI generation gap

37:50 Culture Flow and the Rapid Fire Five

Connect with Allyson Barr on LinkedIn, or learn more about Active State at activestate.com.

This episode is sponsored by Common Room (https://www.commonroom.io/?utm_source=goat-podcast&utm_medium=partner), the AI GTM Intelligence Platform trusted by teams like Okta, Anthropic, and Fireworks AI.

New conversations with go-to-market leaders every week. Subscribe to The GOATS of Growth on Apple, Spotify, or YouTube.

When Aware launched a new product this year, Brian Krause didn't pull SDRs off existing accounts or hire three more. He put top-of-funnel demand gen on an AI agent from day one, 100%, on purpose. New product, new ICP, no legacy SDR team to muddy the results: "It's the perfect scenario to start at zero and just build an agent for it." What he wants out of it is clean data on which of the platform's value propositions actually land with buyers.

Brian is CRO at Aware, the first US-based biometrics company, 35+ years in, where he runs sales, account management, customer success and RevOps. Before that he led revenue teams at identity verification and computer vision companies, and at Pivot3, later acquired by Quantum.

The agent is "a campaign manager on steroids," running email and LinkedIn, but nothing goes out without a rep's eyes on it. Two reasons. The handoff to a human AE can't feel jarring. And on a technical product, the agent can't say yes to something that's only true if the customer spends more money. On hiring, he's blunt: "If I have to teach you what a CRM is, I'm not okay with that." Reps should show up AI-enabled. Aware's new head of federal runs on tooling about 99% of the day, and she works at a pace well above everyone else.

He also has the most honest take on CRM hygiene you'll hear: every company has the problem, and the fix isn't enforcing it harder. It's taking the ask away from reps entirely.

WHAT WE COVER

- Why a brand-new product launch is the right place to test an AI SDR

- Isolating variables: what the agent's data tells product about which value prop wins

- The two guardrails: brand-consistent handoffs, and no overpromising

- Build vs. buy: buy customer-facing, build internal productivity

- RevOps as the owner of the agent roadmap

- Define what good looks like before you automate it

- Why hiring is plateauing, and what BDRs and AEs should do about it

- Rapid Fire: three teenage daughters, and ringing the bell on Wall Street

CHAPTERS

0:00 Intro and sponsor: Common Room

1:49 Brian's role at Aware

3:52 How trust gets built into biometrics

5:30 Sales is still a people business

7:43 Where AI sits in the funnel today

9:24 Going 100% AI SDR on a new product

15:57 Who built the agent, and how it was trained

18:03 A campaign manager on steroids

19:47 Sponsor: Common Room

20:14 The two guardrails

23:50 Build vs. buy

26:34 RevOps owns the agent roadmap, and the CRM hygiene fix

28:55 Define good before you automate

30:49 Hiring plateaus, and showing up AI-enabled

33:48 "If I have to teach you what a CRM is…"

37:07 Rapid Fire Five

40:31 Ringing the bell on Wall Street

41:37 Outro

Connect with Brian Krause on LinkedIn, or learn more about Aware at aware.com.

This episode is sponsored by Common Room (https://www.commonroom.io/?utm_source=goat-podcast&utm_medium=partner), the AI GTM Intelligence Platform trusted by teams like Okta, Anthropic, and Fireworks AI.

To hear more conversations like this one, subscribe to The GOATS of Growth on Spotify, Apple Podcasts, or YouTube.

Jessica Storry co-founded Stockpress in 2019 out of the web design agency she was running, drowning in files across Dropbox, Box, Google Drive and hard drives clients mailed them. They took a contract building on an open-source DAM, decided it was overcomplicated, pitched the customer on something simpler, got told no, and built it anyway -- on her promise that she'd sell it. She did. Stage 2 Capital invested, they closed the agency in 2020, and Stockpress now occupies the gap nobody was serving: a $9.99 Dropbox account on one side, a $2,500-a-month enterprise DAM on the other, nothing in between.

The same instinct drove the decision that actually grew the business. They started out cloning the enterprise DAMs and charging less, and were still doing every onboarding by hand. So they re-architected around self-serve, lowered the price -- the only time it's ever gone down -- and replaced a product full of in-app pop-ups with a five-step onboarding checklist. "Maybe we don't give customers enough credit sometimes. They can figure it out if they if it's presented in the right way." She also tells the story of the Friday night she spent topping up tokens in Claude Design and ended with nothing to show: "I could have just designed that page myself in an hour and it would have been fine."

