Or, why today’s GTM leaders must evolve or risk becoming obsolete
TL;DR: What GTM Leaders Must Do Now
- Appoint a GTM AI task force to lead cross-functional transformation.
- Redesign workflows to take advantage of agentic AI, not just tools.
- Prioritize solutions that integrate with your proprietary data to build competitive moats.
- Avoid “shiny object” syndrome: focus on high-impact, measurable use cases.
- Audit and simplify your current stack—then rebuild with an AI-native foundation.
Is your tech stack ready for the AI arms race?
We’re emerging from the last wave of sales and marketing tech. But this next one—driven by AI agents and autonomous systems—isn’t just an evolution. It’s a complete rewrite of the GTM operating system.
The very definition of software is being rewritten by AI agents capable of orchestrating complex, multi-step workflows with minimal human intervention (see how we define AI agents here). And the velocity of change is staggering.
This rapid evolution in AI capabilities, which far outstrips the pace of GTM software development over the last decade, demands a shift in how we evaluate and adapt our tech stacks. What’s working today won’t necessarily keep you competitive tomorrow, and simply being an early adopter of new trends isn’t going to cut it any more.
What does this mean for GTM leaders?
1. The Pace of AI Innovation Is Breaking the GTM Stack
AI in GTM is now a high-stakes game of musical chairs—and the tempo keeps accelerating.
Today’s category leaders—in predictive analytics, AI-powered prospecting, or sales enablement—face an existential truth: no one is safe. Disruptors rise fast, and just as quickly fall behind as new models, capabilities, and modalities enter the scene.
Why?
Model Velocity Is Relentless: In just a few years, AI has progressed from basic text generation to multimodal systems that rival human-level reasoning and performance on professional benchmarks. And it’s not slowing down.
Customization Is the New Standard: Off-the-shelf AI models won’t cut it. Companies are increasingly fine-tuning models on proprietary data, making generic solutions easy to replace.
AI-Native Beats AI-Enhanced: Tools that once added AI as a feature are being outpaced by fully autonomous systems that execute entire workflows.
Meanwhile, the rapid pace of innovation is causing chaos within GTM teams who are trying not to upset existing pipeline while they figure out where AI fits. Two-thirds of the GTM leaders we surveyed are exploring regular AI use in their GTM motions, and half of those had internal mandates, from the top down, to do so. The other third have taken a “wait and see” approach.
Betting on a current technology category leader might feel safe, but in a market where innovation is measured in weeks, not years, “future-proofing” your stack means staying agile.
Takeaway: Appoint a GTM AI tiger team (could be one SME to start) that works alongside RevOps, marketing, sales, and CS leaders to test, deploy, and scale new workflows. Treat this like a re-org, rather than an isolated tooling decision.
2. Agentic AI Is Creating a New Standard of Operation
Traditional software operates within predefined boundaries, and GTM organizations have long dedicated entire teams to calibrating and tuning these boundaries. That ends now. AI agents aren’t just glorified bots–they are general-purpose, configurable agents that can run complex business processes. In this way, they are better thought of as users than tools.
Agents are:
- Dynamic: Adjusting strategy based on real-time buyer behavior and unstructured data.
- Systemic: Orchestrating multi-step, multi-system processes (e.g., an AI sales agent doesn’t just schedule a demo, but also drafts personalized follow-up emails, updates Salesforce, and triggers nurture sequences based on buyer behavior.)
- Self-improving: Learning from results to optimize outreach, timing, and targeting.
We are already seeing the early shift in GTM operations, with teams starting to hire GTM engineers–roles that straddle ops, automation, and AI orchestration–to manage these agents and workflows.
Software categories will be redefined as GTM leaders question the necessity of existing tools in their stack. Think about it: when AI agents can be built to execute complex workflows across functions, will you still need half of your current tech stack? Point solutions, even AI-native ones, that only automate single steps of a process will be absorbed into larger, more capable AI ecosystems, presided over and orchestrated by GTM engineers.
Takeaway: RevOps teams must evolve into systems architects—assembling AI-native workflows that use a mix of off-the-shelf and custom agents to fill capability gaps and drive performance. Not every agent needs to be homegrown, but every workflow should be intentional, adaptive, and designed to evolve.
3. The GTM AI Gold Rush: Too Many Tools, Too Little Differentiation
The market is flooded with AI-powered GTM tools–four thousand of them, according to the latest G2 data. Every week, a new “game-changing” platform drops on Product Hunt. For GTM leaders, it’s tough to navigate the chaos.
Most of these supposedly “game-changing” solutions:
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- Lack sustainable differentiation, since they are built on the same LLMs doing the same tasks
- Offer more noise than signal, forcing teams to waste time vetting tools that overpromise and underdeliver
- Deliver high burnout, creating a substantial, accumulated tech debt for leaders that attempt to keep up with the tools du jour
In this crowded, fast-evolving landscape, GTM teams can’t afford to blindly adopt every new AI tool or to sit on the sidelines waiting to see which solutions take the lead. While AI unlocks exponential gains, the path to ROI is murky. The winners won’t be those who adopt the most tools, but those who deploy the right agents in the right workflows, powered by the right data.
Avoid an “AI for everything” approach. Instead:
- Align AI adoption to the most broken, most valuable parts of your funnel.
- Use general-purpose LLMs to prototype quickly. Then graduate to custom agents.
- Focus on tools that integrate seamlessly into existing systems while driving measurable outcomes.
GTM functional leaders can spin up AI workflows on their own, in a matter of hours or days; engineering and developer resources are not required.
The success of AI-adoption relies on a clear understanding of the underlying processes that work today. AI can then be applied to scale those processes in exponential ways.
Takeaway: Audit your processes. Use free models (ChatGPT, Claude, etc.) to test ideas. Then double down on high-impact areas using your own customer data, sales conversations, and engagement history to fine-tune how human resources are deployed. Your GTM advantage lies in the data others don’t have.
The Bottom Line
AI isn’t a tool category. It’s a new operating system for GTM.
The next generation of GTM leaders won’t just adopt AI, they’ll rebuild around it. Tech stacks will shrink. Workflow velocity will explode. And the line between “team” and “tech” will blur.
So the question is: Is your GTM strategy built to evolve—or destined to be disrupted?
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AI in Action:
- Identify one high-effort, lowest-yield workflow across sales, marketing, and CS.
- Prototype an AI workflow to automate or augment each.
- Assign a cross-functional AI tiger team to operationalize learnings over 90 days.