Mika Sagindyk is Co-Founder at Tackle, where she acts as a Product and GTM leader.
When ChatGPT first entered the market, much of the excitement in the go-to-market (GTM) space centered on AI sales representatives that could replace humans, completely automating the grind of prospecting.
Yet nearly three years later, I think it is clear that the real impact of AI in GTM has not been substitution but augmentation. Sales has always been about helping people solve problems, and AI now enables humans to work with more leverage: identifying the right customers faster, speaking more clearly in the customer’s own language and moving deals forward with less friction.
What’s Actually Changing
Since large language models (LLMs) became widely accessible, GTM has been one of the business functions most visibly reshaped by AI. A natural starting point has been customer support—one of the earliest workflows companies moved to automate. Intercom, for example, has made AI-powered support central to its strategy, using agents to instantly resolve simple customer requests while freeing humans to focus on more complex interactions.
Companies across GTM are using AI to summarize conversations and detect deal risks in real time. As a part of this movement, the concept of a GTM engineer role, which combines technical and GTM skills to design and execute scalable workflows, is an idea pioneered by Clay and adopted by many others. In fact, at my own company, we’ve followed a similar path. By orchestrating the GTM motion and embedding AI elements into it, we’ve been able to accelerate the overall customer experience.
The common thread across these examples is clear: The goal is not to replace humans, but to enhance their ability to focus on higher-value work.
Designing The Modern AI GTM Stack
At first glance, the AI market for sales may feel crowded with tools that sound interchangeable, which makes it easy to confuse volume with value. A practical way to cut through the noise is to view the stack through five archetypes:
1. Co-Pilots: These are assistive layers that guide reps with context, such as meeting preparation or suggesting next steps. This includes co-pilots like Gong Engage and HubSpot AI.
2. Purpose-Built Tools: These focus on solving narrow problems like outreach, account research or personalization. Think: Outreach, Apollo and 6sense.
3. AI Agents: The focus of this article, these act as digital teammates that can qualify leads, answer routine requests or route conversations. Popular examples are Intercom Fin AI and Forethought.
4. Workflow Builders: Examples you may have heard about include Zapier, Clay, Make and n8n. These tools stitch together systems and processes to automate repetitive steps.
5. Direct Integrations: Like purpose-built tools, these are custom API or LLM projects that adapt AI models to an organization’s unique data and workflows (e.g., OpenAI APIs).
Implementing AI in GTM isn’t about adopting every tool, but identifying where your team benefits most. It may feel tempting to simply pick one of each archetype, but in my experience, that will only add to the noise.
Instead, map the ideal customer journey and focus on points that improve key metrics, such as better qualification, stakeholder management or faster pilot outcomes. Once goals are clear, it is easier to choose tools that accelerate progress. Keep on top of the AI landscape, though, for it is still evolving; it’s up to GTM professionals to decide what truly moves the needle.
A Practical Case: Positioning In Minutes
My team applied this mindset to one of the most critical activities in GTM: refining positioning. With hundreds of customer conversations, it was easy to get lost in volume and noise. We fed LLMs with our context and asked targeted questions such as: What words do customers actually use to describe their problem? How do they frame the impact we create? What outcomes matter most to them?
By surfacing these insights, we could focus on the most essential information and truly understand the customer’s voice. AI helped us synthesize patterns in minutes, which we then used to craft positioning that spoke directly in the language of our best-fit customers.
The result was transformative: Demo requests that were previously only 40% qualified became 90% qualified, and our sales cycle shortened by half. AI didn’t replace the work of understanding customers; it amplified our ability to hear, interpret and act on the signals that matter most.
What GTM Leaders Should Do
Again, the imperative is not to chase every AI announcement, but to apply discipline in how you design your go-to-market systems. To emulate our success in incorporating AI, here are five practical steps I find helpful:
1. Identify bottlenecks. Focus on where deals are stalling, qualification is weak or messaging lacks consistency, and apply AI there first.
2. Measure quality, not volume. Optimize for deal velocity, win rates and customer sentiment rather than outreach counts or ticket deflection rates.
3. Avoid spray-and-pray. Resist using AI to mass-produce outreach or content, and instead invest in sharper ICPs, cleaner segmentation and more relevant messaging.
4. Enable experimentation. Give teams permission to test new tools in small pilots, measure outcomes and iterate without over-engineering the stack.
5. Blend human and AI strengths. Let AI generate drafts and summarize conversations, but rely on humans to edit, contextualize and connect authentically.
Final Thoughts
The real opportunity lies in continually elevating the customer journey. GTM professionals now have leverage they didn’t have before to identify the right customers faster, understand them more deeply and advance deals with less friction.
Leaders who treat AI as a shortcut to volume risk diluting their brand and losing trust, while those who use it to amplify intelligence and relevance can build resilient teams that thrive over the long term. Focusing on augmenting GTM professionals rather than substituting them means prioritizing lasting value over short-term results. In GTM, leverage has always been the true advantage—and I believe AI promises to amplify it like never before.
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