Britni Borrelli was brought into Mitratech, a $600 million PE-backed company built from roughly 24 acquisitions, to implement AI across the entire go-to-market organization. What she found was seven Salesforce instances, $5 million in overlapping software that nobody had quantified, legacy systems that leaders had emotional attachment to, and data scattered across so many tools that the chief product officer was told to just go listen to Gong calls if he wanted to know what customers were saying.

In this episode, Rachael Bueckert sits down with Britni Borrelli, VP of Global Sales Strategy and Chief of Staff to the President at Mitratech, for a conversation on how she assessed 24 acquisitions worth of tech debt before touching AI, why she started with data and relationships instead of tools, how she navigated the politics of replacing systems that leaders had built and still owned, the $5 million in software waste she uncovered from an Excel spreadsheet nobody had looked at, and why ripping the Band-Aid on consolidation beats trying to make everyone happy.

Resources Mentioned in This Episode:

GTM Ops Frameworks

00:23 - Intro to Britni Borrelli and Mitratech

01:12 - What "implement AI" actually meant when she walked in

02:10 - Buying software with AI in the name vs. actually implementing AI

03:12 - Starting with data: garbage in, garbage out

05:29 - Observing first: building relationships before changing anything

07:12 - Seven Salesforce instances and the consolidation decision

09:44 - Software that could replace 12 tools at once

11:43 - Managing pushback when you're overhauling everything

13:21 - The current tech stack and why contracts make consolidation slow

16:07 - Acquisition integration: what went wrong and the better process

21:32 - A decade at Tableau and how it changed her relationship with data

25:05 - Surfacing customer voice to product without Gong call listening

30:30 - How do you trust the data once AI is producing it?

35:38 - The 85% vs. 15% close rate problem

46:00 - The $5 million in software overlap nobody had quantified

48:15 - 25-30% more selling time from reducing admin

53:38 - What boards should be asking but aren't

01:08:34 - What a good AI ecosystem actually looks like

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GTM STRATEGY & AI ENGINEERING FOR B2B TECH

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Almost every revenue leader will tell you their number one priority is generating more pipeline. But if the pipeline you already have is leaking, more volume just means you lose more deals, only faster. Reps are chasing deals they will never win, sandbagging deals they could close, and pushing close dates quarter after quarter because nobody trusts what's in Salesforce. Meanwhile, the CRO is spending hours going rep by rep, deal by deal, just trying to get a read on the forecast.

In this episode, Eddie Reynolds and Rachael Bueckert walk through the updated v3.0 of USC's Pipeline Management Framework layer by layer. The conversation covers how to identify whether pipeline management or lead generation is actually the problem, the ICP qualification criteria most companies skip, how to define sales stages and exit criteria that make sandbagging and ghost pipeline impossible, the pipeline inspection process that turns one-on-ones from interrogation into coaching, and why AI is the last layer to add and what breaks when it's the first.

Resources Mentioned in This Episode:

The Pipeline Management Framework v3.0

GTM Ops Frameworks

The First 90 Program

01:09 - More pipeline won't fix a leaking funnel

04:21 - Telltale signs the problem is pipeline management, not lead gen

07:38 - The pyramid: why fundamentals, adoption, optimization, amplification in that order

09:03 - What sales methodology actually means and why most teams stop too early

15:42 - ICP for pipeline management: who should even be in your pipeline?

19:25 - The fuzzy data points that make or break qualification

25:24 - Sales stages, exit criteria, and when to walk away from a deal

32:49 - Adoption: how to get reps to actually follow the process

37:44 - Pipeline inspection that turns one-on-ones into coaching

41:23 - Optimization: using stage conversion data to coach rep by rep

44:02 - The Salesforce SDR handoff tweak that doubled conversion overnight

46:36 - The Go-to-Market Council

51:08 - Amplification: where AI fits and where it doesn't

56:01 - The fastest win: clean your pipeline in one day

This episode is a guest appearance by Eddie Reynolds, CEO of Union Square Consulting, on the Epic Events by vFairs podcast with Muhammad Younas. You can find the original episode on Spotify and Apple Podcasts.

