RESEARCH: From Outsourcing to Ownership - The State of AI in GTM
A new era of GTM is emerging—and it’s no longer about buying AI tools, but building AI expertise. Outsourcing AI strategy to vendors is a losing game. Just as no one would outsource their spreadsheet skills, high-performing teams are now internalizing AI fluency, treating it as a permanent capability rather than a one-off experiment.
Winning GTM organizations don’t “install” AI—they design systems around it. They start with process design first, defining specific jobs to be done (JTBD), and only then “hire” AI to perform those jobs. The principle is simple: don’t buy tools to solve undefined problems—clarify the problem, then apply automation with intent.
Process First, AI Second
Every major technological leap—from telephony to SaaS—began not with tools, but with process redefinition. The same is true for AI in GTM. Teams that succeed in deploying AI do so by embedding it at multiple points in the customer journey—rather than relying on a single vendor solution. This shift is accelerating rapidly: more than a dozen new GTM-specific AI tools launched in Q1 2025 alone, and leading teams are using modular combinations of agents rather than monolithic platforms.
Kyle Norton, CRO at Owner.com, exemplifies this shift. By designing an AI GTM “operating system” around existing workflows, he doubled ARR within a year—targeting high-impact stages and layering specialized agents to achieve automation and scale.
Designing AI Around the Job to Be Done
The most advanced teams now use the Jobs to Be Done (JTBD) framework to map AI opportunities. Instead of asking “what tool should we use?”, they ask:
- What’s the problem or friction?
- What inputs, outputs, and decisions define it?
- What should the AI handle—and what should humans own?
Each JTBD is then scored by complexity and impact to decide whether it’s an assistive AI task (AI supporting human judgment) or an agentic AI task (AI acting autonomously). This clarity ensures teams scale automation responsibly while maintaining human oversight.
Building Momentum with Quick Wins
AI maturity isn’t achieved in one leap—it’s earned through credibility and iteration. High-performing GTM teams start small, using targeted wins to prove value and build internal confidence. These early projects often include:
- Automating CRM enrichment and data hygiene.
- Using AI to summarize customer calls and surface risk signals.
- Deploying AI SDRs for low-stakes outreach and pipeline qualification.
- Creating “AI copilots” to streamline onboarding or account handoffs.
These small, well-scoped pilots quickly demonstrate ROI, turning internal skeptics into advocates and paving the way for broader automation.
The Role of the AI Strategy Owner
Every successful GTM organization featured in the research has one common denominator: an AI strategy owner.
This role sits at the intersection of data, process, and execution—aligning AI initiatives with business priorities, orchestrating cross-functional adoption, and managing vendor relationships without bias toward any platform.
Without clear ownership, AI initiatives risk fragmentation and stalled progress. With it, companies move from experimentation to execution, embedding AI as a structural advantage within RevOps.
— The Bottom Line —
The winners of 2026 will be those who stop renting AI capability and start owning AI strategy. They’ll know when to deploy vendors, when to build, and how to orchestrate AI as part of a connected GTM system.
AI isn’t a department—it’s an operating layer. And the companies designing for that reality today are already pulling ahead.
These insights are from a recent article written by Winning by Design Revenue Architect, Walter Velazquez Taboada. The full piece can be found in our Growth Journal - subscribe here.
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