From headcount to workflows.
The old GTM playbook solves every problem by adding headcount. A mid-stage B2B SaaS platform went the other way: we solved the actual workflows with AI across the whole revenue bowtie, from new business to retention and expansion. The efficiency showed up everywhere, bigger deals, faster cycles, higher win rates, and an engine that grows without adding bodies.
What this chapter drove, and what it taught me on the way. Both up front, because the lessons are the point, not a footnote.
The rebuild only compounded because the team was fixed before the engine was built. Run it backwards and you get a very sophisticated machine bolted onto a broken org. The AI was the third move, never the first.
Cutting deep once was the hardest call this seat makes, and the people who left were not the reason the engine was broken. What can be defended is the system waiting behind the cut: the profile, the ramp bar, the coaching rhythm. A purge without a system is a smaller version of the same problem.
The product-led channel became the number-one source of new business, but only after product owned the event schema and marketing owned the loop-closer. The channel was there the whole time. The org chart was hiding it.
Every stage of the bowtie, a workflow instead of a hire.
The old playbook solved every sales problem by adding people. I rebuilt each stage of the revenue bowtie as a workflow, so efficiency compounded across the whole funnel instead of a hire per gap.
I was hired to scale a go-to-market and found one that lost money on every selling head. The old playbook says solve that with more heads. This chapter is what happens when you go the other way.
The first move was the hardest one, and it has its own page now: the team rebuild. Cut deep once, redefine the profile around who actually wins the seat, and put RAISE behind the cut, recruit, accelerate, inspect, sharpen, earn. The smaller team nearly doubled new business on about 80% fewer selling heads, and the unit swung from money-losing to net positive.
Then the seat got bigger on purpose. I took over Customer Success and ran Revenue and CS as one bowtie, so the handoff that breaks most GTM orgs became one owner. Retention went proactive off an early-warning system, churn stopped and the base grew, and the same read pointed backward produced a seven-figure churned-customer win-back.
The channel nobody could see got built with the functions that owned its data: product owned the event schema and we ran the journey analysis jointly; marketing owned the motion mix and the nurture loop-closer. The product-led channel became the number-one source of new business, winning nearly half its qualified deals.
And under all of it, the engine: the AI-native operating system, most of it coded personally. Enrichment, scoring, routing, rep briefs, a Deal Confidence Score that made the forecast defensible, sixty-plus reports reading themselves. AI took the drag; the people kept the conversations, the coaching, and the calls that carry consequence. Reps in the room, not out of it.
Every stage of the bowtie became a workflow instead of a hire. Bigger deals, faster cycles, higher win rates, an engine that grows without adding bodies, and the receipts on this page. The order mattered: the team first, the partners second, the platform third. Run it backwards and you get a very sophisticated machine bolted onto a broken org.
CHAPTER ONE, IN FULL: THE TEAM REBUILD →The ICP got rebuilt from who looks like a fit to who actually buys, off more than a thousand customer and prospect calls and pressure-tested against our own won and lost deals. The team refocused on the accounts that close, and deal sizes and win rates both climbed.
We built the lead engine end to end, enrichment, scoring, routing, and the context a rep needs to know why this account and why now, with a Deal Confidence Score so the forecast told the truth. Revenue moved off a pure seat model, product-led and self-serve motions came online, and the enterprise motion was built from zero on PLAN selling and a vibecoded Deal Room.
The repeatable work moved to agents, a self-running enrichment pipeline that writes its own account briefs, analyst agents that learn from their misses, an AI Sequence Builder wired to our own MCP, and Mixmax University with AI roleplay partners, so reps and CSMs keep the conversations and the deals.
N · NORMALIZE is how the team made it the default way of working. T · TIE BACK is the outcomes below.
How the build unfolded.
What I owned, and what it produced.
The whole commercial number, and the receipts it threw off. Pick a lens.
New-business revenue nearly doubled while the team got leaner. The engine compounded.
The engine
How the AI-native system got built, and how it scores.
The AI-native system, module by module.
Pick a piece. Each one replaced a headcount problem with a workflow. The visual updates as you go.
Go deeper: the full engine deep dive: ICP, the stack, and what broke →A pipeline that scores and enriches itself, writes its own account briefs, and hands over a forecast you can defend.
What the people I built with said.
I worked for Heath as VP of Revenue for several years, and he stands out as one of the best sales leaders I've worked with across multiple industries. At a mid-stage B2B SaaS platform he led the transition from a sales-led to a product-led-sales motion, wearing countless hats and handling all of them expertly, and he brought the team along with him. Driving short-term growth is one thing; building an engine that delivers consistency is what Heath is incredible at.
Heath was my VP of Sales at a mid-stage B2B SaaS platform and a joy to work with, more friend than boss. He knew his sales fundamentals inside out, vouched for his team, helped during deals, and made my transition into being an AE super easy. He has the energy of a hungry SDR years into an established career, and it's inspiring to see.
Heath led Revenue, I led Product and Growth. Those roles are traditionally destined to fight. We understood we could build together. He started our Product Channel Tiger Team, pushed us to define PQLs together, and kept showing up to the product and data conversations that most sales leaders would skip. Heath is honest in a way that's rare, he says the uncomfortable thing, and he does the work that falls between org charts. I learned something almost every time.
I had the pleasure of working with Heath at a mid-stage B2B SaaS platform, and what I appreciated most was how data-driven he is as a leader, which made it easy to align on initiatives and make decisions we could actually stand behind. He also stays close to the work, happy to dig into the details himself and quick to unblock whatever's in the way. On top of that, Heath is someone you want to work with: supportive, easy to be around, and genuinely invested in his team's growth. Any team would be lucky to have him.
I had the privilege of working with Heath at a mid-stage B2B SaaS platform. He creates an environment where everyone feels comfortable speaking their mind, asks the right questions, and challenges you to think differently without ever making you feel small. He encourages people to grow, trusts them to own their work, and celebrates their wins along the way. I'm grateful to have learned from his leadership and would highly recommend him to any team looking for someone who leads with both empathy and intention.
I joined the a mid-stage B2B SaaS platform marketing team during a time of real transition, and Heath was one of the first to make sure I had context and set me up for success. He was incredibly generous with his time and GTM knowledge, patient and welcoming, and never once made me feel like I was imposing with my many questions. Anyone who works with Heath will find him approachable, quick to collaborate, and just as willing to dive into the gritty details as he is to implement a new sales strategy.
Heath ran sales at a mid-stage B2B SaaS platform with a builder's instinct. When his team needed something the product couldn't do yet, he'd wire up an AI-assisted solution and have it running within days, used daily by real people rather than sitting in a demo folder. Several of those ended up shaping features we shipped. He is relentless, practical, and he finishes what he starts.
It is rare to find a GTM leader who cares as deeply about their team's individual wellbeing as they do about the metrics. Heath understands that human connection and trust are critical to successful teams. He loudly shares their wins, champions women leaders, and ensures each person's voice is heard. When it comes to building, iterating, and leveraging AI and automation, Heath is at the forefront, thoughtfully identifying when and where it fits: let the robots do their job so we can focus on human connection and lasting customer relationships. As my direct manager I appreciated his style, letting me do my job, asking questions and pushing me to think deeper, without micromanaging.
See how it played out elsewhere.
Same method, on your workflow.
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