Eddie breaks down how events fit into a GTM strategy and why most companies waste their event budget by skipping the foundational work. The conversation covers why ICP and buyer personas have to come before any event investment, how Eddie stacked an entire conference with pre-arranged meetings and generated millions in pipeline from small events, the step-by-step process most companies miss between the event itself and actual revenue, why intimate dinners where customers sell to your prospects are more effective than booths with badge scanners, and how to evaluate whether your event spend is outperforming what you could get from another channel.

Resources Mentioned in This Episode:

GTM Ops Frameworks

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GTM STRATEGY & AI ENGINEERING FOR B2B TECH

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Salesforce co-founder Parker Harris stood on stage and asked the same question every sales rep has been thinking for 20 years: why should you ever have to log into Salesforce again? Then Salesforce went headless, opening up the MCP so that AI tools like Claude and ChatGPT can read from and write to Salesforce directly. Eddie has been telling customers to force their reps to update Salesforce while simultaneously having his own executive assistant do 99% of his updates for years. That contradiction is about to go away.

In this episode, Eddie Reynolds sits down with Gerry Marletta, SVP of Delivery at Union Square Consulting, for a conversation on what Salesforce Headless 360 actually means for go-to-market teams, why your Salesforce doesn't need to be clean for AI to start delivering value, the difference between reading data and writing data and where the guardrails need to be, why they built their own analytics tool on SOQL queries instead of trusting the MCP for hard numbers, and how six months of CRM cleanup can shrink to six weeks when AI does the heavy lifting.

Resources Mentioned in This Episode:

GTM Ops Frameworks

01:13 - Do we ever need to log into Salesforce again?

03:56 - Depends on who you are and what you do

05:07 - No rep in history has ever wanted to update Salesforce

07:17 - The Wall Street team that refused to log in

09:19 - The 35-field mutiny that got everyone fired

12:03 - What is Salesforce Headless 360 and MCP?

16:45 - Does your Salesforce need to be clean for this to work?

22:00 - Reading data vs. writing data: where to take the training wheels off

25:07 - Automating call prep from calendar events

27:16 - Your data can live in 25 different sources now

31:56 - Updating opportunities from call recordings

39:09 - Why rigid tools kill creative selling

44:38 - The dinner party metaphor: stop cleaning rooms nobody visits

50:07 - SOQL queries vs. MCP: why hard numbers need zero interpretation

56:12 - Hallucination risk: where it's zero and where it's not

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GTM STRATEGY & AI ENGINEERING FOR B2B TECH

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John Barrows has trained sales teams at Salesforce, LinkedIn, Google, and Amazon. He's watched every wave of outbound innovation since before predictable revenue was a playbook. And right now, he has a challenge for anyone claiming AI has tripled their results: show me the numbers. Because what he's seeing across hundreds of companies is that we're still in the efficiency phase of AI, not the results phase. Reps are sending faster, but they're not selling better. And 14 out of 20 reps who booked a call with him couldn't even tell him what he does for a living.

In this episode, Eddie Reynolds sits down with John Barrows, CEO and founder of JB Sales, for a candid conversation on what's actually working in outbound right now and what isn't. The conversation covers the "give a shit" factor that separates reps who get responses from reps who don't, why the SDR model was never really profitable and where it goes from here, how to run sales hackathons that get your whole GTM team building AI solutions together, why email might be about to become the most valuable outbound channel again, and the ICP and segmentation foundation that has to exist before any of it matters.

Resources Mentioned in This Episode:

GTM Ops Frameworks

00:23 - AI and outbound: separating signal from noise

03:56 - Still in the efficiency phase, not the results phase

06:51 - Why top-down AI adoption fails

10:04 - Turn your sales org into a sales hackathon

22:01 - The SDR model was never really profitable

27:12 - The "give a shit" factor and the Morgan story

32:16 - 14 out of 20 reps couldn't tell John what he does

33:43 - Why email is about to become the most valuable channel again

36:41 - The foundation: ICP, personas, and segmentation before AI

40:50 - Eddie's reps switched to templates and the meetings dried up

41:50 - Why hiring outside tech might be the answer

52:33 - Happiness, core values, and the unsexy path to winning

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GTM STRATEGY & AI ENGINEERING FOR B2B TECH

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For ten years, the partner channel has been the single largest source of revenue for Union Square Consulting. Not marketing, not outbound, not referrals from happy customers. Partners. And for most of those ten years, Eddie was reaching out to the wrong ones. Thousands of conversations with partners who looked right on paper but never sent a single deal. It took years of trial and error to land on the three criteria that actually predict whether a partner will deliver. And if any one of those is missing, it's a waste of everyone's time.

