---
title: "Why AI Means More RevOps Hires, Not Fewer"
episode: 95
podcast: "The LeanScale Podcast"
publisher: "LeanScale"
guest: "Jimmy O'Halloran"
guest_title: "VP of Go-To-Market Strategy & Operations"
date_published: 2026-07-20
date_modified: 2026-07-22
duration: 01:03:47
word_count: 10155
topics: ["revenue-operations", "sales-enablement", "consumption-revenue", "forecasting", "ai-in-gtm", "sales-compensation"]
canonical_url: https://leanscale-knowledge-hub.netlify.app/podcast/jimmy-ohalloran-new-relic-revops-consumption-revenue/
source: "LeanScale Podcast Knowledge Hub — https://leanscale-knowledge-hub.netlify.app"
license: "Free to quote and cite with attribution to The LeanScale Podcast."
---

# Why AI Means More RevOps Hires, Not Fewer

_Jimmy O'Halloran on the operator's playbook for RevOps, sales enablement, and consumption revenue_

**Episode 95 · The LeanScale Podcast**  
Jimmy O'Halloran, VP of Go-To-Market Strategy & Operations (New Relic) · Hosted by Anthony Enrico  
Published July 20, 2026 · Updated July 22, 2026 · 01:03:47  
Canonical: https://leanscale-knowledge-hub.netlify.app/podcast/jimmy-ohalloran-new-relic-revops-consumption-revenue/

**Topics:** Revenue Operations · Sales Enablement · Consumption Revenue · Forecasting · AI in GTM · Sales Compensation


## Executive summary

The prevailing 2026 narrative is that AI plus a couple of GTM engineers has quietly shrunk the RevOps org. Jimmy O'Halloran — VP of Go-To-Market Strategy & Operations at New Relic, where he owns the full Revenue Operations org and sales enablement — thinks that logic runs exactly backwards. Hand him a tool that makes one operator 300% more productive and he won't cut two heads; he'll hire two more and go faster. This conversation with LeanScale co-founder Anthony Enrico is a rare, unhedged operator's tour of what actually makes a revenue organization work at scale.

Jimmy's credibility comes from proximity, not a bag. Across nearly two decades — 13 years at EMC and Dell (after literally recruiting himself out of a staffing job), four and a half years running field operations at Snowflake, a stint helping Glean cross from startup-mode ops toward enterprise scale, and now New Relic — he has lived next to revenue without ever carrying quota. That vantage point is the spine of the episode: RevOps splits into two flavors, the back-office crowd that never touches the field and the operators who live between the machine and the sales team, and only one of them ever gets invited into the room.

The middle of the conversation is a clinic on consumption revenue — the model almost every AI company is now forced into for economic reasons, and the one most teams underestimate. Jimmy walks through why acquisition is a process, not an event; why you should never 'land at scale' (the 18-wheeler-versus-lawnmower problem); how to structure hunter/farmer teams and pay them without one side interloping on the other; why consumption forecasting belongs to a centralized data-science function owned by finance, not go-to-market; and how to define an ARR number you can actually raise venture capital against. His Snowflake consumption forecast, he notes, called quarterly results within a couple of percentage points at the aggregate — and was 'all over the place' at the account level, which is exactly the point.

The throughline that ties it together is a career-defining reframe: the difference between leverage and trust. Early on, Jimmy forced his way into leadership rooms by making himself the indispensable oracle — that's leverage. It didn't build influence. Real influence came when sales leaders trusted him enough to invite him in and hand him information they'd share with no one else. That makes RevOps a strange seat: on the leadership team, but annexed from it — privy to more than your peers, and responsible for stewarding that information with judgment. Tools are great, Jimmy says; the human beings are more important.

Who should listen: RevOps and GTM operators who want a seat at the table, founders and CROs designing a consumption go-to-market, finance leaders wrestling with usage-based forecasting and quota design, and anyone trying to separate the real AI-in-GTM signal from the LinkedIn theater. The biggest outcome of the hour is a mental model for where operators create leverage — by humanizing a job that is fundamentally about human interaction — and a contrarian, evidence-based take on why productivity gains should grow revenue teams, not gut them.


## Key takeaways

1. **A 300% productivity gain should trigger hiring, not layoffs** — Jimmy rejects the 'AI shrinks the org' thesis on its own logic: if a tool makes one person three times more productive, the rational move for a growth-hungry business is to add capacity and go faster, not to bank the savings and hold output flat. He hasn't seen credible, trusted testimonials that AI replaces operators — mostly 'AI-generated slides on LinkedIn.'
   _Why it matters:_ Frame AI investment as a throughput multiplier against an unbounded growth appetite, not a headcount-reduction lever. The teams that compound are the ones that pair strong operators with tools and press the accelerator.
   _For:_ RevOps Leaders, Founders, Revenue Executives

2. **There are two flavors of RevOps — only one gets invited into the room** — One flavor lives in the back office fixing Salesforce, redoing quotas, and taking tickets; the other lives in the field, between the machine and the sales team, injecting value into forecast calls, campaigns, and programs. Jimmy found his lane as the second kind almost by accident at EMC and never left it.
   _Why it matters:_ Decide deliberately which flavor you are building. Influence, budget, and a seat at the leadership table accrue to field-facing operators — back-office-only teams get stereotyped as ticket-takers.
   _For:_ RevOps Leaders, Revenue Executives

3. **Build your operating cadence to mirror how the customer buys** — The way a customer consumes you — awareness, consideration and decision, implementation, value realization — should be the way you operate internally. Map programs, staffing, and metrics onto those four phases and roles, expectations, and measurement all fall into place.
   _Why it matters:_ Reverse-engineer your internal cadence from the customer journey rather than from your org chart. It surfaces where you're over- or under-resourced and gives every function a clear role in the same process.
   _For:_ RevOps Leaders, Sales Leaders, Marketing Leaders

4. **Sales enablement is the secret sauce — and almost nobody staffs it correctly** — Enablement breaks when it's over- or under-staffed: too few people can't enable a large field, too many become an 'albatross' the field resents. Jimmy budgets it zero-based ('tell me what you want, in waves, forget your last company') and builds it as a living sales academy with distinct learning paths for new hires, high-potentials, and 20-year veterans.
   _Why it matters:_ Treat enablement as a competency web around a $1–2M-quota AE, not a content factory. The special sauce is new-hire energy, hi-po programs, and human connection — not which methodology you badge.
   _For:_ Sales Leaders, RevOps Leaders

5. **Build formal enablement once span of control passes 5–6 (earlier if you sell enterprise)** — The trigger is a frontline manager's span of control crossing five or six reps, and the complexity of what you sell. Enterprise motions need it early — maybe not headcount at first, but strategy and consultancy, the same way RevOps needs strategy before it needs hands.
   _Why it matters:_ Don't wait for fires. If you sell complex, high-consideration products, stand up enablement strategy well before you can justify a full team, or you'll live and die by the uneven talent of your sales leadership.
   _For:_ Sales Leaders, Founders

6. **In consumption, acquisition is a process, not an event** — A signed PO is where the work starts, not ends. Revenue doesn't recognize until the customer actually uses the product, so the whole game after signature is driving adoption and demonstrated value — a fundamentally different job than closing.
   _Why it matters:_ Design roles, comp, and cadence around post-signature adoption. Treating the signature as the finish line is the single biggest mistake legacy companies make when they pivot to usage-based revenue.
   _For:_ RevOps Leaders, Customer Success, Founders

7. **Don't land at scale — start a lawnmower, not an 18-wheeler** — Landing big invites risk: heavy expectations, unused credits, and an ugly end-of-term conversation. Jimmy's motion is land small (a $10k 'paid pilot'), prove value fast, run a six-month 'double-tap' true-up, then bridge to the 12-month renewal, which is really the first real deal.
   _Why it matters:_ Right-size the land to what the customer can actually consume. Running out of credits is a good conversation for both sides; over-scoping manufactures churn and destroys trust.
   _For:_ Sales Leaders, Customer Success, Founders

8. **Acquisition and install reps must carry different numbers — and probably no consumption quota on the hunter** — Setting quota starts with a hard conversation between go-to-market and finance about who carries consumption and who doesn't. Jimmy is a proponent (against many finance leaders) of acquisition sellers not carrying consumption quota, then building a bookings plan for hunters and a consumption plan for farmers, layered with spiffs and target-incentive mixes.
   _Why it matters:_ There is no silver bullet — comp must be customized to your product and consumption behavior. A reckoning is coming for legacy variable-pay models in a token/usage world; expect to iterate ('keep chopping wood').
   _For:_ RevOps Leaders, Sales Leaders, Revenue Executives

9. **Consumption forecasting is a data-science job owned by finance, not go-to-market** — Sellers cannot tell you how a customer will consume a product — 'it's never going to happen.' Go-to-market's job is to bring back the raw materials (account plans, commercial events, product releases, macro signals like the World Cup or Black Friday); a centralized data-science function turns those into a forecast. At Snowflake that org sat under a Chief Data & Analytics Officer.
   _Why it matters:_ Don't hire a bespoke data-science team inside RevOps — every function will get different answers to the same question. Centralize it, influence where it points, and hold sales accountable for bending the curve, not predicting it.
   _For:_ Revenue Executives, RevOps Leaders, Founders

10. **Define an ARR you can actually raise against — and expect the RPO conversation** — In a fundraise, investors want recurring revenue, and you won't want to discount consumption down to only contracted revenue. You need a bulletproof definition that folds consumption into ARR, which quickly pulls you into RPO (remaining performance obligation). Anyone claiming to have all the answers here is 'posturing.'
   _Why it matters:_ Invest early in a defensible ARR definition and the data lineage behind it. It's a cross-functional agreement between product, marketing, finance, and go-to-market about who owns what — without it, the number won't survive diligence.
   _For:_ Founders, Revenue Executives

