Revenue Operations has moved from a backstage function to a core component of modern business strategy. But just as companies master its principles, the next evolution is already here. As organizations invest more in RevOps, they face a fundamental shift driven by artificial intelligence. This is not just about adopting new tools; it is about embracing a new operational model centered on intelligent automation.
To navigate this change, we turned to an expert whose experience offers a clear view of this evolution. On an episode of The Go-to-Market Podcast, Fullcast Co-Founder and CMO Amy Osmond Cook sat down with Rachel Krall, Senior Director of Strategic Finance at LinkedIn.
After a decade leading GTM and RevOps at the tech giant, Rachel brings a crucial lens to the intersection of technology, finance, and revenue strategy. This article distills her insights into an actionable roadmap for integrating revenue operations AI, focusing on the foundational prerequisites and team structures required for success, especially as GTM challenges intensify according to the 2025 Benchmarks Report.
“One thing that I’ve heard said recently that I really like too is this idea of, you know, process documentation and just understanding what work people do… is gonna basically become a currency because it’s really the foundations for how we can then start to bring technology into different spaces and understand how technology can augment.”
- Rachel Krall, Senior Director of Strategic Finance at LinkedIn
Why Your Process Is the Real Currency for GTM Success
A critical mistake leaders make when adopting AI is layering sophisticated technology over a broken foundation. Rachel Krall cautions that true transformation with revenue operations AI is impossible without first establishing disciplined processes, clean data, and the right skills.
You cannot automate what you do not understand. Before you can achieve the benefits of AI, you must invest in the fundamentals.
Shift Your Mindset From Process Steward to Product Owner
The first step is a crucial mental shift. Modern RevOps teams can no longer be passive stewards of existing processes. They must become proactive designers and builders of the internal go-to-market “product.”
“One theme that I personally find pretty interesting and exciting is this shift from not just being process stewards… to being more product minded,” Krall explains.
She put this into practice by formally restructuring her teams to operate like product organizations, even hiring dedicated “business product owners” to manage the roadmap for internal GTM technology. This approach elevates the function from a reactive support role to a strategic driver of RevOps process optimization.
Master Your Workflows Before You Automate Them
Krall’s key insight is that the most successful AI implementations target mature, well-understood workflows. Throwing an AI agent at a chaotic process only automates the chaos.
“Our biggest opportunities right now for this type of agentic technology is in processes that are already relatively mature,” she notes.
This means the process must be standardized, have clear success metrics, and operate under established SLAs. If you cannot define what “good” looks like for a human, you cannot hold technology accountable to it. This essential groundwork of creating clear, enforceable rules forms the basis of any successful automation strategy.
These rules become the Automated RevOps policies that guide the AI.
Embrace “Citizen Development” to Foster Agility and Innovation
Building a tech-forward RevOps team does not mean every member needs to be a full-stack developer. Instead, leaders should foster a culture of “citizen development” by empowering their teams with low-code and no-code tools.
Krall shares a powerful example of her team using Microsoft Power Apps to build their own forecasting dashboards. “It’s allowing you to actually connect to a lot of different resources you have across your entire company,” she says.
This enabled them to solve their own problems and innovate on forecasting models without waiting in a long queue for formal engineering resources. This agility fosters a culture of technical curiosity and problem-solving, proving that with the right platform, companies can consolidate tools and accelerate GTM execution like Degreed.
How Revenue Operations AI Delivers Real Value
With a strong process foundation, RevOps teams can begin deploying AI to deliver tangible business outcomes. The goal is not to replace human expertise but to augment it, moving from manual guesswork to intelligent, data-backed decision-making.
Augment Human Judgment With Data-Driven Forecasting
Forecasting is a prime example of where AI combines human expertise with objective analysis. Historically, sales leaders have relied on intuition and manual adjustments. As Krall puts it, managers would say, “Oh, Carl always overestimates, I’m gonna take him down 20 percent.”
Today, an AI model can provide a more objective layer of analysis. By connecting to an OpenAI API, Krall’s team experimented with coding the sentiment in sales rep notes as positive, neutral, or negative. Over time, the model learns individual rep tendencies and provides a more normalized, data-backed forecast.
The manager is still essential but is now equipped with better intelligence to make smarter decisions, a process that moves teams away from the manual, spreadsheet-based planning that companies like Collibra have left behind.
Deploy “Digital Workers” for High-Volume, Rule-Based Tasks
Krall distinguishes between general AI tools and specialized “digital workers” or agents designed for specific, end-to-end tasks. These are not just productivity helpers; they are automated team members trained on a narrow, well-defined skill.
Consider a high-volume, rule-based process like contract review.
A company can train a digital worker to ingest a contract, extract key terms, flag anomalies against a standard template, check customer data in the CRM, and route the document for the correct approval. This frees up legal and ops teams from tedious, repetitive work, allowing them to focus on high-value strategic exceptions. This is the future of RevOps Data Automation.
Build a Future-Proof Tech Stack With Composable AI Agents
A more advanced approach to revenue operations AI involves creating a flexible, composable tech stack. This means deploying multiple, specialized AI agents that work together to complete complex workflows.
“For each part of that process, you may want like a different technological solution,” Krall advises. One agent might be skilled at document extraction, another at translation, and a third at summarizing and routing information. A central “supervisor” agent then deploys the right skill for the right task.
This federated model creates a powerful and adaptable automation engine that can evolve with the business. It mirrors how an intelligent GTM platform allows a company to execute complex territory designs in minutes, as Udemy does, instead of months.
Where Talent, Structure, and Technology Intersect
Integrating AI is as much a human challenge as it is a technical one. Success requires a thoughtful approach to team structure, skill development, and leadership to ensure the organization is prepared for this new way of working.
AI Will Make RevOps More Strategic
The fear that AI will eliminate jobs is pervasive, but Krall sees it differently. AI is an augmentation tool that automates the mundane, freeing up RevOps professionals to focus on higher-value strategic work like GTM design, performance analysis, and cross-functional leadership.
In an AI-powered organization, the role of RevOps as the “connector” across sales, finance, and product becomes even more critical. “Rev ops sits at the core of all of that,” Krall states. They are the ones who understand the end-to-end processes and can best identify the most valuable opportunities for automation, leading the RevOps revolution from within.
Align Your Reporting Structure With Your Company’s Core Goals
The debate over where RevOps should report is constant: to the CFO, CRO, or CTO? Krall argues there is no single right answer. The ideal structure depends entirely on a company’s strategic priorities.
- Reporting to the CTO: This structure is ideal for growth-stage companies focused on scaling efficiently through technology. As Krall observes, “They realized, well, we have an opportunity to figure out how we scale our business, but through technology as much as through people.” This is a model exemplified by hyper-growth companies scaling their business through technology, like Copy.ai.
- Reporting to the CFO/CRO: This alignment is often better for mature organizations where the focus is on financial optimization, predictability, and sales productivity.
The reporting line should reflect the primary goal the business needs RevOps to solve.
The Real Foundation for an AI-Powered GTM Strategy
The integration of revenue operations AI is a modern competitive necessity, but the path to success begins with people and process, not technology. As LinkedIn’s Rachel Krall makes clear, the most powerful AI tools are ineffective without a strong foundation of standardized workflows and a team that thinks like product owners.
This internal work elevates RevOps from a support function to a strategic driver of the business.
The leaders who will thrive are those who encourage their teams to take on unfamiliar challenges and embrace the role of a strategic technologist. The future of go-to-market is intelligent and automated, but it builds upon a human-centric framework of discipline and curiosity. The time to start laying that foundation is now.