WHAT WE COVER

- Arguing against an AI support agent, and being wrong: 72% of tickets closed by the bot

- What AI is allowed to write, and what has to be accurate enough that it isn't

- Wireframes straight to the dev team, and why nothing AI-made ships as final design

- Going self-serve: lowering the price and cutting the hand-holding

- The five-step checklist that beat in-app pop-ups

- 85% of customers leaving every AI feature switched on

- Buyers who arrive from ChatGPT having never seen the website

- Where AI burned money for an eight-person team and produced nothing

CHAPTERS

0:00 Intro and sponsor: Common Room

1:16 What Stockpress is, and the founding story

4:23 Cutting the hand-holding, and lowering the price

7:59 The five-step checklist that replaced the pop-ups

10:28 AI inside the product, and the 85% who turn it all on

13:17 Arguing against Fin, and losing

15:36 Under three minutes, by hand, at any hour

18:02 Common Room, and how leads get attributed

19:07 Otterly and buyers who never see the website

19:59 Fifty prompts, and building content to show up against them

21:17 Narrowing the ICP after being "Stockpress for everyone"

23:38 Writing with AI, and the one thing she won't hand over

27:22 Claude Design for wireframes, straight to the dev team

28:46 AI in customer success, and the MCP server in progress

35:18 Her Friday night with no output

38:37 Rapid Fire Five

Connect with Jessica Storry on LinkedIn, or learn more about Stockpress at stockpress.co.

This episode is sponsored by Common Room (https://www.commonroom.io/?utm_source=goat-podcast&utm_medium=partner), the AI GTM Intelligence Platform trusted by teams like Okta, Anthropic, and Fireworks AI.

To hear more conversations like this one, subscribe to The GOATS of Growth on Spotify, Apple Podcasts, or YouTube.

Eric Herzog says AI cut his team's content development time in half — and that anyone treating it as more than a tool is about to embarrass themselves in front of their biggest customers. A farmer still drives the tractor. A lumberjack still runs the chainsaw. What's changed is that the tool is now fast enough to make a sloppy company look sloppy at scale.

Eric is CMO at Infinidat, the enterprise storage company behind InfiniBox, recently acquired by Lenovo and now operating semi-independently inside its Infrastructure Solutions Group. He runs a 15-person team across the full breadth of marketing, selling high-end systems to the Global Fortune 1000. Before Infinidat he held CMO and VP roles at IBM Storage and EMC, and helped build several startups that were acquired.

His operating motto is trust but verify. Humans make errors; AI hallucinates, which is the same thing. He's seen it firsthand — someone showed him an AI-generated profile of himself with the wrong university, the wrong location, and the wrong Eric Herzog. In high-end enterprise, a data sheet claiming something technically impossible tells the buyer you're a sloppy company, and in storage people lose their jobs over a bad buying decision.

The payoff is real when it's controlled. Eric's team ran an AI competitive analysis for sales, and it surfaced genuine Reddit reviews from end users explaining why they ripped out a competitor's product — material the reps now carry in their back pocket. His warning on workflow design is just as concrete: get the data sources wrong at a global manufacturer and the bulldozer bolts ship to the Corolla factory.

WHAT WE COVER

- Why AI is transformational but still a tool — and how open-source AI can expose your IP, customer data, and GDPR, HIPAA and PII obligations

- The bulldozer-and-Corolla problem: what bad AI workflow design does to a supply chain

- "Trust but verify," and the AI profile that got Eric's own biography wrong

- Where the ROI actually shows up: content creation and competitive intelligence

- The Reddit reviews an AI tool found that Infinidat's sales team now uses

- Why a human designer still beats AI at building a deck, and why AI still gets you halfway there

- Build vs. buy, and why AI inside the martech stack is becoming a checkbox, like a spare tire

- Rapid Fire: the collegiate wrestler who wants to be a CEO, and why Frodo is his model for quiet leadership

CHAPTERS

0:00 Intro

2:18 Infinidat, Lenovo, and a 15-person marketing team

5:45 What AI changed — and why it's still just a tool

7:08 How open-source AI exposes your IP

9:34 Bulldozer parts at the Corolla factory

11:43 Trust but verify — and the wrong Eric Herzog

13:14 Where the ROI shows up: content and competitive

15:39 Verification still cuts the time in half

17:22 Workflow automation and the AI-guided website

19:02 Why the designer still beats AI on slides

21:24 Playing to your audience: CIOs and King Oberon

23:24 Build vs. buy

25:46 AI as a checkbox: the spare-tire argument

28:53 Rapid Fire Five

37:18 How to reach Eric

Connect with Eric Herzog on LinkedIn, or learn more about Infinidat at infinidat.com.

