Lenny's Podcast: Product | Career | Growth

The future of AI-powered sales with Vercel COO, Jeanne DeWitt

November 30, 2025

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  • Go-to-market (GTM) encompasses all functions touching a customer or making a dollar, including marketing, sales, customer success, and support, requiring integrated lifecycle orchestration. 
  • The Go-to-Market Engineer (GTM Eng) is an emerging role focused on applying technical prowess and AI to re-architect GTM workflows, significantly increasing leverage and efficiency (e.g., reducing 10 SDRs to 1 QAing an agent). 
  • The majority of customers (around 80%) buy to avoid pain or reduce risk rather than to gain upside, meaning GTM messaging should focus on competitive differentiation and de-risking decisions, especially for enterprises. 
  • Effective segmentation requires layering attributes like company size and workload type to determine the appropriate sales process (e.g., Enterprise vs. Mid-Market) and tailor buying content. 
  • A successful sales organization must possess incredible product depth, acting as an extension of the Product Management team by discerning market signal from noise, rather than just pushing for feature requests. 
  • Pricing should be treated as a product, requiring continuous tuning—like Vercel's unbundling of features from their Enterprise SKU to better serve startups via self-serve channels—to align value capture with customer usage and cost. 

Segments

GTM Importance in AI Era
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(00:00:00)
  • Key Takeaway: GTM strategy is becoming more strategically important due to AI intensifying competition among market players.
  • Summary: With many players pursuing the same market opportunity in the AI era, the ability to bring a product to market and differentiate is crucial. Customers primarily buy to avoid pain or reduce risk (80% of cases), rather than to gain upside. The customer buying experience itself will increasingly differentiate companies when products are only marginally different.
Defining Go-to-Market
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(00:05:52)
  • Key Takeaway: Holistic GTM includes any function touching a customer or making a dollar, requiring integrated lifecycle orchestration across marketing, sales, and support.
  • Summary: While often narrowly viewed as marketing and sales, Jeanne DeWitt Grosser defines GTM as encompassing marketing, sales, technical sales, customer success, support, and partnerships. This holistic view is necessary because functions often have overlapping, non-perfectly aligned strategies (e.g., segmentation frameworks). The goal is to map and orchestrate the entire customer journey from awareness to high LTV.
Evolution of GTM Roles
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(00:08:44)
  • Key Takeaway: GTM has shifted toward deeper consultation, driven by consumption models and AI, leading to the rise of forward-deployed engineering.
  • Summary: Consumption-based models forced GTM to become more consultative, requiring deeper customer understanding to align initial small lands with long-term potential. The AI era further emphasizes this consultative approach, with forward-deployed engineering embedding with customers to understand needs and generalize findings back to the product. Bringing AI to bear on the sales process is another major recent change.
Rise of the GTM Engineer
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(00:11:09)
  • Key Takeaway: The GTM Engineer applies technical prowess to GTM, using AI to re-architect workflows and enable personalized customer experiences at scale.
  • Summary: The GTM Engineer role emerged to bring technical skill to tooling, data use, and increasingly, AI implementation within GTM. At Stripe, an early attempt to build a data-driven prospecting system (Project Roslyn) failed in 2017 but is now feasible with AI. The GTM Eng’s remit is to break down GTM workflows and turn them into agents where AI outperforms humans, such as in lead qualification.
AI Agents in Sales Workflow
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(00:14:21)
  • Key Takeaway: AI agents, built by GTM Engineers, can automate rote sales tasks while maintaining quality through a human-in-the-loop review process.
  • Summary: AI agents are being used to automate inbound lead qualification and response, achieving the same lead-to-opportunity conversion rate as humans but with fewer touches. The process involves shadowing top performers to define the workflow, letting the agent draft responses, and having a human QA/approve before sending. This automation allows existing SDRs to be redeployed to higher-value outbound activities.
Defining Sales Roles (SDR vs AE)
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(00:23:54)
  • Key Takeaway: SDRs focus on pipeline generation (inbound qualification or outbound prospecting), while AEs focus on closing deals by moving prospects to a buying decision.
