Key insights:

Most founders think of GTM as "sales and marketing." Jeanne defines it as any function that touches a customer or makes a dollar -marketing, sales, customer success, support, partnerships, and field engineering.

"Those functions often have this Venn diagram strategy where marketing is pursuing one thing, it overlaps with what sales is pursuing but not perfectly, which also overlaps with what support is pursuing but not perfectly," Jeanne explains.

With 10 players now pursuing the same market opportunity (AI lowering barriers), differentiating through GTM execution - not just product - becomes critical. Think of GTM as a customer journey to orchestrate, from awareness through to high LTV.

Key Takeaways:

The GTM engineer -combining sales expertise with technical skills to build AI agents -is delivering 10x productivity. At Vercel, former sales engineers now build agents that automate workflows.

In 2017 at Stripe, Jeanne's "Project Rosalind" -enabling personalised outbound at scale -failed. "Our false positive rate... just never really got there," she recalls. The same concept now works with AI agents.

GTM engineers shadow top performers, encode their workflow into agents using tools like Vercel's SDK. Agents handle rote work while humans review.

Results? Jeanne's inbound SDR team: 10 people → 1 person with AI agents. 90%+ cost reduction ($1M+ salaries → $1K/year). The remaining SDR spends 70% of time with customers versus 30%. AI frees salespeople to use their full capacity from day one.

Key Takeaways:

80% of customers buy to avoid pain or reduce risk, not increase upside -especially enterprises. Yet technical founders sell "art of the possible" and future potential.

"We all love to talk about everything we're going to enable in the future, but that's often really a sale that's going to resonate with another founder. Everybody else, particularly enterprises, you're avoiding the risk of not making your revenue target next quarter," Jeanne explains.

Focus on "here's how you'll be better than competitors" and "here's how you'll avoid missing goals." This differentiation messaging resonates with risk-averse buyers.

With AI products, customers know they need to change but don't know what to change -creating an opening for consultative selling. The buying experience itself differentiates when products are similar.

Key Takeaways:

Jeanne's superpower: building sales orgs that don't feel like sales orgs to engineers. Her test: "If you put an AE in front of 10 engineers, it should take them 10 minutes to figure out you aren't a PM."

Map the customer journey from awareness to high LTV like a product experience. Identify friction points, optimise conversion, instrument everything, iterate on data.

You can't automate what isn't documented. GTM engineering forces companies to codify best practices -discovery questions, objection handling, what separates high from average performers.

When Vercel built their lead agent, they started human-in-the-loop: agent recommends, humans review. Over time, autonomy increases.

Jeanne's team built a "deal bot" in real-time Slack channels. The bot analyses all interactions and surfaces insights: "You're this far in and you haven't talked to an economic buyer." Like having an AI sales coach in every deal.

Key Takeaways:

Vercel deploys AI agents across GTM functions. Start with "legible" workflows -writable, replicable, deterministic.

Inbound lead qualification was first. Agent qualifies leads, researches databases, crafts responses. Humans review. One person handles what required 10 SDRs.

Outbound prospecting starts at lower market end (single decision-maker). For enterprise, humans lead, agents research. Install-base expansion -agents map workflows and suggest next steps.

The "lost bot" breakthrough: Fed every Slack/email/Gong call from biggest lost deals. Top loss marked "lost on price." Agent revealed truth: "You never really got in touch with economic buyer... the reason we lost was an inability to demonstrate value."

This drove: (1) better content on quantifying value, (2) real-time "deal bot" coaching reps mid-deal.

Key Takeaways:

Building agents isn't hard or expensive. Lead qualification: six weeks, one engineer part-time. Lost bot? Built in two days.

Cost to run? Lead agent (replaced 10 SDRs): $1,000/year. 99.9% reduction versus $1M+ salaries.

"Your own esoteric context -your content, your workflow -is really key to unlocking the power of the agent," Jeanne explains. Off-the-shelf platforms don't capture your sales process nuances.

Build before buying. Building first creates deep understanding. Internal agents let you iterate rapidly and codify learnings.

Jeanne's GTM engineers: sales engineering backgrounds -understanding GTM motion plus technical skills to encode it.

Key Takeaways:

Segmentation sounds basic but is often executed poorly. Misaligned segmentation is the biggest source of GTM inefficiency.

"Marketing is pursuing one thing that overlaps with what sales is pursuing but not perfectly, which also overlaps with what support is pursuing but not perfectly," Jeanne explains. This Venn diagram creates wasted effort and confused customers.

For AI agents to work, segmentation must be consistent across all GTM functions. Without consistency, you're building agents on shaky foundations.

Early-stage companies: focus on a single segment and nail it before expanding.

Involve revenue operations early. "It's a good idea to have that analytical arm of sales earlier than you think because having data, having process, is actually what gives you insights as a founder," Jeanne advises.

Key Takeaways:

Traditional sales hierarchy - SDRs generate pipeline, AEs close - is being disrupted. Many SDRs shift to outbound closing or consultative work versus years of rote prospecting.

"No one graduated from college and was like, 'Yes, I just went to college for four years to become an SDR,'" she observes.

With AI handling qualification and prospecting, SDRs do work they enjoy: customer conversations, discovery, relationships. At Vercel, when inbound SDRs shrank from 10 to 1 (plus AI), the other 9 shifted to outbound.

For AEs, the shift is strategic work. AEs historically spent 30-40% of time with customers, the rest on research, follow-ups, CRM. With AI handling admin, AEs now spend 70% of time with customers.

Hybrid roles emerging: AE/GTM engineers who figure out best practices in deals, then build agents to augment selling.

For first sales hires: bring in RevOps early (before your first AE) to instrument process. Then hire "general athletes" who define the playbook.

Key Takeaways:

Document your sales process, then find "legible" workflows to automate.

"You can't really apply go-to-market engineering unless you actually have a point of view on what best practice should look like," Jeanne notes. Get to 10-20 customers with a repeatable playbook before automating. Understand what best reps ask, how they handle objections, what content moves deals.

Then hire someone with GTM experience and technical skills -sales engineer, technical CSM, former developer. Have them shadow performers and encode workflows into agents.

Start with inbound lead qualification or install-base expansion -data-rich, deterministic, immediate ROI. Build human-in-the-loop initially, increase autonomy later.

For larger orgs, audit GTM segmentation. Are marketing, sales, success aligned? Fix fragmentation before layering in AI.

Treat GTM like a product. Map the journey, identify friction, instrument everything, iterate on data. Companies mastering this gain massive advantage as AI commoditises product and GTM execution becomes the moat.

Key Takeaways:

That’s a wrap.

As always, the journey doesn't end here!

Please share and let us know what you liked or want changing! 🚀👋

Alastair 🍽️.