This episode is sponsored by [Common Room](https://www.commonroom.io/?utm_source=goat-podcast&utm_medium=partner), the AI GTM Intelligence Platform trusted by teams like Okta, Anthropic, and Fireworks AI.

To hear more on what AI and agents are actually doing inside real go-to-market organizations, subscribe to The GOATS of Growth on Apple Podcasts, Spotify, or YouTube.

Lance Hodgson thinks a huge slice of the economy — construction, oil and gas, mining, manufacturing — is about to skip SaaS entirely and jump straight to AI. Not because these industries got faster at digitizing, but because AI removes the reason they needed the in-between step.

Lance is CRO at Sitemate (formerly Construction Cloud), the AI platform for what he calls "the built world." He joined as the company's first go-to-market hire and worked every seat on the way up — marketing, VP revenue, GM of Oceania, and now CRO — including years running the sales funnel himself after his own "negative perception of sales" wore off. Before Sitemate he was in marketing at Sony Pictures and CBS.

Internally, Sitemate runs four categories of AI agents — workflow, intelligence, coaching, and collateral — built by Lance's chief of staff, who does the building while he defines the go-to-market logic. The team now ships three to four new agents a week. The intelligence agents are the ones he can't function without: instead of combing through call recordings by hand, agents now surface who's following the playbook — turning "high viz" from an aspiration into something that actually runs.

The clearest signal of AI's impact isn't GTM headcount — Sitemate is still hiring SDRs, AEs, and CSMs at close to the same pace. It's ops. RevOps hiring has slowed sharply, because a chief of staff plus a few agents now does work that used to need a growing team under him. Engineering keeps hiring aggressively — the agents just make them ship faster.

WHAT WE COVER

• Why the built world (construction, oil & gas, mining, manufacturing) might skip straight to AI, bypassing the SaaS step most industries went through
• Sitemate's four categories of agents — workflow, intelligence, coaching, collateral — and which one Lance can't live without
• How a chief of staff, not a platform, became the unlock for shipping 3–4 new agents a week
• Where AI has and hasn't touched headcount: flat in GTM, down in RevOps, still climbing in engineering
• Lance's unconventional path to CRO — Sitemate's first go-to-market hire, who'd never carried an AE quota
• Why "everyone can build agents now" and the real bottleneck is deciding which ones and what context to feed them

CHAPTERS
0:00 Intro
1:43 Sitemate and the built world, defined
3:51 Skipping straight from paper to AI
5:13 Why Lance bet on Sitemate: the TAM math
9:42 From marketer to accidental salesperson
10:53 Building the systems: the VP of Revenue years
13:36 Redefining what sales actually is
20:28 Rolling out AI: the four agent categories
22:31 Documentation first: mapping the funnel before building agents
29:38 Intelligence agents: "High Viz on steroids"
32:43 The chief of staff model: shipping 3-4 agents a week
38:23 "Everyone can build agents" — the real bottleneck
40:50 Headcount: steady in GTM, thinner in ops
44:44 Rapid Fire Five
48:33 Bonus question: lessons from "failures," not perfection

Connect with Lance Hodgson on LinkedIn (linkedin.com/in/lancehodgson), or learn more about Sitemate at sitemate.com.

This episode is sponsored by Common Room, the AI GTM Intelligence Platform trusted by teams like Okta, Anthropic, and Fireworks AI.

To hear more on what AI and agents are actually doing inside real go-to-market organizations, subscribe to The GOATS of Growth on Apple Podcasts, Spotify, or YouTube.

Ben Keighran got frustrated that Claude couldn't search LinkedIn. So he built a dataset larger than LinkedIn's and pointed AI at it.

It's over a billion people, 10 billion records, 75 million updates a day. Not scraped — crawled from public profiles, sourced through 50 data partnerships, enriched with inference tokens. Available inside Claude and your own agents for eight dollars.

Ben is a four-time founder whose products have reached over a billion people. He started coding at 13 in Australia, moved to Silicon Valley in 2006, built Chomp — the app search engine Apple acquired and turned into App Store search — then Caffeine, a live streaming platform that grew to 61 million users. Super Carl is his fourth.

But the database isn't the moat, and he says so. He expects everyone to have one eventually. The thing he thinks is durable is the relationship intelligence layer on top: you upload your own contact exports, Super Carl works out who you actually know among the matches, and the AI tells you who to ask for the intro. Not scraped, not read — metadata only, and you can export or delete it whenever.

Jay uses it to run his own recruiting agent. Searches that used to take weeks now finish in twenty minutes, which is why he spends part of this episode complaining that eighteen minutes feels slow.

