For much of the past decade, go-to-market strategy has been treated as a downstream function. Build the product, hire sales, push pipeline. Eric Maida argues that this view no longer holds, especially for enterprise AI companies navigating long sales cycles, heightened risk scrutiny, and investor pressure for capital-efficient growth. Go-to-market, he says, is a value creation engine.
“Go-to-market is not just about selling a product,” Maida explains. “It is about translating the speed of an early stage company and the authority of a trusted brand into a single, predictable revenue story that investors and boards can trust.”
As Vice President at TrustModel AI, Maida builds and executes enterprise GTM strategy at the intersection of AI trust, model risk, and measurable business outcomes. His perspective is shaped by leading GTM efforts across the valuation lifecycle, from early stage momentum to scaled enterprise maturity. Companies that treat GTM as a system, not a headcount plan, command higher multiples.
Go-To-Market as a Valuation System
Maida’s view of GTM was forged across distinct operating environments. At Degreed, a learning and talent development platform serving global enterprises where Maida led enterprise GTM efforts, operational rigor and forecasting discipline influenced investor confidence during hypergrowth. At Harvard Business Impacts, where he worked with senior leaders on growth and partnerships, brand equity and premium positioning helped elevate perceived value beyond near-term revenue. Today, building at TrustModel AI, those lessons converge.
“If your go-to-market is just brute force outbound and heroic sales wins, it breaks as you scale,” he says. “You cannot sustain momentum that way.” In contrast, a well-designed GTM motion behaves like a product. It is data-driven, repeatable, and resilient under growth pressure. It produces revenue that is forecastable and explainable, two attributes that materially influence valuation discussions at the board and investor level.
Discipline Over Volume
The first lever Maida emphasizes is ruthless ideal customer profile discipline. Many early stage companies chase revenue wherever they can find it, especially under pressure to show traction. “Revenue from bad-fit customers creates churn that drags down valuation,” Maida says. “They don’t renew and they don’t expand.” By contrast, revenue from tightly defined ideal customers compounds. It drives retention, expansion, and net revenue growth, metrics that matter far more than top-line spikes.
This discipline requires saying no, even when pipeline looks thin. It also requires alignment across sales, marketing, and product on who the company is built to serve. Without that alignment, GTM becomes noisy, expensive, and difficult to scale.
From Headcount to Leverage
AI is accelerating a second shift, one that reframes how GTM efficiency is measured. Historically, scaling revenue meant scaling teams. More SDRs, more reps, more managers. AI has changed the equation. “AI is shifting go-to-market from a headcount game to a leverage game,” he says. Instead of building large sales floors, companies can use AI to amplify outreach, qualification, and personalization with far leaner teams.
This shift has direct valuation implications. As AI-enabled GTM models mature, investors are paying closer attention to revenue per employee as a signal of efficiency and durability.
Companies that make each seller dramatically more productive lower their cost of acquisition and expand margins, two outcomes that strengthen valuation narratives.
The winners, Maida argues, will not be the companies with the largest teams, but the ones that use AI deliberately to create focused, high-output GTM engines.
Experimentation With Accountability
At TrustModel AI, Maida applies this philosophy through constant testing. The team works with multiple vendors and partners to scale personalized outreach across channels, from cold email to LinkedIn and social engagement. The results have been instructive. “Cold email is not dead,” he says. “It just requires massive volume to be effective.” More personalized channels, particularly LinkedIn direct messages, are producing higher conversion rates when paired with clear ICP targeting and relevant messaging.
What matters is not the channel itself, but the discipline behind it. Experiments are measured against outcomes, not activity. GTM choices are evaluated based on their contribution to predictable revenue, not vanity metrics.
GTM Now Defines Value
As enterprise AI adoption accelerates, trust, risk management, and measurable performance are becoming prerequisites for scale. The same is true of go-to-market. Companies that can articulate how they acquire, retain, and expand customers with precision will stand apart.
For Maida, the implication is simple. GTM is a core determinant of company value, and those who treat it accordingly will build businesses that investors understand, trust, and reward.
Follow Eric Maida on LinkedIn for more insights.