  • Summary: SDRs (Sales Development Representatives) generate pipeline by qualifying leads to ensure they are worth an Account Executive’s time. AEs (Account Executives) are the closers, guiding interested prospects through the decision process to secure payment. Selling to larger enterprises involves more complex coordination, including navigating economic buyers, technical buyers, and procurement committees.
Hiring Sales & GTM Engineer Profile
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(00:26:13)
  • Key Takeaway: Founders should hire their first salesperson around $1M ARR when a repeatable process is documented, and the ideal first GTM Engineer has prior GTM experience.
  • Summary: GTM engineering forces startups to document a sales playbook earlier than they might otherwise, as agents require documented best practices to function. The ideal first GTM Engineer often comes from a technical sales background (like a Sales Engineer) who understands both GTM processes and code. It is crucial for founders to transition knowledge to the first salesperson while remaining connected to customers for ongoing product discovery.
State-of-the-Art GTM Tools
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(00:34:27)
  • Key Takeaway: Gong is increasingly valuable as its transcripts can be analyzed by internal AI agents (like a ‘Deal Bot’) to diagnose root causes of lost deals.
  • Summary: Internal AI agents can analyze Gong transcripts to perform lost opportunity reviews, revealing that losses attributed to price might actually stem from an inability to demonstrate value to the economic buyer. These agents can also provide real-time insights via Slack during active deals, flagging risks like failure to engage the economic buyer. This allows GTM teams to treat process failures as ‘bugs’ to be fixed in weekly sprints.
Build vs. Buy for AI Agents
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(00:41:03)
  • Key Takeaway: Internal experimentation with building custom AI agents is valuable because esoteric internal context is key to unlocking agent power, often yielding high ROI quickly.
  • Summary: Building custom agents is often not prohibitively expensive or time-consuming; a complex lead agent took one GTM Engineer about six weeks part-time. The cost to run the lead agent is estimated at only $1,000 annually, compared to the million-dollar salary cost of the 10 SDRs it replaced. While generalized platforms exist, internal building allows leveraging specific workflows and content, which is critical in this nascent space.
GTM as a Product Experience
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(00:46:37)
  • Key Takeaway: Treating the customer buying journey as a product requires designing unique, human, and collaborative experiences rather than transactional sales interactions.
  • Summary: As technical differentiation narrows, the buying experience becomes a key differentiator, necessitating unique customer journeys. An effective tactic at Stripe was replacing the boring discovery call with a collaborative whiteboarding session where the customer left with a valuable architectural asset. The principle is to add value at every touchpoint, even for prospects who don’t buy immediately, by providing unique insights like performance benchmarking.
Effective GTM Tactics & Segmentation
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(00:52:39)
  • Key Takeaway: Effective GTM tactics involve providing unique insights about a prospect’s suboptimal state (especially competitive gaps) and creating prescriptive blueprints for implementation.
  • Summary: Salespeople should focus on showing prospects how they will be better than competitors, as most buying is risk-aversion. Beyond documentation, providing well-architected guides or blueprints specific to customer segments (like Stripe’s marketplace setup) is crucial for larger customers. Excellent discovery involves talking less than half the time, asking probing questions, and helping customers arrive at conclusions rather than immediately jumping to problem-solving.
Segmentation Primer
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(01:00:39)
  • Key Takeaway: Effective segmentation carves up the market based on attributes that correlate with different buying behaviors, often using a multi-axis approach like size and growth potential.
  • Summary: Segmentation involves dividing the universe of companies based on how they buy differently, moving beyond simple size categories (SB/MM/Enterprise). Stripe used size (X-axis) and growth potential (Y-axis) because it was a consumption business, adding business model (platform vs. marketplace) to tailor product offerings. Vercel layers in observable attributes like site traffic (pushing high-traffic small companies toward enterprise treatment) and workload type (e-commerce vs. crypto).
Refining Segmentation Attributes
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(01:04:12)
  • Key Takeaway: Company size and workload type are critical attributes for segmenting customers into appropriate sales motions like Enterprise.
