Meet the new power player in revenue teams
The AI revolution is reshaping how go-to-market (GTM) teams operate. Intelligent agents are handling tasks that once required human input, and forward-thinking companies are reorganizing around this shift. Those who act early will gain execution advantages that laggards won’t easily close.
We’ve seen this play before. CRM transformed sales in the 1990s, giving rise to modern sales ops. Marketing automation drove the creation of marketing ops in the 2000s. Each wave birthed new GTM roles—and created a competitive moat for early adopters.
The evolution of GTM roles
| Role | Adoption in SaaS | Key Driver |
| Sales Operations | Early 2000s | CRM adoption and need for process standardization |
| Sales Development Rep (SDR) | Early 2000s | SaaS scalability needs and lead generation automation |
| Marketing Operations | Early 2010s | Marketing automation tools and data-driven campaign tracking |
| Customer Success | Mid-2010s | SaaS retention focus and expansion revenue models |
| Growth | Early 2010s | Demand for full-funnel experimentation |
| ABM | Mid-2010s | Need for hyper-personalization for enterprise deals |
| RevOps | Late 2010s | Siloed GTM tools requiring cross-functional alignment |
| GTM Engineer | Mid 2020s (projected) | AI/automation tools replacing manual GTM tasks |
In the past, every new GTM role emerged in response to a substantial, structural shift. And, in every instance, early adopters–the companies that developed and recruited for those roles first–created a GTM advantage. These pioneers established an execution edge that helped them build and maintain market leads for 5-10 years while competitors struggled to catch up.
AI is today’s structural shift, and the GTM Engineer is its creation. As in the past, the dividends reaped by first movers will be significant. According to a 2024 McKinsey study, companies with leading digital and AI capabilities are already outperforming laggards by 2-6X in total shareholder returns across industries.
Organizations that quickly adapt around transformative technologies consistently outperform those that lag behind.
Takeaway: AI is the next wave. Early adopters aren’t just automating—they’re architecting execution advantages that will compound for years. Now is the time to invest in AI-driven roles and workflows before the competitive gap widens.
Enter GTM AI Operations, starting with the GTM Engineer
AI isn’t plug-and-play. To generate results, GTM teams need a new operational layer, GTM AI Operations (a.k.a. AgentOps). At the forefront of this organizational shift stands the GTM Engineer, representing the first of many roles that will define how companies orchestrate both human and AI agents across the revenue engine. The GTM Engineer is a hybrid of software engineer, RevOps architect, and GTM strategist, responsible for building the supporting infrastructure behind AI agents—LLM training, system integration, and process definition–and managing the ongoing performance of the agents.
RevOps brought much-needed alignment and accountability to GTM functions by breaking down silos between sales, marketing, and customer success, and unifying data and processes across GTM. AI Ops builds on this foundation, building and orchestrating a fleet of intelligent agents to act with speed, accuracy, and personalization.
Reporting into RevOps, AI Ops is a critical capability for supporting execution at scale. AI Ops ensures AI agents are:
- Strategically deployed to address critical, high value use cases
- Continuously trained and optimized
- Aligned with human workflows and revenue goals
- Reliable, high-performing, and ethically sound
The new GTM playbook: humans + agents
Welcome to the era of the hybrid GTM workforce. AI agents and human operators are now teammates, and leaders must decide who does what:
- Humans: crafting sales strategy, marketing creatively, building relationships with prospects and customers.
- Agents: delivering outcomes through a series of independent tasks.
Revenue planning now means answering:
- Which parts of the funnel should be owned by agents vs. humans?
- How do we measure performance across both?
- How do we orchestrate seamless collaboration between them?
Organizations that invest early in GTM AI Ops capabilities won’t merely automate existing processes—they’ll fundamentally outperform competitors by ensuring their digital teammates deliver consistent, high-impact results.
Takeaway: As AI agents become core contributors to GTM execution, companies that build dedicated AI Operations functions will unlock a powerful hybrid workforce advantage. To stay competitive, revenue leaders must redesign their orgs for seamless collaboration between humans and AI.
Final word: start now and secure your team’s GTM advantage
The GTM arms race has a new battleground. Companies building AI Ops infrastructure today will dominate tomorrow. Hybrid teams, powered by human ingenuity and AI scalability, are the new standard.
—————
AI in action:
Here are three things you should start doing tomorrow:
- Hire or upskill a GTM Engineer to stand up your first AI Ops capability
- Identify prime areas of your process that fit the profile for AI automation/augmentation, including repetitive workflows like outreach, lead qualification, or follow-ups
- Prototype a human-agent pairing, testing a hybrid model in one part of the funnel. Assign, measure, iterate