The fastest-scaling companies almost always have strong AEs or technical sellers driving them. At Dynamic, we saw 5 companies in 2025 reach $1B+ valuations, do $250M+ in ARR, and do this collectively with only 9 AEs across those 5 companies. Nithya at Outreach is working with a customer who reached $1B ARR with only 10 sellers through a product-led motion. This will continue to be the trend!
TL;DR — automation in GTM is coming for transactional work. But judgment, listening, and the human side of selling will matter more, not less, because prospects can now educate themselves through agents, chat, and self-serve content before they ever talk to a rep.
That's why GTM is getting harder for inexperienced sellers, and why it will be one of the biggest windfalls for companies that pair elite sellers with AI to accelerate revenue. From our point of view, transactional workloads will continue to be more and more AI-driven, but the buying experience will allow enterprise sellers to strategically allocate their time toward high-value accounts — driving even more ARR than humanly possible in previous cycles.
The future will require elite GTM talent to operate alongside the machines. To dig into this, I had the honor to host a panel, bringing together technical Founders and leaders who sell products to technical executives in enterprise software.
You can watch the full session below, and the breakdown that follows covers improving the buying experience by focusing on the product experience and the impact that has on the traditional enterprise sales motion.
This post recaps the talkGTM in an AI-First World — TiEcon 2026Watch on YouTube
Improving the buying experience
"And so when you kind of derive from first principles, who are the people in an organization that seek to understand customer pain, that map out what's the buyer journey, what are all the different things that they need, what are all the things that need to happen for this customer to be successful? It turns out those people are called salespeople."
— Michael Grinich, CEO, WorkOS
The buying experience is part of the product experience. A company can build excellent software, but without someone helping the customer successfully purchase and adopt it, the company is like a restaurant that cares about the food and as Michael describes it, "drops the plate on the way to the table." The routine work will be handled by agents — qualify the buyer, answer routine questions, and determine when a person should enter the conversation. See how Docket does this today, and how WorkOS' launch of Ask WorkOS makes things easy for the customer, sales, and solutions engineers to only engage when the prospect is qualified. Vinay at BREVIAN explained his firsthand experience being offloaded to HubSpot's agent, Fiona, to handle a simple transaction.
All of this means the best GTM organizations are more product-centric. The best sellers uncover needs and problems the buyer hasn't yet recognized. Their discovery goes deep enough that the buyer starts to see their own situation differently.
Pricing is transparent. When someone signs up, they get an invitation to a shared Slack channel — a low-friction way to reach the company without entering a traditional sales sequence. A call is still available, but as an optional exit from the self-service highway rather than a required toll booth.
"When a buyer is educated coming into that first call, the seller needs to be much more prepared. It's not about, 'I'm going to start with discovery question A, B, C.' They expect you to have done the research and to have the right framing."
— Nithya, CPO, Outreach
Marketing and sales are increasingly overlapping
Traditional SEO is now joined by AEO, agents, and chat interfaces, all of which limit how much a vendor can shape the conversation before any human touchpoint. Customer proof, social proof, and visibility inside AI-mediated discovery are becoming essential, because a company that doesn't surface during that early research may never reach the buyer's shortlist.
Sophisticated buyers already do extensive research on their own, which means companies have to meet them in the channels where that research happens. The buying experience shifts on both sides: buyers expect to learn something on every call, and if they don't, you probably won't get another one.
Much of that discovery now runs through products like ChatGPT, Perplexity, and Claude. A buyer can describe what they're building and ask which vendors to use.
"Very honestly, nobody wants to talk to salespeople. The whole point is that you used to talk to salespeople because there was an information asymmetry. The salespeople knew the domain. So you're like, 'Hey, in financial services, who's buying these products? What are they using them for? How are they buying this and securing it?'
That goes away now because the information asymmetry is gone. In fact, buyers actually know more than the sellers at this point.
The buying process shifts to a set of AI being able to synthesize your requirements, map them to tools that exist, give you ways to evaluate them, and then you filter out a couple of vendors and talk to them.
That's why the stat that 80% of buyers buy the first vendor they talk to is not because they don't go and talk to other people. It's because they've already researched, and they're talking to the top two people. That's how it changes."
— Vinay, CEO, BREVIAN
The future buying process is therefore partly agent-to-agent, with people entering at the point of commitment, judgment, or approval.
Nithya and others expected some version of this: exposing company information and services directly to buyer-facing agents, rather than reserving technologies like MCP for internal employees. That would give vendors more influence over what buyers learn, instead of leaving the company's representation entirely to whatever an external model decides to say.
So what does that mean for Enterprise and Sales-led?
The future seller will need to uncover the real problem and the technical understanding to connect that problem to the product. Although this has always been a "best practice," most sellers could hide behind their organization's momentum. The real-time buying experience requires people to be excellent at this or they will lose deals — buyers now have so many options and will bet on the company most aligned to their understanding of their problems and needs. Software is easier to build than ever, and people know this as well.
"The best sellers are good at finding the unknowns—the problems and needs that even the buyer may not realize they have. They go deep enough in discovery that the buyer says, 'Wow, I didn't know this existed.' That depth of discovery, combined with real product expertise, is probably what defines the future seller. We may need fewer sellers, but those who remain will need to pair deep discovery with product selling."
— Arjun, CEO, Docket
The best companies will always seek to go up-market with human sellers, and it has the highest-leverage opportunities for the use of AI. What used to be relationship- and rolodex-driven sales through exclusive steak dinners is now turning into "knowing the customer extremely well and coupling that with product expertise" to earn the time with buyers who are offered hundreds of steak dinners per quarter or have direct access to the C-level at your closest competitors.
This is producing a modern enterprise motion, or what we call a "deferred enterprise motion," where AI and product handle most of the upfront work so the multistakeholder engagement that defines enterprise selling becomes the seller's primary focus instead of a secondary effort squeezed in around CRM updates and admin.
The seller's strategic energy goes to coupling the product's value with the prospect's expected value. To Nithya's point, "AI can identify the right accounts, prioritize opportunities, run research, and clear away administrative work, freeing the seller to focus on the conversations and organizational work that move a complex deal forward."
Michael described the future seller as a creative problem solver — a kind of "sociological engineer" who understands the organization, connects the necessary relationships, and identifies what must happen for the customer to succeed with the product. This is less like operating a sales process and more like designing a path through an organization.
He also observed that every large customer is structured differently. Their teams, goals, ownership, and internal problems do not follow a standard template. A customer may quickly agree that the product is useful but remain unable to determine how to purchase it because too many departments must approve the decision.
Real-world example: WorkOS includes a developer-success engineer or solutions engineer on early calls (or on Slack) so the key stakeholder does not have to schedule another meeting to get a technical answer. The underlying principle is to respect the customer's time. Human involvement should add immediate value, not merely advance an internal sales process.
The best technical GTM teams
- Go up-market efficiently and fast by leveraging AI and customer data to support elite sellers.
- Treat pricing as a first-class citizen in your GTM motion — reducing friction for customers and creating a model that makes it easier for sellers to disarm prospects by providing transparency and a simple process (e.g. "pricing is public, here's a Stripe link").
- Move from traditional enterprise sales to a modern (deferred) enterprise motion. By focusing on technical wins and product adoption, sellers are in a better position to support a wider set of clients and use data, signals, and triggers to determine when to lean in technically or commercially to drive a deal if necessary.
Thank you to Gamiel at Mayfield for having me speak at TiECon and host Arjun at Docket, Vinay at BREVIAN (shoutout to a fellow Databricks alum), Michael at WorkOS, and Nithya at Outreach.