11. **Own less, influence more — hire mercenaries, not jacks-of-all-trades** — The younger Jimmy wanted to maximize the 'O'Halloran kingdom.' The mature version wants a small set of specialists with very specific targets, plus control and influence over the whole — not direct ownership of functions like data science he isn't equipped to govern. 'We have to make humans superhumans,' and you do that with focus, not a pile of responsibilities.
   _Why it matters:_ Resist empire-building. Clarity on what you own and what you're great at — and knowing when to pull others in — is a marker of a secure, senior operator and a better use of scarce resources in an AI-driven world.
   _For:_ RevOps Leaders, Revenue Executives

12. **Operators win on trust, not leverage — get invited to the table** — Early on, Jimmy forced his way into rooms by making leaders unprepared without him. That's leverage, and it doesn't build influence. Trust does: bringing unsolicited insight ('this rep always slips 20% from this number — try this'), stewarding sensitive information from meetings sales isn't in, and humanizing a job that is fundamentally about human interaction.
   _Why it matters:_ Get off the ticket hamster wheel and invest in human moments. Reputation as the person you want in the foxhole at end-of-quarter is what moves you from RevOps director to the CRO's right hand.
   _For:_ RevOps Leaders, Revenue Executives, Sales Leaders

13. **You're on the leadership team, but annexed from it — steward the information** — RevOps reports to the CRO but doesn't get the CRO's peer conversations; simultaneously it knows more than most direct reports and hears things in rooms sales never enters. That asymmetry is powerful and dangerous — deploy it to help ('put your arm around someone'), never to harm.
   _Why it matters:_ Treat information as a fiduciary responsibility. Knowing when to share, when to hold, and how to give a struggling leader a little more than their boss did is the soft-skill edge that makes an operator indispensable.
   _For:_ RevOps Leaders, Revenue Executives

14. **AI still needs a sanitized playground — garbage in, garbage out** — Jimmy is bullish on arming RevOps with AI but skeptical of the hype: even well-funded companies that spend heavily on data cleanliness don't have clean data, and AI requires heavy oversight and a sanitized dataset to draw from. He also flags the tribal-knowledge risk — automate away the people who built your workflows and you can't fix the workflows when they break.
   _Why it matters:_ Invest in data cleanliness and human oversight before expecting durable AI leverage, and keep the people who understand how the systems were built. Watch the cost curve too — AI feels cheapest right now, and labor-vs-tools math will shift.
   _For:_ RevOps Leaders, Founders, Revenue Executives


## Frameworks

### The Two Flavors of RevOps (01:20)

**Definition:** A split between back-office RevOps (systems, process, tickets, quota fixes — never touches the field) and field-operator RevOps (lives between the sales team and the machine, injecting value into forecast calls, campaigns, and programs).

Jimmy argues the field-operator flavor is the one that earns influence and a seat at the table; back-office-only teams get stereotyped as ticket-takers. Deciding which you are shapes staffing, time allocation, and career ceiling.

### Operating Cadence That Mirrors the Customer Journey (05:42)

**Definition:** Build your internal operating rhythm around the four phases of how a customer consumes you — awareness, consideration & decision, implementation, and value realization — rather than around your org chart.

Each phase implies specific programs, owners, and metrics (e.g., marketing owns awareness; forecasting lives in consideration/decision). Mirroring the buyer's journey brings clarity to roles, expectations, and where you're over- or under-resourced.

### Enablement as a Competency Web (Zero-Based) (10:28)

**Definition:** Staff sales enablement by treating every non-quota role (managers, RevOps, SEs, enablement) as overhead wrapped around a $1–2M-quota AE, and asking what each role gives back. Budget it zero-based and build it as a living sales academy, not a content factory.

Over- or under-staffing is why enablement fails. The 'secret sauce' is new-hire energy, hi-po and leadership programs run concurrently, and distinct learning paths — human connection over methodology badges.

### The Span-of-Control Trigger for Enablement (18:29)

**Definition:** Stand up formal enablement once a frontline manager's span of control passes five or six reps — earlier if you sell complex, enterprise, high-consideration products.

The right time is a function of sales-org and customer maturity. Enterprise plays need enablement strategy (if not headcount) early, or you live and die by the uneven talent of your sales leadership.

### Hunter/Farmer in a Consumption Model (22:09)

**Definition:** Split the field into hunters who acquire new logos (traditional sales path) and farmers who grow the install base aggressively, then engineer the bridge so neither feels the other is interloping.

At scale, consumption revenue needs both motions and a clean handoff narrative to the customer ('I get you in and prove value; then you get more resources from the install team'). The hard part is how you pay them.

### Acquisition Is a Process, Not an Event (23:00)

**Definition:** In consumption revenue, the signed PO is where the work starts. Because revenue recognizes on usage, the entire post-signature job is driving adoption and demonstrated value.

This reframes 'closing' as the beginning of acquisition, not the end, and it's the reality legacy companies most often ignore when they pivot to usage-based models.

### Don't Land at Scale (Lawnmower, Not 18-Wheeler) (24:22)

**Definition:** Land small as a paid pilot, prove value fast, run a ~6-month 'double-tap' true-up, then bridge to the 12-month renewal — which is really the first real deal.

Landing big invites risk: heavy expectations and unused credits that create an adversarial end-of-term conversation. Starting small and ramping value keeps the relationship organic and the renewal friendly.

### Consumption Quota Design (31:14)

**Definition:** Acquisition and install reps carry different numbers; acquisition sellers ideally carry no consumption quota. Build a bookings plan for hunters and a consumption plan for farmers, layered with spiffs and target-incentive mixes.

There's no silver bullet — comp must be customized to product and consumption behavior, and requires go-to-market and finance to share the mission. Expect to iterate as a reckoning hits legacy variable-pay models.

### Consumption Forecasting = Centralized Data Science (Owned by Finance) (37:23)

**Definition:** Go-to-market gathers raw materials (account plans, commercial events, product releases, macro signals); a centralized data-science function owned by finance turns them into a forecast. Sellers cannot predict consumption.

The 'grocery store problem': you know what a customer bought, not whether they'll eat, freeze, or donate it. Centralizing data science avoids every function computing a different answer; a strong aggregate model can call quarterly results within a couple points.

### Leverage vs. Trust (51:10)

**Definition:** Leverage is forcing your way into the room by making leaders unprepared without you. Trust is being invited in because sales leaders want you there. Only trust builds durable influence.

You earn trust by bringing unsolicited insight, humanizing the job, and stewarding sensitive information well. It's what separates a RevOps director from the CRO's right hand — and it can't be faked with hard skills.

### On the Leadership Team, But Annexed From It (55:34)

**Definition:** RevOps sits in a strange seat: reporting to the CRO but excluded from the CRO's peer conversations, while simultaneously knowing more than most of its peers and hearing things in rooms sales never enters.

That information asymmetry is powerful and dangerous. Stewarding it — knowing when to share, when to hold, and how to help a struggling leader — is the fiduciary soft skill that makes an operator indispensable.

### AI Makes Humans Superhuman → More Hires, Not Fewer (57:49)

**Definition:** AI is a productivity multiplier that requires clean data and human oversight. A productivity gain should be reinvested in more capacity to go faster, not banked as headcount reduction.

Jimmy rejects the 'AI replaces operators' narrative for lack of trusted evidence, flags garbage-in/garbage-out data reality and tribal-knowledge loss, and notes AI's cost will rise from today's lows.


## Quotes

_Speakers inferred from an undiarized transcript — verify before attributing._

> "If you tell me my one person can start giving me a 300% output, that doesn't mean I'm going to lay off two people. It probably means I'm going to hire two more people, because all I want to do is move faster."
>
> — Jimmy O'Halloran, The LeanScale Podcast Ep. 95 (58:41)

> "What I learned over time was that that's leverage, that's not trust. And if your sales leader trusts you, you will be invited into the room."
>
> — Jimmy O'Halloran, The LeanScale Podcast Ep. 95 (51:10)

> "How a customer consumes you as a company should be how you operate."
>
> — Jimmy O'Halloran, The LeanScale Podcast Ep. 95 (05:42)

> "Sales enablement is the secret sauce if you do it appropriately. The reason it falls apart so quickly is either companies overstaff it or understaff it."
>
> — Jimmy O'Halloran, The LeanScale Podcast Ep. 95 (09:05)

> "If you're going to run a hunter/farmer model, it's about accepting that acquisition is a process, not an event."
>
> — Jimmy O'Halloran, The LeanScale Podcast Ep. 95 (22:48)

> "In the consumption game, what you're trying to do is not land at scale, because that just invites risk. It's kind of like trying to start an 18-wheeler versus start a lawnmower."
>
> — Jimmy O'Halloran, The LeanScale Podcast Ep. 95 (25:01)

> "Sellers can't give you insights on how your customer is going to consume your product. It's not going to happen. It's never going to happen."
>
> — Jimmy O'Halloran, The LeanScale Podcast Ep. 95 (38:58)

> "You're basically asking a grocery store when their customers are going to eat your food."
>
> — Jimmy O'Halloran, The LeanScale Podcast Ep. 95 (43:15)

> "I don't want a bunch of jack of all trades working for me. I want mercenaries who have very specific sets of skills — and then I want control and influence over all of it."
>
> — Jimmy O'Halloran, The LeanScale Podcast Ep. 95 (47:29)

> "We have to make humans superhumans. And you don't do that by giving them a bunch of responsibilities. You do that by giving them a very specific set of responsibilities that aligns really well to what they're good at."
>
> — Jimmy O'Halloran, The LeanScale Podcast Ep. 95 (48:14)

> "I can't teach you how to have a hard conversation with a rep. I can't teach you how to tell somebody that makes three times as much as you that they're not getting paid on something. That is its weight in gold in our world."
>
> — Jimmy O'Halloran, The LeanScale Podcast Ep. 95 (54:05)

> "You're in this weird space where you're a part of the leadership staff, but you're annexed from it."
>
> — Jimmy O'Halloran, The LeanScale Podcast Ep. 95 (56:17)