In this episode, Eddie Reynolds and Rachael Bueckert break down how to define your ideal partner profile with the same rigor you'd put into your ICP. The conversation covers the three criteria that separate partners who send deals from partners who send pleasantries, how to think about partner benefit when there's no revenue share in the equation, why even internal partners at companies like Salesforce and Wells Fargo compete for mindshare the same way external ones do, and how to operationalize your IPP into a continuously improving system that actually drives revenue.

Resources Mentioned in This Episode:

GTM Ops Frameworks

01:25 - Why we don't talk about the partner channel enough

02:38 - The three criteria for an ideal partner

06:28 - Why direct benefit matters more than revenue share

09:24 - Wells Fargo: 186 lines of business, 35 business cards, zero mindshare

10:11 - Salesforce: internal partners competing for attention

11:31 - Why $500 in quota relief doesn't move the needle

14:37 - Where people go wrong defining their partner profile

21:21 - What enablement for partners actually looks like

22:14 - The La Bernardine dinner story

31:37 - AI for keeping your ideal partner profile current

37:15 - How to measure whether your partner program is worth it

45:24 - Turning the IPP into a continuous improvement system

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GTM STRATEGY & AI ENGINEERING FOR B2B TECH

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Jeff Perry joined Carta in late 2018 when it was a 275-person startup selling cap tables to founders. Eight years later, it's a $650 million private markets platform with 50,000 companies on the platform, a fund administration business for venture and PE firms, a total compensation product, tax advisory, and a recently acquired law firm. The cap table business is still healthy, but it has a ceiling. Every dollar of growth beyond that core product came from building or buying entirely new revenue lines for entirely new types of customers.

In this episode, Rachael Bueckert sits down with Jeff Perry, CRO of Carta, for a conversation on how to keep growing when your core TAM runs out, the specialization mistake that taught them to simplify instead of overengineer, and how a $50K customer becomes a $1M customer over time through multi-product cross-sell.

Resources Mentioned in This Episode:

GTM Ops Frameworks

00:26 - Intro to Jeff Perry and the Carta growth story

01:13 - What Carta looked like at $20M and 275 people

05:06 - Why the cap table TAM ran out and what came next

07:39 - Build vs. buy: what's harder, new products or acquisitions?

09:50 - The Manhattan takeover and guerrilla marketing for PE firms

13:40 - How customer conversations drive the product roadmap

17:53 - One-call closes and two-year enterprise cycles in the same company

21:18 - The customer symposium where the CPO built features before dinner

23:28 - Enabling reps to sell across a widening product portfolio

32:50 - Territory and capacity planning across multiple business lines

34:55 - The separate sales team mistake and why they simplified

40:09 - Comp plan evolution: product-specific quotas vs. one quota

46:13 - Expansion: how a $50K customer becomes $1M

50:09 - Advice for CROs hitting a TAM ceiling

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GTM STRATEGY & AI ENGINEERING FOR B2B TECH

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On paper, everything looks healthy. Reps are hitting activity. Leads are coming in. Pipeline looks full. And yet deals are slipping, cycles are running long, and customers keep churning out the back. So much of that comes back to an ICP definition that's too broad, too shallow, and stuck in a doc that nobody uses.

In this episode, Eddie Reynolds and Rachael Bueckert break down how to turn your ICP from a static document into a living system that sharpens itself every quarter. The conversation covers how to identify which customers are actually your best, why the one differentiating data point matters more than 50 generic ones, how to build an account scoring model reps will actually trust, and how to build the continuous improvement engine that feeds every new lead and lost deal back into the definition.

