How 2 AI Juggernauts Approach GTM Systems
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When you have near infinite resources and the smartest minds in AI/ML Research at your disposal, how does the approach to GTM Systems look compared to everyone else?
To find the answer, look no further than two titans of the AI era: Anthropic and OpenAI.
One is a refined version of the Salesforce-centric era; the other is the first true signal of a warehouse-native future with GTM Systems treated as internal products rather than back-office utilities.
Anthropic:
The Modern Traditionalists
The Anthropic team is characterized by a traditional, legacy GTM Systems organizational structure. They have adopted a "Leadership at the top" model with a blend of ICs across standard functions to fill out the org.
GTM Systems @ Anthropic (9)
The Philosophy
This Leadership group comes from fairly conventional backgrounds - Uber, Snowflake, LinkedIn, and other respectable Enterprise orgs. These are leaders with a very traditional approach to scaling systems infrastructure.
It's these traditional environments where you often see slower moving GTM Systems releases and a backlog of projects that is set with equal involvement from the Technology org and the various business stakeholders they support.
Even more surprising is the apparent split ownership structure for the GTM Systems team, which is split between Finance and Sales. This dynamic is all too common across companies of all sizes and often points to a simple reality:
Stakeholders disagree about how to get work done efficiently.
Individual business units often want to manage their own tech stack only if they lack confidence in a centralized Technology team's ability to handle it.
But this 'silo' of systems ownership structure creates friction as two separate teams are crafting two separate roadmaps, while attempting to build cohesive technology infrastructure for the company as a whole.
This creates problems even in traditional orgs but orgs with a siloed ownership model will make is 10x more difficult to properly adopt AI given the unique challenges presented by the need for unified data architecture.
(Additional Note)
Hiring Signals: Their open roles confirm this "Standard Stack" approach:
Revenue Systems Architect: Traditional GTM Systems Architect role with a primary focus on CPQ.
Business Systems Analyst: Traditional GTM Systems Analyst role working out of the usual stack, including Salesforce, Hubspot, Gong, LeanData, Clay, etc
Salesforce Administrator
OpenAI:
The Architect-First Model
In contrast to the top-down approach with directives set by Biz Tech Leaders and stakeholders from across the business, OpenAI has a heavy investment in the GTM Systems Lead layer - the critical piece of any team that we have long said carries the greatest impact.
GTM Systems @ OpenAI (15)
Person
The Innovation Hybrid
OpenAI recently established what they call the Go-to-Market Innovation team, described as "an incubator to amplify the impact of Sales, Technical Success, Enablement, and Revenue Operations by deploying our technology at scale."
The core mandate of this unit, led by Nickhil Nabar, is to drive rapid innovation around bringing AI into every team that engages customers.
The Approach:
Deeply embed with internal stakeholders to understand their problems
Conduct formal user research to fully understand the problem space
Create rapid prototypes to iterate on solutions FAST
Validate, synthesize, formalize, and ship scalable solutions
There are several factors that make OpenAI's org design and overall approach a potential model for how GTM Systems should look.
First and foremost, this is an organization that ruthlessly priorities speed, a challenge plaguing the vast majority of GTM Systems & Biz Tech teams.
Moving fast doesn’t mean shipping low quality features.
In a high skilled team, the ability to conduct user research, experiment, and rapidly prototype is a massive unlock - it allows you to test and iterate along the way instead of laboring over a solution for 3 months and shipping the finished product with no opportunity to look back.
This approach not only allows teams to ship at a much higher velocity but also results in higher quality solutions.
Second, the emphasis on GTM Systems Leads yields a high degree of autonomy. It allows GTM Systems Leads to become true Product Strategists, embedding deeply within a set workstream, and driving innovation forward on behalf of that team.
Finally, the addition of a GTM Innovation team layers unique capabilities on top of an already talented GTM Systems team.
The future of Go-to-Market Tooling in the AI era will look entirely different that the current approach. Rather than complete reliance on an ecosystem of existing vendors, organizations can build bespoke orchestration layers that sit directly on their data warehouse and aren't constrained by other platform's limitations.
At a basic level, this involves:
API-First Design as Infrastructure: Organizations moving from "CRM as the system of record" to "data warehouse as the foundation", with APIs providing the connective tissue.
AI as Orchestration Layer: The deployment of AI as the coordination mechanism across systems instead of simply embedding AI features inside monolithic platforms.
Machine-to-Machine Optimization: The fundamental unlock to adopt truly autonomous AI Agents is the ability to deploy composable systems designed for AI, not UI-driven human workflows.
This transformation requires a high degree of 'traditional' GTM Systems knowledge but skill sets that are currently atypical for a GTM Systems team will prove to be essential - backgrounds in Data Science, Product Engineering, and highly knowledgable in APIs, Python, JavaScript etc. These are all characteristics you see in OpenAI's current jobs:
The Cultural Chasm: Dictation vs. Autonomy
The adoption of this new approach - one grounded in heavy experimentation, rapid prototyping, and an increased technical capability within the team - will undoubtedly prove to be a challenge for companies to implement.
And the biggest reason isn't the inability for companies to hire the level of talent needed. It's largely due to the cultural shift required in how these teams work.
The Death of the Stakeholder Roadmap
In the old school model, the business teams dictate a high degree of what the technical teams should be building.
In the OpenAI environment, there is virtually no stakeholder-driven roadmap planning.
Stakeholders still play a critical role in crafting the roadmap but do so as collaborators - helping to provide the context needed to define problems and iterate on solutions.
But they are no longer able drop explicit directives on the roadmap.
Conclusion: The Future of GTM Systems
While OpenAI’s team is unique compared to what you see in the market today, it is a very clear signal toward the future.
The divergence between Anthropic and OpenAI isn't just a difference in org charts; it is a preview of two competing go-to-market methodologies.
Anthropic is the culmination of the SaaS Era: a world of specialized "Admins" and "Analysts" maintaining a complex web of third-party vendors, where the CRM is the sun and everything else is a planet in its orbit. It is a stable, governable, but ultimately reactive model.
OpenAI represents the Agentic Era. In this model, the GTM Systems team doesn't just manage vendors; they build internal products. They aren't constrained by the UI of a CRM or the limitations of a "no-code" flow builder. Instead, they are moving toward a Composable GTM Architecture:
While the OpenAI approach is unique today, it provides a clear signal of where the market is headed. The "moat" for future GTM teams won't be their ability to buy the best tools, but their ability to engineer their own orchestration.
The question for Rev Ops and BizTech leaders is no longer about how to support the business. It's a question of building systems that fundamentally power the business.