> "Tools are great. The human beings are more important."
>
> — Jimmy O'Halloran, The LeanScale Podcast Ep. 95 (57:03)

> "It requires a very sanitized playground for it to draw from... it's not clean, so it's garbage in, garbage out."
>
> — Jimmy O'Halloran, The LeanScale Podcast Ep. 95 (58:00)

> "If you're giving me a tool to make me run faster, my gut reaction is not to remove productive people from the org. It's to hire more and to go faster."
>
> — Jimmy O'Halloran, The LeanScale Podcast Ep. 95 (59:09)

> "Give me smart people and give me a mission, and I'll find my way home and I'll use whatever tools I can get. But if you're telling me people are tools, I'll take the people all day."
>
> — Jimmy O'Halloran, The LeanScale Podcast Ep. 95 (01:00:59)

> "Part of it is knowing how you got there. And if you don't know how you got there, you don't know how to fix it. That's my biggest paranoia around it."
>
> — Jimmy O'Halloran, The LeanScale Podcast Ep. 95 (01:01:46)

> "There's no satiating the human race's appetite to consume and to grow and to want to do more and do it faster."
>
> — Anthony Enrico, The LeanScale Podcast Ep. 95 (59:31)


## Practical advice by role

### Founders

- If you're pivoting to (or born into) a consumption model, budget for it: it is materially more expensive and data-intensive to run than legacy SaaS. Fund the data science before you need the forecast.
- Nail a defensible ARR definition that folds in consumption early — you'll need it bulletproof for diligence, and it drags in the RPO conversation. Don't wait for the raise to figure it out.
- Stand up enablement strategy (not necessarily headcount) early if you sell a complex product; don't outsource your reps' success to the luck of your sales leadership's talent.

### RevOps Leaders

- Force yourself into the field: set recurring 'tent-pole' touchpoints (forecast calls, parachuting into work streams) so you have a live pulse on the business and the context to make good systems decisions.
- Get off the ticket hamster wheel — humanize the job. Bring unsolicited insight to sales leaders and you'll be invited to the table instead of forcing your way in.
- Own less, influence more. Don't build a bespoke data-science team inside RevOps; centralize it, influence where it points, and resist empire-building.
- Steward information like a fiduciary: you'll know more than your peers and hear things sales won't — deploy it to help, never to harm.

### Sales Leaders

- Design the customer journey deliberately (awareness → consideration/decision → implementation → value realization) and staff each phase — it's the backbone that makes enablement and RevOps effective.
- Build enablement zero-based as a living sales academy with distinct paths for new hires, high-potentials, and veterans; run hi-po and new-hire programs concurrently for talent synergy.
- In consumption, don't land at scale — land small, prove value, double-tap at ~6 months, and treat the 12-month renewal as the first real deal.

### Marketing Leaders

- Own the awareness phase explicitly within the shared operating cadence, and hand off cleanly to sales at the awareness-to-consideration boundary with agreed programs and MQL→SQL definitions.
- Feed the forecasting machine: campaigns, commercial events, and macro signals (World Cup, Black Friday for retail-heavy products) are raw materials the data-science function needs to predict consumption.

### Customer Success

- Post-signature is where acquisition actually begins in consumption — your job is speed-to-value and adoption, not just retention.
- Right-size expectations: running out of credits is a healthy value conversation; over-scoped lands manufacture churn. Bridge customers to renewal with an organic, professional transition narrative.

### Revenue Executives

- Have the hard cross-functional conversation with finance about who carries consumption quota and who doesn't — and expect to iterate; nobody has nailed consumption comp.
- Make consumption forecasting a centralized data-science exercise owned by finance; hold go-to-market accountable for bending the curve, not predicting it.
- Reinvest AI productivity gains into capacity. If a tool triples an operator's output, add people and go faster rather than banking the savings.


## AI takeaways

**Thesis:** AI is a powerful tool that makes the humans who harness it more powerful — not a substitute for them. The correct response to a productivity gain is to add capacity and go faster, not to reduce headcount.

- **Productivity should grow the team** — A 300% output gain from one operator triggers +2 hires, not −2. There's no satiating a growth-hungry company's appetite to move faster — so reinvest the leverage.
- **Garbage in, garbage out** — AI needs a sanitized playground and heavy oversight. Even well-funded companies that spend big on data cleanliness don't have clean data — invest there before expecting durable AI leverage.
- **Protect tribal knowledge** — Automate away the people who built your work streams and you can't fix those work streams when they break. Knowing how you got there is how you fix it.
- **Watch the cost curve** — AI feels cheapest right now (Anthony: turned Fable off after seeing token usage). When AI-built and human-built cost the same, reliability and accountability tip back toward people.
- **Signal vs. theater** — Jimmy discounts most AI-replaces-ops claims as AI-generated LinkedIn slides without trusted testimonials — and notes the frontier labs themselves are still hiring GTM, ops, sales, and marketing.

**Agent & automation ideas**

- Arm RevOps operators with AI to raise per-person throughput (data hygiene, account-plan drafting, forecast raw-material assembly) — then add headcount to compound the speed.
- Consumption forecasting is an aggregate-model problem: an AI/data-science layer that ingests commercial events, product releases, security events, and macro signals to project usage curves by segment.
- Enablement copilots that personalize learning paths for new hires vs. hi-pos vs. veterans, so a thin team can enable a large field.


## Operations takeaways

### Revenue operations

- **Operating cadence.** Mirror the customer journey (awareness → consideration/decision → implementation → value realization); it dictates how you staff and where you spend time.
- **Overhead optimization.** Every non-AE role is overhead around a $1–2M-quota seller — justify each by what it gives back, starting with the SE (easiest ROI).
- **Centralize intelligence.** Don't own data science inside RevOps; centralize it so product, finance, and go-to-market work from one source of truth.
- **Own less, influence more.** Hire specialists with narrow targets; keep control and influence over the whole rather than direct ownership of everything.
- **Trust over leverage.** Get invited to the table by bringing insight and stewarding information — not by making leaders dependent on you.
- **Information stewardship.** You're annexed from leadership yet know more than peers; deploy that asymmetry to help, with judgment.

### Pipeline & marketing ops

- **Awareness is marketing's phase.** Marketing owns awareness within the shared cadence — pipeline, campaigns, and programs — then hands to sales at the consideration boundary.
- **Clean lifecycle progression.** Define how opportunities move MQL → SQL → sales opportunity, and which tool manages stages vs. forecasting.
- **Acquisition data is the truth serum.** Enablement quality shows up most clearly in acquisition and ramp data, before it gets muddied in the install base.
- **Feed the forecast.** Campaigns and commercial/macro events (World Cup, Black Friday) are raw materials the data-science function needs to predict consumption.

### Customer operations

- **Acquisition doesn't end at signature.** In consumption, the work starts when the PO is signed — speed-to-value and adoption are the job.
- **Renewal is the first real deal.** Year one is a paid pilot; the 12-month renewal is where the real commitment forms.
- **Don't over-scope.** Running out of credits is a healthy value conversation; over-scoped lands manufacture churn and adversarial end-of-term talks.
- **Organic transitions.** Hand customers from hunter to farmer with a customer-friendly narrative, not a territory-reshuffle surprise.
- **Farmer resourcing.** Give install-base specialists the resources to elevate conversations with large, growing accounts.


## Metrics mentioned

| Value | Metric | Context |
| --- | --- | --- |
| $1M–$2M | Quota per AE | The order of magnitude an enterprise AE carries; the unit you optimize the pod (SE, enablement, RevOps) around. |
| $10K land vs. $150–200K | Land size vs. avg deal | In consumption, land small relative to your average enterprise ACV and expand into it — don't land at scale. |
| 5–6 reps | Span-of-control trigger | Once a frontline manager exceeds five or six reps, it's time to build formal enablement (earlier for enterprise). |
| 12 months | First renewal | The 12-month renewal is really the first real deal; year one behaves like a paid pilot. |
| Hire #28 · $4M → $58M · ~$500M exit | Emailage trajectory | Jimmy's case study that even a natively usage-based company faces new, bigger problems at every phase of scale. |
| Within a couple of points, quarterly | Snowflake forecast accuracy | A strong aggregate consumption model called quarterly finish within a couple percentage points — while being 'all over the place' at the account level. |
| 300% → +2 hires | Productivity gain | A 3x productivity gain should trigger hiring more people to go faster, not laying two off. |


