Every technology wave creates opportunity.
With the internet, it was websites. With mobile, it was apps. With cloud, it was SaaS. But with AI, the opportunity is more broad as intelligence becomes ‘on-demand’.
That’s also why so much of the GTM conversation right now seems muddled, noisy. The opportunity is incredibly broad which makes locating a starting point difficult at best. When everything is a possibility, it’s tempting to begin by attempting to solve for the problems GTM has struggled to get right for years. AI prospecting. AI-written content. AI workflows that promise to remove people from the slow, repetitive, painful aspects of revenue generation.
Some of that will work, some won’t, and some already is.
We recently had an AI for GTM Roundtable and the most interesting finding was not that companies are using AI to do more outbound or create more content. It was that the best operators are applying AI to problems that have always been hard: coaching reps, improving hiring, understanding why deals are won or lost, shortening ramp time, surfacing buyer signals, and helping teams make better decisions with the data they already have.
That same pattern kept showing up in recent episodes of the Cheat Code & Friends podcasts. For example, Hannah Ajikawo, Jake Dunlap, Mike Damphousse, Matt Heinz, and Kris Rudeegraap each came at the topic from a different angle, but the common thread was clear. AI is not magically fixing GTM. It is exposing where GTM was already weak.
Everyone Is Chasing the Obvious Opportunity
There is nothing wrong with using AI for prospecting. The problem is treating prospecting as the center of the AI conversation.
Jake Dunlap made this point clearly when we asked why so many companies immediately gravitate toward the SDR role. Jake’s view was not that AI has no place in outbound. It was that too many companies are using AI to scale the same tired motions instead of improving the quality of the work.
Jake said it best, “The companies that are really doing a good job are not trying to automate outbound. Instead, they say, “I can make my 23-year-olds sound like 20-year vets.”
That distinction matters for early-stage companies. A founder does not need more mediocre outbound. They need better discovery, better account understanding, better messaging, and better judgment from the people having customer conversations.
The roundtable showed examples of teams using ChatGPT Agent Mode to build value propositions and account plans, then turning them into audio summaries so reps could rehearse before meetings. Others used AI-generated personas, prospect research, and pre-call preparation to help sellers enter conversations with stronger context.
That is a better use of AI than simply increasing volume. The market already has enough noise. The founders who win will be the ones whose teams show up with more context, more relevance, and more ability to help the buyer move. Not the ones who send the most emails.
Good Companies Get Better. Weak Ones Get Exposed.
AI rewards operational discipline. It does not create it.
The roundtable’s strongest ROI examples weren’t one-off experiments. They were the outcome of focused, strategic efforts. They were tied to specific bottlenecks inside the revenue engine. One agent analyzed call transcripts, graded deal strategy, and recommended next steps based on historical best practices, producing a 30% boost in closed deals over two months. Another custom sales agent helped grow bookings by 30% and saved more than 225 hours per week per team through more focused coaching.
Those examples matter because they are not really about software. They are about repeatability.
Jake made a similar point when he talked about sales leaders building their own GPTs. His argument was that managers waste too much time answering the same tactical questions when their real value should come from coaching people through higher-order skill development. “Sales leaders, you should all have your own GPT,” he said, adding that a leader’s value is not “answering the same question 50 times.”
This is where Mike Damphousse’s perspective becomes useful. In his conversation on category design, he described the work as something that started as marketing but became “full business strategy.” Later, he said the biggest takeaway from the work is “the process itself,” because companies are making a strategic move and “re-engineering” themselves.
That is exactly the trap with AI adoption. Too many companies still treat it like a tool decision. Which platform should we buy? Which workflow should we automate? Which role can we make cheaper? The better companies are asking a different question: where is our process already breaking, and can AI help us fix it faster?
That is why AI will widen the gap between disciplined operators and everyone else. If your sales process is unclear, your CRM data is bad, your coaching is inconsistent, and your ICP is fuzzy, AI does not solve those problems. It scales them.
The interesting thing is that all of these examples point to the same outcome. AI isn’t just helping companies become more efficient. It’s helping them make better decisions. And when every competitor has access to the same AI tools, better decisions become one of the few remaining ways to earn buyer trust.
Trust Gets More Valuable When Everything Else Gets Cheaper
The other major theme from our conversations is that AI is increasing the value of trust.
That may sound counterintuitive, but it makes sense when you look at what is happening in the market. Content is cheaper to create. Products are faster to build. Outbound is easier to automate. Competitive noise is higher. Buyers have more information and less certainty.
Hannah Ajikawo framed this through product parity. “You need to differentiate through execution and people, right? Because product parity is so real, like so much looks the same.”
Kris Rudeegraap made a similar point from the perspective of buyer experience. As buying journeys have evolved and products have become commoditized, companies have to “grab people’s attention differently” and “engage and build that relationship differently now.”
Matt Heinz took it one step further. His advice was not to start with the technology at all. “Strategy first, process second, technology third,” he said. Later, he talked about the need to build customer self-confidence, not just seller productivity. The more interesting use of AI is not another appointment-setting motion. It is helping buyers understand the pain, evaluate the tradeoffs, and build enough conviction to act.
For founders, this is a critical distinction.
AI can help you generate content, research accounts, summarize calls, and build workflows. It cannot give you credibility. It cannot make your product matter. It cannot create trust with a buyer who does not believe you understand their problem.
The companies that use AI well will not just move faster. They will help buyers feel more confident. In a noisy market, that may be the better advantage.
The Better Question
The AI for GTM roundtable started with a simple question: what is actually working?
The answer was not one category of tools or one department inside the revenue organization. The strongest examples came from teams applying AI to the unglamorous parts of GTM: coaching, hiring, account planning, deal analysis, workflow automation, forecasting, and buyer experience.
That is probably the lesson for early-stage founders and the investors backing them.
The first question should not be, “What can we automate?” That question usually leads to shallow answers.
A better question is, “Where are we making weak decisions, creating unnecessary friction, or failing to help the buyer move?”
That is where the more durable AI use cases are showing up. Not in replacing the work, but in understanding the work well enough to make it better.
Every technology cycle produces a rush toward shortcuts. The companies that endure usually do something less glamorous. They use the new technology to become better operators while everyone else is busy chasing the obvious thing.
AI will be no different.