WHAT WE COVER

- How Ben picks problems: watch a medium shift, then ask who won the last one and why they probably won't win this one

- Why Chomp existed — apps had no metadata, so you had to type "Facebook" to find Facebook

- The Instagram App Store chart hack, and what it teaches about distribution timing

- Build the product first, then build the business — and how you know you've crossed over

- The three things blocking AI from your network: walled gardens, disconnected graphs, and cold outreach getting worse

- Why the billion-record dataset is a diminishing advantage

- What "your data stays yours" actually means in practice

- Rapid Fire 5: play-based learning, Steve Jobs, and the skateboard injuries

CHAPTERS

00:00 The show is looking for a sponsor

00:27 Chomp, Caffeine, and a billion people

02:36 How Ben decides what to build

06:06 You had to type "Facebook" to find Facebook

06:50 Finding the first users

09:46 The Instagram App Store chart hack

11:07 Why Claude couldn't search LinkedIn

16:39 Walled gardens, broken graphs, dead cold outreach

18:04 Building a data set bigger than LinkedIn's

20:41 The relationship intelligence layer

22:35 Why the database is a diminishing advantage

26:46 Product fit first, then build the business

31:02 Rapid Fire 5

36:01 Bonus question: what's your biggest learn?

39:10 Where the name Super Carl came from

Find Ben at supercarl.ai — free account in ten seconds, or book a meeting with him directly from the homepage.

This episode is unsponsored. If you want to reach GTM leaders and founders who ignore your emails, reach out: [email protected]

Subscribe wherever you listen, and tell someone who'd like it.

"No data, no agent. Bad data, bad agent. Good data, good agent." Cris Mendes thinks data readiness is the number one reason the agentic enterprise fails, and he has the receipts — he ran the experiment on his own team before he sold it to anyone else.

He's VP of Revenue at Momentum.io, acquired by Salesforce in large part because of the go-to-market motion he built there. Before that: Drift, Airtable, and LogMeIn. Before all of it, U.S. Navy search and rescue swimmer. I've known him almost twenty years and introduced him to LogMeIn myself, so take the enthusiasm here with whatever salt you need.

His argument is that everyone is buying agents on top of a data layer that was never built. The average customer conversation moves about eight thousand words; maybe eight of them make it into the CRM. Companies don't have a first-party data layer, they have a human-input data layer — and that's what every agentic project quietly runs aground on.

The story worth the listen: Momentum had a brutal drop-off after the first call. Rather than sampling a few calls and guessing at messaging or ICP, Cris pushed 800 first calls through deep research and got one answer back — the reps weren't prescribing a next step. So he had Momentum listen to the last ten minutes of every call and fire a clip into Slack whenever an AE either didn't prescribe or tried and missed. With humor, so it didn't read as surveillance. Prescription went through the roof. As he puts it: a team respects what you inspect, but you can't inspect everything — unless the inspecting is automated.

WHAT WE COVER

- Selling shovels in a gold rush, and why structured data was the bet

- Why "not everything called an agent is an agent" — deciding vs. automating

- The last ten minutes of a sales call as sacred ground, and how to enforce that at scale

- Bringing AI into QBRs: "it got so real so fast" — no more recency bias and self-preservation dressed up as a forecast

- 10x growth six quarters running, zero attrition, 95% of the team over quota — and why he credits hiring over product

- Whether AI cuts headcount: expands capacity, thins middle management, and the ~10% of companies that are actually AI-forward are hiring hardest

- Auto-advancing pipeline stages on deal milestones instead of trusting a rep's guess — "otherwise you're just buying something like Clari to miss your forecast very expensively"

- His IDEC hiring model, and why he'd trade relevant experience for intrinsics every time

CONNECT WITH CRIS

LinkedIn: Cris Mendes — Cris with no H, Mendes with an S

Momentum.io

---

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CHAPTERS

00:00 Twenty years, and a Navy rescue swimmer

03:39 Selling shovels in a gold rush

04:13 "No data, no agent"

05:33 Is it deciding, or just automation?

07:07 Running Momentum on Momentum

08:33 800 first calls, one missing skill

10:27 You respect what you inspect

14:46 Does AI actually cut headcount?

20:41 Killing hype in the QBR

22:05 Zero attrition, 95% over quota

25:57 8,000 words spoken, 8 reach the CRM

31:43 Auto-advancing deal stages on milestones

35:44 Orchestrate, or get orchestrated out

38:39 IDEC: how Cris hires

40:53 How to reach Cris