## Entities mentioned

- **New Relic** (company) — Jimmy's current employer; he is VP of Go-To-Market Strategy & Operations owning the full RevOps org and sales enablement. · https://leanscale-knowledge-hub.netlify.app/company/new-relic/
- **Snowflake** (company) — Where Jimmy ran field operations for ~4.5 years; his reference case for consumption revenue, a centralized data-science org under a Chief Data & Analytics Officer, and a forecast accurate to within a couple points at aggregate. · https://leanscale-knowledge-hub.netlify.app/company/snowflake/
- **Glean** (company) — Jimmy helped Glean make the jump from startup-mode ops toward enterprise scale. · https://leanscale-knowledge-hub.netlify.app/company/glean/
- **EMC** (company) — Where Jimmy recruited himself in from a staffing job and spent the formative years of a 13-year run that found his RevOps lane. · https://leanscale-knowledge-hub.netlify.app/company/emc/
- **Dell** (company) — Acquired EMC; part of Jimmy's 13-year early-career run close to revenue. · https://leanscale-knowledge-hub.netlify.app/company/dell/
- **Emailage** (company) — Jimmy was hire #28 at ~$4M revenue; the company scaled to $58M and exited for roughly half a billion dollars — his case study that even a natively usage-based business hits new, bigger problems at every phase. · https://leanscale-knowledge-hub.netlify.app/company/emailage/
- **Anthropic** (company) — Cited (with OpenAI) as an example of a frontier AI company experimenting with abandoning variable pay for sellers — a move Jimmy doesn't think is right. · https://leanscale-knowledge-hub.netlify.app/company/anthropic/
- **OpenAI** (company) — Cited alongside Anthropic as experimenting with radically different (variable-pay-free) seller compensation in a consumption/token world. · https://leanscale-knowledge-hub.netlify.app/company/openai/
- **Albertsons** (company) — Used as the grocery-store metaphor for centralized data science: the store sees purchases, but the 'mothership' runs the algorithm that predicts and stocks each location. · https://leanscale-knowledge-hub.netlify.app/company/albertsons/
- **Jimmy O'Halloran** (person, guest) — VP of GTM Strategy & Operations at New Relic; ~20 years in RevOps across Snowflake, EMC/Dell, Glean, and Emailage. · https://leanscale-knowledge-hub.netlify.app/guest/jimmy-ohalloran/
- **Anthony Enrico** (person, host) — Co-founder of LeanScale and host of The LeanScale Podcast. · https://leanscale-knowledge-hub.netlify.app/guest/anthony-enrico/
- **Salesforce** (tool, CRM) — Referenced as the CRM system-of-record RevOps is stereotyped as merely 'fixing' — the back-office trap Jimmy warns against.
- **Claude Code** (tool, AI Dev Tool) — Referenced ('cloud code') as the kind of AI tooling that prompts the 'do we even need people anymore?' question Jimmy pushes back on.
- **Fable** (tool, AI Model) — Anthony mentions turning the Fable model off after realizing how many tokens it consumes — a live example of AI's rising cost curve.


## FAQ

**Q: Does AI reduce the number of RevOps hires you need?**

A: No — Jimmy O'Halloran argues the opposite. If AI makes one operator 300% more productive, the rational move for a growth-hungry company is to hire more people and go faster, not to bank the savings as layoffs. He hasn't seen trusted evidence that AI replaces operators, only 'AI-generated slides on LinkedIn.'

**Q: What are the two flavors of RevOps?**

A: Back-office RevOps (systems, process, tickets, quota fixes — never touches the field) and field-operator RevOps (lives between the sales team and the machine, injecting value into forecast calls, campaigns, and programs). Only the field-operator flavor reliably earns influence and a seat at the leadership table.

**Q: How do you set quotas in a consumption revenue model?**

A: Start by deciding, with finance, who carries consumption quota and who doesn't — Jimmy favors acquisition sellers not carrying it. Build a bookings plan for hunters and a consumption plan for install-base farmers, layered with spiffs and target-incentive mixes. There's no silver bullet; expect to customize to your product and iterate.

**Q: Who should own consumption forecasting — go-to-market or finance?**

A: Finance, through a centralized data-science function. Sellers can't predict how a customer will consume a product. Go-to-market's job is to gather raw materials (account plans, commercial events, product releases, macro signals); a centralized model turns them into a forecast that can be accurate within a couple points at the aggregate.

**Q: When should a company build a formal sales enablement team?**

A: Once a frontline manager's span of control passes five or six reps — earlier if you sell complex, enterprise, high-consideration products. Even before you can justify headcount, stand up enablement strategy, or you'll live and die by the uneven talent of your sales leadership.

**Q: What does it mean that 'acquisition is a process, not an event' in consumption?**

A: Because revenue recognizes on usage, a signed PO is where the work starts, not ends. The whole post-signature job is driving adoption and demonstrated value. Legacy companies pivoting to usage-based revenue most often fail by treating the signature as the finish line.

**Q: How do you define ARR you can raise venture capital against in a consumption business?**

A: You need a bulletproof definition that folds consumption into recurring revenue rather than discounting down to only contracted revenue — which pulls you into the RPO (remaining performance obligation) conversation. It requires a cross-functional agreement on who owns what; anyone claiming to have all the answers is posturing.

**Q: What is the difference between leverage and trust in RevOps?**

A: Leverage is forcing your way into the room by making leaders unprepared without you. Trust is being invited in because sales leaders want you there. Only trust builds durable influence — earned by bringing unsolicited insight, humanizing the job, and stewarding sensitive information well.


## Timeline

- **00:00** — Cold open + ground rules
- **02:04** — The two flavors of RevOps
- **02:18** — Recruiting himself into EMC: finding his lane
- **04:19** — Structuring your week to stay in the field
- **06:59** — An operating cadence that mirrors the customer journey
- **10:28** — Sales enablement: the secret sauce
- **21:29** — When to build a formal enablement team
- **24:33** — The real challenges of consumption revenue
- **26:22** — Hunter/farmer: acquisition is a process, not an event
- **29:00** — Don't land at scale: the 18-wheeler vs. the lawnmower
- **34:35** — Setting quotas in a consumption world
- **40:32** — Forecasting & defining ARR you can raise against
- **43:26** — Should data science report to RevOps? The grocery store problem
- **48:15** — The Emailage story: hire #28 to a half-billion exit
- **53:05** — Leverage vs. trust: getting invited to the table
- **59:09** — Part of leadership, but annexed from it
- **01:01:30** — Does AI change everything?
- **01:04:04** — Why more AI means more people, not fewer


## Related episodes

- **Ep. 85: Why AI + GTM Engineers Can't Replace RevOps** (Tessa Whittaker) — The sibling argument to this episode's thesis — AI and GTM engineers don't replace the operating layer. · https://leanscale-knowledge-hub.netlify.app/podcast/tessa-whittaker-ai-gtm-engineers-revops/
- **Ep. 88: Why AI Won't Close Your Biggest Deals** (Michael Kiernan, CRO at Nextdoor) — A CRO's take on the limits of AI in enterprise selling — pairs with Jimmy's signal-vs-theater view. · https://leanscale-knowledge-hub.netlify.app/podcast/michael-kiernan-nextdoor-ai-wont-close-deals/
- **Ep. 91: Why Outcome-Based Pricing Is a Trap for Most AI Companies** (Roee Hartuv) — Pricing & packaging counterpart to the consumption-comp and land-small discussion. · https://leanscale-knowledge-hub.netlify.app/podcast/roee-hartuv-outcome-based-pricing-trap/
- **Ep. 6: Why Your Forecast Is Broken** (LeanScale) — Foundational forecasting episode that Jimmy's consumption-forecasting model builds on. · https://leanscale-knowledge-hub.netlify.app/podcast/why-your-forecast-is-broken/
- **Ep. 2: How to Measure New Business With Usage-Based Pricing** (LeanScale) — The measurement problem underneath 'acquisition is a process, not an event.' · https://leanscale-knowledge-hub.netlify.app/podcast/bernardo-alves-usage-based-pricing/
- **Ep. 15: Where Should RevOps Report?** (LeanScale) — Org-design companion to the 'annexed from leadership' and trust-vs-leverage discussion. · https://leanscale-knowledge-hub.netlify.app/podcast/cameron-legge-where-revops-report/


## Full transcript

_Machine-transcribed and not diarized; speaker attribution is inferred._  
_Transcript only, as a separate file: https://leanscale-knowledge-hub.netlify.app/podcast/jimmy-ohalloran-new-relic-revops-consumption-revenue/transcript.md_

### 00:00 — Cold open + ground rules

**[0:00]** My guest today has spent nearly two decades in the trenches of revenue operations long before anybody called it that. Jimmy O'Halleran is the VP of go-to-market strategy and operations at New Relic, where he owns the full revenue operations org plus sales enablement. Before New Relic, he spent four and a half years at Snowflake as a field operations leader, helped Glean make the jump from startup mode ops toward enterprise scale and cut his teeth over 13 years at EMC and Dell after literally recruiting himself into the company from a staffing job. But what makes Jimmy worth listening to isn't the logos, it's his conviction that

**[0:42]** the entire job comes down to one thing, earning the trust of salespeople and getting invited to the table instead of forcing your way in. In this conversation, we get into how operators actually break into the room, why he thinks the AI hype has RevOps exactly backwards, and the career defining moments most ops people don't even realize they're walking into. Jimmy, you split RevOps into two totally different flavors, the back office crowd that never touches the field and the operators who live between the machine and the sales team. When did you figure out which one you truly want it to be?

**[1:20]** Honestly, it happened on accidents, really. I took, as you hinted at in the beginning, I took a very low level job at EMC. I didn't know what I was doing, I didn't know what career I was getting into. And I was just, I was a grinder. I was driving an hour and 20, an hour and 30 minutes each way into Franklin, Massachusetts, where EMC used to have a massive foothold working all kinds of hours, working with salespeople, working with ops people, with finance people. And I just kind of got addicted to being that close to revenue, but not actually selling. And very quickly understood that I could drive a lot of value in the relationships

### 02:04 — The two flavors of RevOps

**[2:04]** that I had and the efficiencies that I had pushing forward with salespeople gravitated towards working more strategically with them, working on campaigns and programs that they were running after. And I just got the bug and that's where I found my feet. I'm much more effective when I'm working with salespeople as opposed to working on systems or process or anything related to more of the back office functions. It's just kind of my skills are more aligned to that side of the house. Yeah. And I think a lot of people in RevOps or operators, they don't carve out the bandwidth

### 02:18 — Recruiting himself into EMC: finding his lane

**[2:46]** and the time to interact with the team that gives them the data they need and the context they need to make really good systems and process decisions. How do you structure your day, your week, and what does it look like to be doing enough of being in the field with the team? I force myself to make sure that I have like different 10 polls throughout the week across different programs or processes. So I like getting on forecast calls to get a pulse of the business, to get a feel for how things are going, where they're going, how they're feeling. And I typically parachute into different work streams to make sure I have a pulse on

**[3:30]** the business, so to speak. So that's kind of how I prioritize my time and then try to find kind of interesting moments or compelling events where either I or my team can inject value. I have this kind of healthy bordering on unhealthy paranoia about my team's value in making sure that we're just constantly pushing that forward to make sure that we don't fall into the kind of stereotype of rev ops, which is kind of ticket takers. I always want my people to be on the front foot. I always want us to introduce things and make sure that we're questioning and challenging the field in a respectful way to make sure we're pushing