  • Summary: A top 25 traffic site might fall into the mid-market based on size alone, but Vercel pushes them to Enterprise due to the need for a more in-depth sales process. Workload type, such as e-commerce versus crypto, dictates the specific language and technical context required for effective sales engagement. This layered approach helps create distinct buying content tailored to the customer’s operational reality.
Data-Driven Segmentation Framework
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(01:05:05)
  • Key Takeaway: Segmentation frameworks should be derived from regression analysis correlating customer attributes with revenue potential and success rates.
  • Summary: Founders should identify attributes correlated with high revenue potential (e.g., traffic rank) and areas where the company repeatedly wins. A heuristic of focusing on three key attributes is recommended for initial segmentation when resources are limited. Segmentation is a company-wide discipline, not just a GTM function, as it informs product development priorities for new features.
Sales Org Alignment with Engineering
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(01:09:55)
  • Key Takeaway: The litmus test for a successful sales org is that engineers cannot easily distinguish an Account Executive from a Product Manager within ten minutes.
  • Summary: Sales teams must have incredible product depth to gain credibility with engineering and product counterparts. The best GTM organizations are equal parts revenue driving and R&D, acting as an extension of the product team by translating customer feedback into actionable roadmap signals. Salespeople need to think like general managers, knowing when to objection handle versus when to advocate for a market-validated feature need.
GTM Strategy and Product Partnership
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(01:13:00)
  • Key Takeaway: Company strategy is the intersection of product strategy and go-to-market strategy, requiring GTM leaders to focus on pricing optimization.
  • Summary: GTM leaders must adopt a GM hat, focusing on how to make revenue generation more efficient through winning products and optimal commercialization. Pricing strategy is a critical lever that, when aligned with product value and cost structure, reduces friction in sales. Insights gathered constantly from customer interactions must be fed back to make the overall company strategy more effective.
PLG Ceiling and Sales Necessity
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(01:14:00)
  • Key Takeaway: Product-Led Growth (PLG) is highly relevant initially but typically hits a ceiling because customers rarely commit to eight-figure deals via self-serve flows.
  • Summary: PLG is often appropriate for products targeting startups initially, but companies must transition to sales motions to achieve larger deal sizes and sustain high growth rates. Companies often fail by waiting too long to introduce a replicable sales process, especially turning outbound into a predictable engine. Nearly every company, even those starting PLG, eventually needs to build a sales organization.
Pricing Strategy Tips
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(01:16:44)
  • Key Takeaway: Pricing must be treated like a product, aligning charges with where customers derive value and where costs are incurred, often requiring unbundling.
  • Summary: Companies frequently underprice due to fear of charging for provided value, and many default to freemium without it being a deliberate strategy. Stripe eliminated a free trial for Stripe Billing because integration effort implied commitment, removing zero downside. Vercel successfully unbundled features from their Enterprise SKU that startups valued, driving growth in the self-serve PLG funnel.
Sales Comp and Hiring Profiles
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(01:19:27)
  • Key Takeaway: Structured sales compensation plans reduce organizational flexibility, while hiring a diversified portfolio of salespeople balances sales skill with analytical rigor.
  • Summary: Highly structured annual sales plans can hinder innovation, as demonstrated by Vercel introducing the AI cloud mid-year after plans were set, challenging incentives. Sales organizations benefit from pairing experienced Account Executives with profiles from consulting or banking backgrounds for quantitative analysis. This mix creates a richer learning environment where AEs learn TCO analysis and consultants learn core sales skills.
Life Motto and Diving Lessons
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(01:22:46)
  • Key Takeaway: The necessity to immediately repeat a dive after a poor landing instills an obsession with excellence and replicability, crucial for sales forecasting.
  • Summary: Jeanne DeWitt Grosser’s mother’s motto, “When the going gets tough, the tough get going,” is pulled on during challenging sales quarters. Diving taught her the importance of precision and repetition, which translates to driving predictable outcomes and excellent forecasting in sales. Getting comfortable with ’no’ is vital, as a ’no’ provides data, whereas ‘maybe’ stalls progress.