There’s a new sales equation taking shape, one that redefines how growth is created and sustained. Sales is entering a phase where intelligence has become the key factor. What once depended on manual effort is now being reshaped by systems that can learn, prioritize, and act in real time. For Daniel Saks, CEO of Landbase, this shift is also defining the future of go-to-market (GTM) strategy. “There are some things that are inherently human that will be very important and then there are other things that are inherently better done by AI,” he says.
This distinction is reshaping how organizations think about machine intelligence, human performance, and the structure of modern sales teams. Saks’ thesis centers on blending human and machine intelligence in sales to unlock AI-powered growth, while preserving the uniquely human elements that drive trust and conversion. The goal is not replacement, but coordination.
Redefining the Role of Sales Teams
The traditional GTM stack has long relied on humans to power systems designed for record-keeping. Customer relationship management (CRM) updates, forecasting, and data enrichment have consumed hours of manual effort. Saks sees this model as outdated. “The last 30 years of software were humans putting things into a database,” he says. “We’re moving to where software works for you.”
This transition is at the heart of agentic AI and autonomous workflows. Instead of acting as data entry operators, sales teams are being repositioned as relationship builders and strategic advisors. AI handles repetitive processes such as identifying target accounts, sourcing contact data, and prioritizing opportunities. Humans focus on articulating value and building trust.
For early-stage companies and enterprise teams alike, this shift signals a move from bloated GTM stacks to streamlined, AI-driven GTM strategy for B2B organizations. It is also redefining how leaders structure teams, often placing revenue operations (RevOps) at the center of orchestrating human and machine collaboration.
From Activity to Intelligence-Led Selling
As execution becomes more automated, the competitive edge shifts to how well teams interpret and act on data. “The teams that have the intelligence will win,” he says. This intelligence is derived from signals. Website visits, product trials, ad engagement, and online behavior all provide insight into buyer intent. Instead of relying on intuition, sales reps can structure their day around these signals, prioritizing high-probability opportunities.
This approach is already changing outbound conversion rates. By focusing on relevance rather than volume, teams can dramatically improve performance. “The best way to run campaigns is to not spray and pray, but be very targeted on who you’re reaching out to – when and why.” The result is a measurable increase in efficiency. Organizations leveraging signal-based personalization are seeing significantly higher engagement, with some achieving up to five times reply rates compared to traditional outbound methods.
Scaling Personalization with Machine Intelligence
One of the persistent challenges in GTM automation has been scaling personalization without losing brand voice. Saks believes the answer lies in combining machine intelligence with domain-specific models. At Landbase, this has led to the development of systems that can identify, score, and prioritize entire markets. “We can quantify that addressable market and then score and prioritize that list on who you should focus on, based on all these signals,” he says.
This capability allows companies to move from slow GTM processes to exponential growth. Instead of casting a wide net, teams can focus on high-fit accounts that demonstrate clear intent. In some cases, this approach has expanded a company’s addressable market by 33%, uncovering opportunities that would have otherwise gone unnoticed. The broader implication is a shift toward VibeGTM, where launching campaigns becomes conversational and intuitive. Sales and marketing teams can scale campaigns with machine intelligence, while maintaining precision and relevance.
The Future of Go-To-Market
While the technology is advancing rapidly, the human dimension remains critical. The introduction of agentic AI raises questions about job security and trust. “We are in this era of uncertainty in which no one knows the way things are going to pan out,” he says. His approach to leadership centers on transparency and upskilling. Rather than offering false assurances, companies should invest in AI fluency across their workforce.
At Landbase, even non-technical employees are trained to automate their own workflows using advanced tools. “We have a simple framework: own it, automate it, elevate it,” says Saks. Each team member takes responsibility for a function, uses AI to streamline it, and continuously improves the outcome. The shift from systems of record to systems of action marks a defining moment for sales organizations. As AI continues to evolve, the companies that succeed will be those that embrace autonomous workflows, while doubling down on human performance.
Follow Daniel Saks on LinkedIn and X (formerly Twitter) or visit his website for more insights.