**[4:12]** them forward. And then that works in both directions. I also do that with the teams internally at the company that want that kind of gateway into sales, whether it's HR, finance, project, et cetera. Just making sure that we're not a pass through as an organization or we're not viewed as ticket takers and we fix things that go wrong in Salesforce or we redo a quota or something like that. That's always served me really well, just making sure that value, value, value at all times. Yeah, it's pretty easy to do a lot of the work behind the scenes. Oftentimes rev ops can be a very thankless job because if there are no problems, you're doing your job, but

### 04:19 — Structuring your week to stay in the field

**[5:01]** it doesn't necessarily mean that there's recognition occurring and you don't get a big close one opportunity for doing something in rev ops. So sometimes you don't get the spotlight, but you mentioned you have some tent poles throughout. Do you have like structured one on ones? Do you just have reminders to say, Hey, I should probably slack this person every now and then, or do you get like embedded into certain operational functions that kind of organically give you that interaction with the team? What's the best way? And maybe I could frame it as if someone kind of identifies as, Hey, I feel like I'm more of a back office

**[5:42]** rev ops person. I'm not really spending much time with the field. What are some systems to start doing that? Yeah. So from a, my biggest thing is, is an operating cadence for the business. And then that'll really determine how you staff people. And I actually will determine how you spend your time. Now you're operating cadence for the most part is built for the field. I, I, I live by the ethos ethos that like how a customer consumes you as a, as a company should be how you operate. That way, like your customer's journey, whether it's through kind of awareness, consideration and decision, implementation, value realization, kind of those four phases,

**[6:28]** you should build your operating process around those things. So like, for example, awareness, like how are you looking at pipeline? What kind of campaigns are you running? How successful are they? What kind of programs do you have associated with that? And then if you break that down for most sales engagement perspective, okay, this is how we're going to drive sales into this, this work stream. This is how marketing is going to partner. Cause obviously marketing, that's kind of where they play for the most part is, is in the awareness phase. And then how do you carry them from awareness to consideration and decision, which is generally your, your

### 06:59 — An operating cadence that mirrors the customer journey

**[7:04]** kind of stereotypical sales process, right? We've got an opportunity that went from marketing qualified sales, qualified to a sales opportunity. We carry it through that process and you build programs and activities that exist underneath all of those things. And then you staff appropriately and different parts of the business are going to play different roles. So if you're a field facing resource, uh, in the consideration and decision phase, you're much more involved on the forecasting front where that would be appropriate for someone who is more internal facing. Now you're talking about, all right, well, what kind of sales stages do we have?

**[7:45]** How do we progress from one sales to sales stage to another? What's the process there? Are we using the right tool to manage forecasting? Are we using the right tool to manage stages? You know, competitive intelligence, business intelligence, there's a bunch of different things. But if you set that overarching framework of like, this is how we're going to operate internally and it mirrors how your customer is going to internalize you as a, as a potential vendor or partner. Um, it brings a lot of clarity as far as roles are concerned. It brings a lot of clarity as far as expectations are concerned. And you can very easily measure

**[8:20]** kind of all of that process across and determine where you're succeeding, where you're failing, where you can up level, um, you know, where maybe you have too many resources, whether that be tools or people just, just brings a lot of insight as far as, uh, what you're doing, uh, as a company or a sales team. Yeah, I love that. Just being methodical about how you map that out. And one of the unsung heroes that I feel really help ensure that it goes from looking really good on paper, looking really good in systems to actually being executed in the field is sales enablement. I haven't seen a lot of companies do sales enablement

**[9:05]** really well. Uh, there's a handful that I think have some world-class teams and have a strong methodology for how they run sales enablement. I would love to hear how you think of that function, structure it, operate it. How do you know if a sales enablement team is doing a good job? It's a highly debated topic. Uh, I would say sales enablement is the secret sauce if you do it appropriately. The reason it falls apart so quickly is either companies overstaff it or understaff it. And what I mean by that is they expect a very small amount of people to drive learning and enablement for a very large amount of people

**[9:54]** or they overstaff it to the point where the field sees it as misappropriation of investment where they're not extracting enough value from a large Albatross organization. I think about it as if you're in a go to market organization, you don't think about the head count distribution as granular as we have quota carrying jobs and we have non-quota carrying jobs. And non-quota carrying jobs would be anything that's not direct. And what I mean by that is anything that's not an AE. Frontline sales managers are overhead. Second line sales managers are overhead. RevOps is overhead. Sales enablement is overhead. Sales engineers are overhead.

### 10:28 — Sales enablement: the secret sauce

**[10:34]** You have to look at it from how are we going to optimize this AE who is carrying a million or $2 million in quota and what are we going to put around them from a pod perspective and then what do we get back each time we put someone around them. Now you have a sales engineer which is the easiest path to ROI, right? Technical conversations that you want to have with a customer. All right, that one's easy. You then move along and you think of it from almost like a competency web of these are all the things that the AE is going to run into beyond their natural talent which can be a very sliding scale in tech sales.

**[11:14]** What are each of these roles going to do and what do they give back? And if you look at the data, the data would tell you sales enablement is anywhere between the first or the third thing that a sales person will complain about if they're not doing well. And if you don't resource from that perspective, like almost like what I was talking about from the operating cadence perspective, it's almost like it's kind of hiding in plain sight. It's almost screaming at you like this is the most obvious thing that this person is going to need. Like they need to understand how to position the product. They need to understand how to sell

**[11:52]** the product and they need to get better at selling and you it would be hard to find a sales person that within the first five minutes of asking them what they need to be successful. Those phrases coming out of their mouth. So building that function in a thrifty way that has a large impact is incredibly important, but building it from like almost a zero sum budgeting exercise where you pull the sales leadership aside and you say what do you need? Walk me through it from zero. Don't worry about what it looks like. I don't want to hear about what was at your other company. Like tell me what you want and tell me how

**[12:29]** you want it to come in waves and then build it organically from there. Sales enablement can become extremely expensive. It can become bloated and you can have a lot of things going on with it, but I think it's an opportunity to not only kind of codify this is how we're going to do learning enablement, which honestly is not that difficult. Right. E-learnings. We're going to push these things out. The special sauce is how are you running new hire? Are you going to get people excited about what they do at new hire? Are you running in person? Do you have a hypo program? Do you have a leadership program? How do you

**[13:06]** progress talent? Where does talent come in and out? How are you supporting that talent once they're getting in these roles? If you build it organically as almost like a sales academy where you're constantly challenging and constantly learning and not only that, but you have different learning paths for different people. Did you get into some of these folks that have been selling for 20 years? You're not going to teach them anything about selling. So what kind of programs are you running to keep them engaged? Do you have them in a hypo program? Are you running the hypo program concurrently with your new hire

**[13:40]** program? So that when you have your new people, they're in the same place as your high potential people. And maybe your leadership at the same time. So you get this nice synergy of talent. It's things like that, that I really think about from a sales enablement perspective. Not that they're not important, but I don't, I don't necessarily care about all the kind of the blocking and tackling of like, we're going to run command the message or we're going to run med pick or like pick your favorite flavor of ice cream. It's about people. It's about human connection, how to drive a culture of learning and enablement that is much more

**[14:19]** important. And that goes much beyond the content or, you know, whatever, whatever tool you're using to drive content out there and things of that nature. So I'm a big believer in building something in the image of what sales needs, building it in a way where they feel like it's additive to what they're doing, as opposed to just another overhead organization that, that they know beyond a shadow of a doubt, they are supporting from a revenue perspective. So, or a cost perspective. So it's important to make sure, again, back to the value thing. How are we driving value? How are we showing value? Why is, why is this important and why

**[15:00]** would we invest in it? And if you can answer those, if you can answer those questions quickly and efficiently and tie it, tie it back to what sellers are asking for, makes everything easier. If you were to walk into a company that has no enablement versus a company that has done enablement right, assume all other things equal, what would be the biggest things you would notice that would be different between those two companies and those two sales teams? I would expect the company with no enablement to have massive attrition problems because salespeople, salespeople need that, that kind of stuff to be, to be effective. If the company

**[15:42]** is performing in spite of the sales enablement, that's an easy opportunity to pull, pull that lever to spike productivity without doing more investment. I think the first thing I would probably do is ask the company what they were doing from an enablement perspective that made them think that shuttering the, the function completely, like what kind of value that was, that was getting back. That would probably be the first, the first thing I would do. I don't know how you succeed without having a, a very strong enablement plan. Do you see it show up in some of the metrics as well? Things. So you mentioned attrition.

