Your buyers’ inboxes are overflowing, and your GTM team is fighting a losing battle for their attention. According to recent sales performance benchmarks, just 14% of sellers are now responsible for 80% of new logo revenue. This statistic amplifies the pressure to find a sustainable competitive edge. While many leaders focus on adding new channels or increasing volume, the real advantage comes from rebuilding the GTM engine from the ground up.
On a recent episode of The Go-to-Market Podcast, we sat down with Kris Rudeegraap, Co-CEO at Sendoso, to discuss this critical evolution.
He argues that the most essential hire for modern revenue teams is not another SDR or marketer. He calls this new strategist the go-to-market AI engineer.
This article breaks down Kris’s insights on why this role is the key to unlocking future growth. We will explore how to move beyond saturated playbooks and build an intelligent, automated GTM motion that genuinely connects with buyers.
Why Yesterday’s GTM Playbook Is No Longer Enough
Success today isn’t about adding more channels; it’s about creating genuine differentiation by blending digital and physical touchpoints. The digital-first strategies that once guaranteed meetings and pipeline are now common practice, and the results are getting worse. Kris Rudeegraap’s journey from a top sales rep to founding Sendoso was driven by his firsthand experience with this saturation. This reality requires a strategic pivot from a digital-only mindset to a more holistic approach that prioritizes genuine connection.
From Mail Merge Advantage to Digital-Only Dead End
Kris’s entrepreneurial journey began with a simple observation about competitive advantage. “In the mid 20 teens, like 2014, for example, in sales, I was early to use Mail Merge,” he explained. “I could book meetings so easily and get 90% response rates from emails.”
But that advantage was short-lived. As email sequencing tools proliferated, what was once a differentiator became standard practice. “A lot of email sequencing tools popped up on the scene,” Kris recalled. “The original ones, like the Yesware and Tout apps, and then quickly Outreach and SalesLoft and all those came about.” The advantage he had built around being “the best mail merger” evaporated as the technology became accessible to everyone.
This pattern is familiar to every GTM leader. What works brilliantly when you are early becomes noise when everyone adopts it. So the question isn’t “what channel should I add?” but “how do I stand out in a meaningful way?”
Reclaiming Buyer Attention With a Multi-Channel Strategy
Faced with digital saturation, Kris discovered that the answer lay in blending digital outreach with tangible, offline touchpoints. “I started manually sending out gifts,” he shared. “I’d hear a dog bark on a call and send a dog toy after. I’d go steal swag from our marketing closet and pack it up or write handwritten notes after demos. And all of it worked so well.”
Kris became the top rep at his company, and his VPs encouraged him to keep expense reporting his gifting efforts. This experience taught him a crucial lesson about modern GTM strategy.
“I do believe in the power of digital, but I believe you can’t have a digital-only strategy,” Kris emphasized. “I think you need email, you need ads, you need content, you need direct mail offline, you need field events, and you need everything in order to grab the attention of your buyers or customers.”
The data supports this multi-channel approach. Research shows that 87% of B2B marketers report ABM delivers a higher ROI than any other marketing activity, with some organizations seeing a 171% increase in average deal size. To optimize campaigns effectively, leaders must look beyond single-channel metrics and embrace a holistic view of buyer engagement.
Three Ways to Calculate the True ROI of Your GTM Channels
Justifying investment in channels like strategic gifting requires a sophisticated approach to ROI measurement. Kris outlined three frameworks that savvy marketing and sales teams should employ.
Dollar-In, Dollar-Out: “Savvy marketing and sales teams should look into dollar in, dollar out,” Kris explained. “How do they look at $1 spent on Sendoso and what it can generate in dollars out, and compare that against other channels?” This direct comparison often reveals that gifting and direct mail outperform traditional digital advertising.
Cost of Execution: Beyond direct returns, teams must account for the hidden labor costs of manual execution. “It’s expensive to do this yourself,” Kris noted, “or it’s expensive to use high-quality or high-caliber labor, like a demand gen leader to pack boxes or upload spreadsheets.” Automation and outsourcing can dramatically improve unit economics.
The Cost of Inaction: Perhaps most overlooked is the opportunity cost of not scaling proven channels. “What if you could do 10 times more direct mail? What if you could do 10 times more content?” Kris challenged. “How do you look at what you’re leaving on the table as an ROI strategy?” Companies like Copy.ai have demonstrated what becomes possible when organizations commit to scaling their GTM motions aggressively.
Architecting Your Go-to-Market AI Engine
The GTM AI engineer isn’t just a new RevOps title; it’s a new function focused on architecting AI-driven systems, not just maintaining them. Getting your buyer’s attention requires more than a new channel; it demands a smarter operational backbone. Kris identifies the emergence of the go-to-market AI engineer as the essential next step for revenue teams. This role moves beyond traditional RevOps functions to actively design and build the AI-driven frameworks that will define the future of sales and marketing.