Resources Mentioned in This Episode:

The AI-Driven Continuously Self-Improving ICP (Newsletter)

150+ ICP Data Points List

GTM Ops Frameworks

01:02 - Pipeline looks full but nothing is converting

01:50 - Why a written ICP isn't enough

03:29 - How to identify which customers are actually the best

06:50 - The biggest customer isn't always the best customer

09:09 - Going deeper than firmographics to find what really differentiates

12:08 - 150+ data points you could consider

14:53 - If it describes 80% of your lost deals too, it's not differentiating

16:54 - Survivorship bias: what the planes that don't come back tell you

19:26 - Manual vs. AI: when to use each and why you spot check everything

22:58 - Deterministic data first, fuzzy data second

27:28 - How changing content voice fixed lead quality overnight

30:17 - Fit vs. timing and why both belong in the ICP definition

33:56 - Building the account scoring model reps will actually trust

50:03 - AI provides the signal, humans make the decision

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GTM STRATEGY & AI ENGINEERING FOR B2B TECH

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Miro had 60-70 million users, world-class UX, and reps were overachieving on their numbers. Then the pandemic tailwind died and the product became an easy target to replace with free alternatives. The problem wasn't that people didn't love Miro. The problem was that nobody had ever tied it to anything strategic enough to survive a renewal conversation. Pipeline looked great because the team was monetizing usage. But usage without a business case doesn't survive a CFO asking "what do we actually need this for?"

In this episode, Rachael Bueckert sits down with Sangeeta Chakraborty, former CRO of Miro and current CRO of Amagi, for a conversation on how she rebuilt Miro's entire go-to-market motion from the ground up. The conversation covers how she redesigned ICP, the use-case sales plays, and how the value engineering function helped buyers build business cases they'd never built before.

Resources Mentioned in This Episode:

GTM Ops Frameworks

00:22 - 70M users loved the product but renewals were breaking

01:39 - Walking into the first QBR and seeing the cracks

04:40 - Monetizing usage vs. selling outcomes

06:41 - Finding the preponderance of use case across 70M users

18:03 - Command of the Message and building use-case sales plays

18:26 - Why the ICP became the product manager

19:06 - Pepsi: a potato chip that took 3 years cut to 10 months

26:04 - Value engineering: helping buyers build business cases

34:11 - Entry and exit gates at every stage of the sales process

42:04 - A cheaper competitor came knocking

43:54 - How competitive pressure made the team stronger

46:17 - "Don't get fooled by vanity metrics"

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GTM STRATEGY & AI ENGINEERING FOR B2B TECH

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Anders Krohn spent years deploying AI into enterprise go-to-market organizations and kept hitting the same wall. The models weren't the problem. The data was. When his team anonymized and aggregated proof-of-concept data across dozens of companies, they found that 86% of CRM records had some kind of entity data error. Not missing fields or stale phone numbers. Structural errors at the entity layer: accounts mapped to the wrong parent companies, subsidiaries treated as headquarters, local offices confused with global entities because they share the same domain. And every enrichment tool, every territory plan, every AI agent built on top of that data inherits the same errors downstream.

In this episode, Rachael Bueckert sits down with Anders Krohn, founder and CEO of Kernel, for a conversation on why the entity and hierarchy layer is the root cause of broken CRM data, how 20% of accounts assigned to reps are flat wrong, why companies spending millions on data providers are still unhappy, how AI exposed a problem that humans used to work around, and what it actually takes to fix the foundation before layering anything else on top of it.

Resources Mentioned in This Episode:

GTM Ops Frameworks

01:15 - Every revenue leader has had this moment

04:06 - Garbage in, garbage out: learning the hard way

05:49 - 86% of CRM records have entity data errors

08:18 - Spending millions on data and still unhappy

08:40 - Territory planning and AI: the two triggers that expose bad data

10:22 - How deep the entity layer problem actually goes

14:57 - 20% of accounts assigned to reps are wrong

20:21 - The domain problem: Starbucks HQ vs. Starbucks around the corner

21:13 - Two parts to solving a data problem

22:51 - Data governance vs. letting reps loose: when each makes sense

23:20 - Data debt and why it compounds as companies scale

39:21 - What this means for AI deployment right now

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GTM STRATEGY & AI ENGINEERING FOR B2B TECH

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