**[16:19]** Would you see it in ramp time, conversion rate? Can they carry a bigger quota if they're enabled properly? I think you can see it clearest in your acquisition data. So how are you acquiring customers and then how are you ramping customers once they come in? That is where you're going to see it most acutely. It's going to get skewed in your install base because a lot of things go into the behavior of customers of your company. A lot of things that don't make any sense drive things in one direction or another, especially if you're running consumption, which is where I've been for the last six or seven years. There's this, you know, paradoxical

**[17:00]** argument around sales role in a consumption environment that, that really skews traditional sales metrics, but you can, you can see it most acutely in the acquisition, right? Because your, your time to close is predicated on your rep's ability to deliver your messaging to a customer, deliver it effectively, both from a product perspective and just a sales competency perspective. So you'd see it there. And then you would see it most acutely if you're running a consumption model and how quickly they adopt whatever you're trying to do. Because again, that goes back to positioning relationships, all of those types of things,

**[17:44]** which, which drives you right back to whatever product positioning you're putting in front of the customer and then the general sales skills associated with getting the customer to ramp on your product. I want to get to the consumption conversation in a moment because that that's a huge one, especially with the rise of so many AI companies and most of their models needing to be tied to consumption for economic reasons. Before we get there, though, one last thing on enablement. When is the right time to start creating a formal enablement team? Is there a revenue threshold? Is it a number of reps threshold? Is it number of fires going on? When do you

**[18:29]** know it's time to bring enablement in? From a headcount perspective, it's probably, it's probably once you get your, your span of control beyond five or six from a frontline sales manager and you start to get into deep water from a, from a customer perspective. Like if your enterprise selling, you probably need it pretty early. It, it's largely predicated on the maturity of your sales organization, the maturity of your customer. If you're selling something that's fairly binary or easy, easy to kind of click into. Maybe you don't need it. Majority of the companies I've worked

**[19:13]** for have been enterprise sales plays. You need enablement pretty early and maybe you don't need the horses, but you need maybe a consultancy. You need strategy there. Just like you need strategy in RevOps. And if you don't have it, you're living and dying with the talent of your sales leadership, which as someone who's done this for almost 20 years, very rarely do you have a lot of depth there. You've usually got a couple of people that are asleep. You've got a couple of people that are riding the coattails of people above them or below them. You've got a lot of people that, you know, have strong RevOps people

**[19:57]** around them that keep them inside the margins. Enablement is, is your uplift and it's either going to, it's either perfume for a bad smell or, or it's, it's, it's the cleanliness is needed to, to get back home, to, to, to get it under control. Yeah. I've definitely seen teams go far too long without bringing it in. And I think that's helpful guidance, especially going back to what you brought up earlier. Hey, what's your customer lifecycle? What does that journey look like? What are you expecting your sellers to do? How complex is it? And then leveraging that to gauge when to bring it in. I want

**[20:40]** to talk about consumption based a little bit, because this is such a huge topic as a RevOps leader. I have, I've worked in consumption based companies at LeanScale. We serve many consumption based companies and some of the biggest challenges tend to be, how do you set quotas? How long does the sales person stay involved? And is there a handoff or do you have them manage existing? And then probably one of the most difficult is how do you forecast? So I know those are some big meaty topics and I know there's no perfect answer for some of these, but I would just love to hear how you've approached these and any guidance you

**[21:24]** could give somebody who's maybe doing it for the first time. I would tell you every business is different. Um, consumption in general insinuates that your product is driving whatever input or output you're looking for and all products are consumed in different, in different ways. So unless you're literally going from competitor to competitor, your, your, your plan of action as far as how to manage it should largely be different. I haven't seen kind of a silver bullet for it. Um, but how I like to approach it is first and foremost, at scale, you need a hunter farmer model, or at least I'm a strong

### 21:29 — When to build a formal enablement team

**[22:09]** proponent of it. You need people that are going and looking for logos, which is a much more traditional sales path. And then you need a team that's growing your install base aggressively and finding the bridge between those two things, as far as how to work, how to get those teams to work together and not feel like one of them is interloping on the other. That's really the challenge. And then the 600 pound gorilla in the room is how do you pay them? Yeah. Can we, we double click on this one because this, you know, devil's in the details on, on this, especially if you want to buy for Kate and do hunter farmer,

**[22:48]** how do you pay them? How long do you like to keep them in? I know it may depend, but let's use maybe you're doing typical mid market level deals. What type of structures have you put in place or have you seen be successful? So if you're going to run a hunter farmer model, which I, which again, I would, I would suggest it's about accepting that acquisition is a process, not an event. So just because a customer is signed a PO with you, and again, we're in a kind of a consumption model construct here, just because the customer is signed a PO with you, that does not mean your acquisition process is over. So if the customer signed

**[23:35]** a 10 K deal, right? And your average enterprise sale is 150 or 200 or whatever it is, you've got some work to do. And typically how I look at it is within your first kind of 12 months with your customer, arbitrary line, but just kind of stay with me, you have a certain process, right? The work actually starts the second the customer signs the PO, right? Everything before that is, is just trying to get them to turn on. But if you're a consumption revenue model, your revenue doesn't start until your customer has signed the PO, which, which, which means the work hasn't started either. So the game is really, how do I get them to

**[24:22]** use my products? And how do I get them to use my product and see value in it as quickly as possible? So if you've got the right acquisition organization, you're giving them to, to that you're giving them two tools in the, in the field, you're showing them how to identify and capture customers that are going to use your product that at high volumes. And then you're giving them tools to once they've agreed that they, they want, you've earned their trust, you've earned their sale, you are then transitioning to, all right, this is how I drive value. And this is how I communicate and position my product the right way. Because in the consumption

### 24:33 — The real challenges of consumption revenue

**[25:01]** game, what you're trying to do is not land, not land at scale, because that just invites risk. If you land at scale, now you have all these expectations around what's, what needs to happen. And you're, it's kind of like trying to start an 18 Wheeler versus start a lawnmower, I think it's going to take you more time, more effort and more, all of those things. But if you start small and then you rapidly, you rapidly show value, you can go from a small land to maybe a six month true up top up amendment, whatever you want to call that kind of secondary, I call it a double tap. You can then bridge them to their natural

**[25:42]** renewal date, which is most likely 12 months from when you signed them. And that 12 month anniversary, that is really the first deal, the kind of that first year in a consumption model, I really pushes like almost like a paid pilot. Because if you bring your customer along that way, and you're upfront with the consumption kind of, again, elephant in the room, which is, I don't really know how you're going to use my product. So the worst thing I can do is oversell you because then we're having a very unfriendly conversation towards the end of the contract where you have a bunch of credits that you haven't used. And I'm

### 26:22 — Hunter/farmer: acquisition is a process, not an event

**[26:24]** trying to talk you into buying more because I don't get paid. Unless you buy more versus, Hey, let's start with 10k. Let's see where this goes. I don't think it's going to get you to 12 months. So let's not let's not blow whatever budget you have. But let me get you through this. Let me show the value. You start consuming my product. And if you've run out of credits, that's a good conversation for you and a good conversation for me. It's a value conversation for me as a seller. And you have demonstrated value using the product because you've run through a bunch of credits that we discussed. So it's just, it's a more

**[27:03]** positive conversation. And then once you get to that year mark, you have all of these positives. The customer doesn't feel like you raked them over the calls. You didn't under scope them. You didn't under promise. You didn't over promise and under deliver from a value perspective. You just have a more organic way of doing business. So that's the important part from a motion perspective. And then you have to look at, all right, well, okay, how are we going to pay people associated with that? And then on the farmer side of the house, how do we give people that are deeply talented at managing installed accounts, managing large

**[27:40]** accounts to make sure that they have the resources they need to elevate those conversations. And then back to my operating cadence perspective, if you're carrying the customer through that process and they feel like instead of every time salespeople change for them, that's just ABC company going through their fiscal year planning and scrambling the eggs and moving a bunch of accounts around. You allow your sellers to have very professional mature conversations with their customer. And they can say, Hey, Mr. Customer, I'm an acquisition rep. What I spend time on is getting you in the door, proving value, getting you to this phase.

**[28:22]** And then once you are investing with the company, you are going to get more resources that aren't aligned to the acquisition function. So I'm going to pass you off to Dave or Sally on the install side. Here's why this is important for you as a customer. Here's all the additional things that you're now going to get from ABC company. And it becomes this organic customer friendly conversation around transition as opposed to, yeah, they changed my territory. You're not my territory anymore. Here's your new sales rep, Dave. He doesn't know a damn thing about what you do. Good luck in the next two or three months while you guys figure

**[28:58]** out where the bathroom is. It's building that organic process, grounding yourself in the way that the customer is going to see value from you and the way you set up your territories and the way you pay your reps and the way you build your strategy. And in a consumption revenue model, that's incredibly important. And the worst thing someone in my job can do is have this kind of foreign perspective on it where they are dragging the customer into the internal bureaucracy of the company that they're buying a product from, which is largely what a lot of companies did up until this point because the way they recognized

### 29:00 — Don't land at scale: the 18-wheeler vs. the lawnmower

**[29:44]** revenue didn't force them to do anything else. But the biggest mistake that a lot of companies make jumping into this consumption revenue is they completely ignore that reality. And it's much more acute in the consumption revenue space because there is such a premium on driving value back to the customer as opposed to you've got 200 people using my product. You're growing to 250 years. Here's an invoice for 50 more seats. I'll talk to you in nine months. Or I won't talk to you in nine months. Somebody on my Renewals team that doesn't know you is going to talk to you in nine months. Yeah, plenty of people have completely botched this

**[30:27]** experience. In this thought experiment, I think the customer process, the customer journey you laid out, agree with everything you said. And it seems like the right time frames, the right handoff points, the right process that's keeping the customer in mind. How do you set quotas? And let me make it a little bit harder. How do you set a quota for a new rep? When all of the unit economic information that the CFO is doing is saying, hey, we need our reps to carry a million dollars. What does a million dollars mean in this thought experiment? And then how do you set a quota for a new rep? You start with accepting that acquisition

**[31:14]** reps and install reps are going to carry different numbers. You need to have a very important conversation amongst go to market leadership and finance leadership around who is going to carry consumption quota and who is not. I am a proponent of acquisition sellers not carrying consumption quota. Some people disagree with me. Lots of finance people disagree with me. And then you go from there. And then it's about building a bookings plan for the acquisition function. It's building a consumption plan for the install base folks with either secondary incentives or a flat out quota on the booking side for those folks as well. And then you

**[32:05]** get into the games of spiffs, secondary incentives, you know, target incentive mixes, all those different things. There's a lot of complexity in how you pay from a consumption perspective. I've done it a bunch of different ways. And that's more because it's a very it's a very evolving topic. I have not seen anyone nail it. You know, I've I've seen I've deployed radical versions of it where it's 100% one thing, 0% the other 90, 10, 50, 50, 70, 30. All things are different. I've seen a couple of things with with I think it was anthropic or maybe it was open AI, like totally abandoning variable pay, which I I don't think is the