Defining the GTM AI Engineer More Than Just RevOps
When Kris describes the go-to-market AI engineer, he carefully distinguishes it from traditional revenue operations roles. “It’s not someone who’s just running Clay outbound,” he clarified. “It’s not someone who’s granting new users access to Salesforce or creating forecast reports for the CRO or doing MOPS campaigns in Marketo.”
Instead, this role represents a fundamental evolution in how organizations approach AI in revenue operations. “I think it’s truly someone who’s obsessed over AI agents, agentic workflows, someone who wants to build out these AI frameworks,” Kris explained.
The distinction matters because the traditional RevOps function focuses on maintaining existing systems and processes. The GTM AI engineer, by contrast, architects entirely new capabilities. They build the operational backbone that enables AI-driven execution at scale. This is the person who constructs the engine, not merely the person who keeps it running.
The GTM AI Engineer’s Toolkit From APIs to Agentic Workflows
A new role requires a new set of tools. Kris identifies several technical capabilities as critical for the GTM AI engineer. “Someone who’s savvy with APIs, data warehouses, some of these advanced tools like Claude Code for AI-powered development, or N8N for workflow automation,” he listed. “Someone who almost wants to be like an AI architect.”
This toolkit reflects the shift from traditional CRM-centric operations to an AI-first CRM paradigm. The goal is to build an interconnected GTM system where data flows seamlessly between platforms and AI sales agents can execute complex, multi-step tasks autonomously. The technical bar is higher than traditional RevOps, but the leverage is exponentially greater.
Translating Human Strategy Into Autonomous AI Execution
The core function of the GTM AI engineer is translation. They take high-level human strategy and operational requirements and convert them into specifications that AI agents can understand and execute.
As Kris put it, this role is about building systems that “can translate these human roles and tasks into specs that agents can then take advantage of.” This translation layer is what enables organizations to scale complex GTM motions without proportionally scaling headcount.
Platforms like Fullcast Copy.ai are designed to support this translation process, unifying workflows across marketing, sales, and RevOps. The key is to integrate AI into core workflows rather than treating it as a standalone tool.
How to Build and Sustain an AI-Powered GTM Team
To effectively deploy AI, you must first unify your GTM data and processes into a single source of truth. Adopting an AI-first GTM approach requires leadership support, careful planning, and a commitment to continuous learning. Leaders must not only hire for these new skills but also foster an environment where data-driven experimentation and innovation can thrive.
Unifying Your GTM Motion for AI-to-AI Engagement
Before you can effectively deploy AI, your GTM data and processes must be unified. From an operator’s perspective, AI is useless without clean, unified data; your AI engine can’t generate pipeline from a messy CRM. This makes a solid operational foundation the critical first step.
This means creating a single source of truth for planning, performance, and compensation. Organizations that want to prepare your GTM motion for the AI era must invest in data infrastructure before investing in AI capabilities. A comprehensive AI in GTM strategy starts with operational clarity.
The RevOps function plays a crucial role here, ensuring that territory planning, quota setting, and performance tracking all operate from consistent, accurate data. Without this foundation, even the most sophisticated AI tools will produce unreliable results.
Fostering a Culture of Continuous GTM Innovation
Staying ahead requires a relentless focus on what comes next. Kris shared his personal strategies for staying ahead of new trends, offering a model for other leaders.
Active Information Diet: “I use an app called Feedly and I basically track 200 plus blog or media outlets,” Kris explained. “Instead of doom scrolling on Instagram, I’m doom scrolling through news articles that are relevant to me in the industry.”
Leveraging Networks: “I also have a unique network of about 100 plus advisors that I go to from time to time when I have challenges or when I want to think about a new product launch,” he shared. These relationships provide perspective and pattern recognition that no algorithm can replicate.
Internal Intelligence: Perhaps most valuable is Kris’s practice of regularly interviewing his own team. “I’ve been interviewing my team on what they’re doing,” he noted. “How are engineering teams using Claude Code? What tools are our SDR team using and what’s booking them the most meetings?”
This commitment to continuous learning is essential for leaders who want to launch your first experiments with AI-powered GTM. Those who lead with AI rather than following will capture disproportionate advantages as the technology matures.
Final Thoughts
You can no longer win by simply sending more emails. As Kris Rudeegraap’s journey illustrates, the playbooks that once delivered 90% response rates have become background noise. The future of go-to-market success lies not in increasing volume, but in building a smarter, more efficient, and deeply integrated revenue engine.
The emergence of the go-to-market AI engineer signals a crucial shift from reactive operations to proactive, AI-driven strategy. This role combines technical fluency with strategic vision to architect the systems that will define competitive advantage for years to come. For revenue leaders ready to act, the path forward is clear: unify your data, evaluate your team for new skills, and cultivate a culture of relentless innovation.