**[32:53]** right thing. But again, like, we just have to keep chopping wood and eventually you'll find the right thing. But I think I think there is a reckoning coming for kind of this this legacy way of compensating sellers in a in a consumption revenue world or tokens, you know, whatever it is. Because I think a lot of people, a lot of people view consumption as as like being on scholarship, as if the salesperson isn't doing anything to actually drive the consumption within the customer, they're just kind of receiving an annuity. I don't believe that at all. But I can understand why someone who's disconnected from the process

**[33:42]** would see it that way. I'm only asking these questions because I know how hard of a topic it is. And I have interviewed lots of people on this topic. And it there's never a straight answer to it because it's such a difficult nut to crack. But I appreciate some of those frameworks that you shared. And I think knowing, okay, what is your customer journey look like? How much can you realistically commit versus how much is going to end up being consumed? How long does it take for a customer to ramp? Where are you pointing your reps in terms of their territory? Like, I think maybe walking away saying, Hey, you're going to have to

**[34:26]** customize your quota and comp process to whatever your product and offering is, because there's not one right way to do it. Am I fair in saying that? Yeah, yeah. And it's, it's really important that your finance team and your your go to market RevOps team understand the mission. Right. And they're partners in that. If you're in a situation where one side or the other has a very stuck in way of doing it, you're probably going to get to the wrong answer, mostly because once one side of the house is always going to lean one direction or the other. And you're trying to organically find the middle. You're trying to find a way to

### 34:35 — Setting quotas in a consumption world

**[35:17]** give salespeople the incentive to drive the behavior that you want. And in the finance side of the house would be we need to see a tangible return for what's going on, which is why they typically have so much, so much trouble with the consumption side of the house, because it's so largely driven by customer behavior. And if you don't know enough about what's going on with what those reps are doing, it becomes a harder conversation to have. So data, data science, tools, clarity of, of what's going on is so critically important. And that that launches a whole different conversation because it becomes more expensive to monitor

**[36:00]** and be able to do this the right way. It is it is much more intensive, time consuming and costly to run a consumption revenue model than it is just kind of a legacy SaaS or hardware. Like it's, it's much more difficult. It's much more difficult to set quotas. It's much more difficult to monitor and measure what's going on because so much of it, it's driven by the customer, right? The customer, if the customer, the customer could turn around and double their contract value overnight, your rep would have nothing to do with it. And the rep still gets paid. And like people that have been doing what I'm doing, which you've

**[36:39]** been doing for a long time, just say, look, that's just the cost of doing business. There's a lot of people that take a large issue with that and, and want to die on that hill. It's not something I would advise, but it's kind of like fighting the tide, but it is what it is. Last question on this, I promise, but I have to get your opinion on this too. Forecasting. So you're walking into a board meeting, you have an executive review meeting. First of all, how do you even define, because the golden number is always going to be ARR. How do you even define their ARR in a consumption model and a consumption world? And then what do

**[37:23]** you need to put in place to reliably forecast against whatever you defined as your annual recurring revenue? So from an ARR perspective, your legacy processes will hold, right? Like forecasting a commercial's event is not hard. And the things that worked 10 years ago are still going to work today. The difficulty with the consumption revenue is, is taking all those raw materials that are fueling consumption, which is commercial's events, which is product releases, which is security events. Like you're dealing with like macro level stuff. Like, you know, for example, companies I've worked for in the past, the world cup will drive consumption

**[38:11]** up. If you have a retail heavy product, you know, Black Friday is going to be a big, big thing for you. So like understanding all of these things, changes things dynamically. So from a, from an ARR perspective, it shouldn't be that challenging to build a forecast in a forecast model that makes sense, right? Like all the raw materials are there on the consumption side of the house. My opinion is that that is largely a data science exercise that is driven from a finance perspective. And you have to get commitment from both sides of the house, that that is what you want to

**[38:58]** do. And that is how you want to manage it because the knee jerk reaction is that go-to-market should be forecasting revenue because that is traditionally how it's always happened. But what you have to think of it as is go-to-market cannot give you insights. Sellers can't give you insights on how your customer is going to consume your product. It's just, it's not going to happen. It's never going to happen. So what you have to do is build the construct that go-to-market is going and getting you all the raw materials you need to predict revenue in the right ways.

**[39:40]** So you need to put those data science resources on your team in order to own that process? Me personally, no. I don't think it's smart for go-to-market to have discrete resources that aren't specific to go-to-market. And if we're talking about projecting revenue, that is not go-to-market's responsibility. That is generally a finance responsibility. That level of data science and intelligence is extremely important for a consumption revenue company. For example, when I worked at Snowflake, they have a chief data and analytics officer. That entire org exists underneath her. She's responsible for all of that stuff. She has

### 40:32 — Forecasting & defining ARR you can raise against

**[40:33]** divisions and parts of the business that directly support go-to-market, directly support finance, but it's all centralized because product, finance, go-to-market, marketing, pick-your-flavor, they all need the same level of intelligence. And if you've got this bespoke data science team that exists across all of those departments, they will all come up with different answers to the same question. So it's important to centralize that stuff. And it's important that one single entity is informing how all of those groups are bringing back raw materials and things of that nature.

**[41:10]** So I think of it through that lens of what questions do my sellers need to go ask? What do they need to be aware of? What kind of account plans do we need to build to bring those types of insights in so that kind of this large algorithm or whatever kind of scary digital monster you want to create can then predict these customers' behaviors over time. This is how a retail customer deals with us in 0 to 3 months. This is how they deal with us in 6 to 9 months. This is how a manufacturing customer does. This is how a manufacturing customer with 10,000 plus employees ingests us. This is how one with 5,000 or less. It

**[41:51]** has to run all of these scenarios and then project out your revenue in those ways. And then sales has to be held accountable to making sure that we are driving the right behavior in those accounts to drive kind of that line up into the right. So you'll have kind of a system projection over time. And then sales job is to bring back raw materials that is either going to bend that curve up into the right or it's going to show you pits and valleys so that the overall company can call guidance the way it's supposed to or anticipate challenges churn all of those different things. If you do it correctly, a really strong algorithm

**[42:35]** at an aggregate level will be really accurate. When I was at Snowflake, our consumption forecast was calling within a couple of percentage points of where we would finish on a quarterly basis and it was on it every time. Now you go down to like an account level. It's all over the place. But that's where you have human beings that understand the business that can inform it in different ways. But if you don't have that strategy in place of like who is actually carrying the water from the well to the house, you're going to run into problems. And if you have a finance organization or a marketing organization or whatever that's

**[43:15]** just saying, hey, go to markets got to do all of that. You're just it's never going to work. You're basically asking a grocery store when their customers are going to eat your food. I don't know. I know that this customer comes in and buys this amount of produce every week. But I don't know if they're eating it. I don't know if they're donating it. I don't know if they're freezing it. I don't know if they're having a party like it. So you have to build a lot more data science into it to understand it. And then once you have that solved, you can make actions based on the insights that you've created for yourself.

### 43:26 — Should data science report to RevOps? The grocery store problem

**[43:53]** And it is a team sport, which it hasn't been from a legacy perspective. And again, that is one of these paradigm shifts that different companies have different problems with. And most of the problems are legacy companies that are converting to revenue to a consumption revenue versus a snowflake that was born into it. Doesn't know anything other than that. Your legacy SaaS companies or your legacy hardware companies that have pivoted to it tend to fall in these pitfalls that cause more problems. It's hard enough if it's woven into your DNA. I worked at a company email edge. I was hire

**[44:35]** number 28. I got in when we were doing 4 million. We scaled to 58, exited for half a billion. We ran it all those challenges, even though from conception or inception, the company was on a usage model. It still felt like every phase we're running into newer and bigger problems as we went. And I think one big takeaway that you mentioned is if you have a consumption model, it's more expensive to run in terms of resources around it to make sure that you're running it well. And if you're serious about having a consumption model, you need to invest in those things. And I like the distinction. I think the metaphor is pretty sound. Let's

**[45:24]** say I'll take Albertsons. You have the grocery store and then you have the Albertsons mothership that's looking at everybody's data and running the algorithm to understand how to stock up each of their stores. And I think it does take a level of horsepower that might not be a total fit for quite a few RevOps teams, especially when you get to a certain level of scale and you really need to bring in a professional unit to do that. And it's a very difficult thing to do because you do have to-- although there's variable on your side, when you're going to do a fundraising process, they're going to want to know what's your

**[46:06]** recurring revenue. And you're not going to want to discount it by saying, oh, here's our contracted revenue. You're going to want to figure out how to get that consumption into that definition that you can raise against. And you're going to want that definition to be pretty bulletproof. Yeah. And then you get into the RPO conversation. It's deep water. And I think if anyone tells you they have all the answers, they're lying to you and they're posturing. All I can tell you is, in my experience doing it, it requires a lot of creativity. It requires functions, admitting what they're good at and what they're

**[46:46]** not good at. And it's challenging legacy responsibilities in a major way. And if you don't have those things and you don't have acknowledgment on those things, it won't work. If you don't have the right tools, it won't work. If you don't have the right chain of custody, it won't work. If you don't have a table stakes agreement between product and marketing and finance and go to market as far as who owns what and who is responsible for what and how we are all driving an output, it won't work. I think that's a very seasoned, mature and secure point of view to be able to say, hey, I know what I own and I know what I need to

**[47:29]** be great at. And I know when I need to bring other people in. Yeah. It's why I told you know, when you ask me, like, should those data sciences data science resources report to me? I think 10 years ago, I would have told you unequivocally, yes, like how large can we make the O'Holler in Kingdom? But it's just not a it's not a realistic application of resources anymore. And especially in this AI driven world, like we need to be very thoughtful about what we own and what we're good at. Right. Like I don't want a bunch of jack of all trades working for me. I want mercenaries who have very specific sets of skills that

**[48:14]** have very specific targets that they're going after. And then I want control and influence over all of it, because ultimately I have to get screwed up. It's on me. But I'm not a data scientist. I shouldn't be governing what data science people are doing on a regular basis. I should be influencing where their where their attention is pointed, but I shouldn't be responsible for telling them how to give me the output I need. And I think that's become more and more important over the last five to seven years, especially as AI has progressed and things of that nature. Like we have to we have to make humans superhumans. And you

### 48:15 — The Emailage story: hire #28 to a half-billion exit

**[49:00]** don't do that by giving them a bunch of responsibilities. You do that by giving them a very specific set of responsibilities that aligns really well to what they're good at. I think a lot of RevOps leaders will it'll probably take them running through some of these mistakes to figure that out. Yes, I'm speaking because I've worked for some of them. Well, hopefully they can just skip some of them and listen to the podcast. But another another thing you mentioned during our prep for this show was 10 years ago, 15 years ago, and you're earlier in your career, you would try to force your way into that leadership table. And you

**[49:46]** said it wasn't until you did the right things you needed to do to get invited to the table to where you started to have real influence. Yeah, I would love if you could walk our audience through that. Yeah, so look, like most of the things in my career, I know more things because I've screwed it up, or I've watched somebody screw it up. And then I have done it right myself. You have to do this job before you can be good at it. And that's why whenever anyone asked me like, hey, what like, what book should I read? What YouTube videos should I watch? And not that you shouldn't do that. But where you should spend a large

**[50:28]** majority of your time is talking to people and getting the experience yourself and really challenging the businesses that you're in. Like, if you want to do what I do for a living, then you need to go talk to the field. If you're in a if you're in a job that you don't spend any time talking to the field, or you only talk to field leadership, you don't actually understand what's what's what's going on. So those types of things are important. And early on in my career, I tried to force my way into those conversations by basically positioning myself as some kind of an oracle of information and enforcing sales leaders

**[51:10]** into situations where if I wasn't in the room, they weren't prepared to be in the room. And what I learned over time was that that's that's leverage, that's not trust. And if your sales leader trusts you, you will be invited into the room, you won't, you won't be in a situation where you have to be in the room, because otherwise, nothing's going to get talked about. So what I spend my time doing when I got paired with different VPs at different levels would be just bringing different insights. Hey, I'm seeing this. I'm hearing this. Hey, should we try this? Hey, this person's calling this number, they always inevitably fall back 20%

**[51:55]** from this. Try this. Hey, you've got a you've got a morale issue on this team, go spend some time here. If you humanize your job, and your job is to interact with humans, sounds kind of obvious, but that will get you home faster. And I think people that work in RevOps are just constantly in this spin cycle of tickets, and quota changes and all this other kind of stuff like there's just there's there's just a pile of work on your desk that's never going to be completely gone. If you jump on that hamster wheel, you will run till you die. So if you don't start thinking about your job as a human interaction, and making

**[52:44]** sure that you are you are taking advantage of human moments with other human beings. That's, that's how you you gain credibility. That's how you move up the ladder. Like I am. I am not smarter than most of the people that that that work for me today. I've gotten where I've gotten because I've built a lot of trust with a lot of very successful sales people that will either want me to come with them to go work, work somewhere. Or I earn that trust very quickly. And you just gain a reputation of of someone that you want in the foxhole during end of quarter, someone you want behind a closed door in a meeting

### 53:05 — Leverage vs. trust: getting invited to the table

**[53:26]** that you're not in, right? Because a lot of RevOps people are in meetings that sales people are not invited to. So are you a trustworthy advocate in those situations? Do you take the information out of those meetings and appropriately give it back to sales because sales doesn't need to hear everything in that meeting. So it's those types of things. It's those types of soft skills. And I'm being honest with you. When I interview people, I am much less concerned with their competency on hard skills beyond obvious things. I am much more interested in where they grow up, where they go to school. What did they get

**[54:05]** out of school? What are they interested in? Talk to me about this situation. Talk to me about that situation. People that have these types of soft skills are always much stronger operators and performers. I don't think there's anything that that I do on a regular basis that can't be taught. But I can't teach you how to have a hard conversation with a rep. I can't teach you how to tell somebody that makes three times as much as you that they're not getting paid on something. And then you have to come back and ask them for help on something a day later. You know what I mean? Like that's sweat equity. That is its weight

**[54:49]** in gold in our world. So it's really important to understand the difference between leverage and trust. And if you have trust, everything else will come. Yeah. And I think that's the difference between somebody who might be a director in RevOps, somebody people trust to run the systems in the process versus, hey, you're the right hand to the chief revenue officer. And I don't know if a lot of people in RevOps, RevOps leaders really understand the significance of the information they have access to, that the CRO cannot have the type of one on ones that you can have with a salesperson. They

**[55:34]** simply wouldn't share those things with them. And then like you mentioned, those salespeople are not in the rooms that you're in hearing all of the details and passing that information along in an artful way, in a trustful way, and sharing when you can't share things too. I think balancing that builds up that sweat equity you mentioned and then enables you to be unbelievably effective. Yes. You're in this weird space where you're like a part of the leadership staff, but you're annexed from it, right? Like you report to the CRO, but the CRO doesn't have the same conversations with you that they have with

**[56:17]** their direct reports. You typically know a lot more than all of your direct report co-workers or peers. And you have to be very careful about how you deploy that information. And sometimes you got to put your arm around somebody and give them a little bit more than maybe their boss is giving them to make sure that they understand like the mission and altering behavior. And he needs to know the difference between doing that in a helpful way and doing that in a malicious way. Like I said, there's a lot of dynamics here at play, and it's not for everybody. I've seen people fail at it fantastically, but it's so much more about

**[57:03]** the human beings and understanding how to overcome the challenges and understanding how to navigate human beings than it is a CRM or, you know, insert whatever tool you want to talk about. Tools are great. The human beings are more important. Well it wouldn't be a go to market podcast in 2026 if I didn't bring up, but Jimmy, doesn't AI change all of that? Isn't everything going to be totally different now that we have access to cloud code? Do we even need the people anymore? What's your take on AI in GTM specifically in ops? I think it is a very powerful tool that will

**[57:49]** make the human beings that harness it more powerful. I am not a big believer on a lot of the things that I see in the press because I have not seen evidence that it actually works. I don't mean that conceptually it can't get from A to B. What I mean is it still requires a ton of oversight. It requires a very sanitized playground for it to draw from, and as someone who's worked at very large companies, very successful companies that spend a very significant amount of money on data cleanliness. It's not clean, so it's garbage in, garbage out. So I think it's an evolving process, and I think we should aggressively arm RevOps people

**[58:41]** with AI tools that allow them to be more productive. And if you tell me my one person can start giving me a 300% output, that doesn't mean I'm going to lay off two people. It probably means I'm going to hire two more people because all I want to do is move faster. I want to move faster and faster and faster. So if you're giving me a tool to make me run faster, my gut reaction is not to remove productive people from the org. It's to hire more and to go faster. So that's why I kind of reject the majority of the things that I see on the internet. It's either one, it looks great on a slide on LinkedIn, which by the way, AI generated.

### 59:09 — Part of leadership, but annexed from it

**[59:31]** I haven't seen testimonials on it from people that I trust that it actually works. And then the secondary thing would be, if it is there to make people more productive, then I would hire more people to get more productivity. I wouldn't accept status quo and keep performing the same output with less people. That doesn't make much sense to me. I don't know. I think I'm in the same camp. There's no satiating the human race's appetite to consume and to grow and to want to do more and do it faster like you put it. And also if you look at the hiring patterns of the frontier models themselves, there's still plenty of open roles in GTM,

**[1:00:17]** ops, sales, marketing. They haven't been able to build it themselves yet and who better to use it than them. So I think at least in the near term, I think we'll see a lot of growth and the people who can leverage the models well and build the agents and workflows that give them operational leverage I think will do well. But like you said, if you have highly productive people, why not have more of them? Yeah. And there's also a cost element. I have a sinking suspicion AI said it's cheapest right now. And maybe that changes over time, but I'm already seeing it from personal use

**[1:00:59]** of AI as far as what I have access to and what I don't have access to anymore. Labor is labor and tools are tools. So, you know, at the end of the day, I just subscribe to the camp of like, give me smart people and give me a mission. And I'll find my way home and I'll use whatever tools I can get. And if AI can help me get there, I'll use it. But if you're telling me people are tools, I'll take the people all day. Yeah. I had to turn fable off yesterday because I was running it on usage after realizing how many tokens it consumes. So I don't think we've even come close to incurring the cost

### 01:01:30 — Does AI change everything?

**[1:01:46]** it is. And then at that point, it's going to be like, well, I could build it with AI, but if it costs the same to have a person do it, you know, which one's more reliable? Yeah, because you lose all the top of knowledge, right? Like part of it is knowing how you got there. And if you don't know how you got there, you don't know how to fix it. Like that's my biggest paranoia around it. It's like, great, you want to build all these work streams. And then I lay off a bunch of people that used to own those work streams. And then my work streams break. And then I don't know how to fix them because the people that built

**[1:02:15]** the work streams don't work here anymore. So it's, you know, there's, there's, there's just constant, constant pitfalls. So I fall somewhere in the middle. Give me really strong people. Give me an opportunity to make them incredibly productive. And I'll get home. But if you're asking me to reduce my workforce, because I'm creating productivity gains. My question back would don't you want me running absolutely as fast as, as I can in creating as much productivity as I possibly can. Because if you want that, then I need more people, but less people, 100%. Well, we'll see where this crazy world goes. But Jimmy, I just want

**[1:03:02]** to thank you so much. This was a masterclass in understanding what it takes to operate in RevOps at the highest level, going through some of the most challenging aspects of RevOps as well, like all the consumption discussions that we had, and then keeping the human element alive in the role and understanding that your trust with salespeople with leadership, giving people the information that they need will make you highly, highly effective. So Jimmy, thanks for everything you shared. Can't wait to follow you and your career and can't wait for our audience to hear this. Thank you. Thank you for having me.


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