Revenue Operations leaders face a defining choice. While the pressure to use AI for growth is immense, one fundamental question complicates the path forward: do we build AI solutions in-house, or buy them off the shelf?
This decision extends far beyond a simple budget line item. It is a strategic choice impacting team resources, long-term maintenance, data governance, and go-to-market agility. As industry leader Cory Coult explains, the wrong choice can lead to bloated costs and brittle systems, while the right one creates a powerful operational advantage.
In a recent episode of The Go-to-Market Podcast, host Amy Osmond Cook, Co-Founder & CMO at Fullcast, sat down with Cory Coult, Senior Director of Revenue Operations at Avalara. Having architected revenue engines from the inside as a RevOps leader by treating RevOps as an internal product, his perspective provides a pragmatic framework for navigating this complex landscape.”
As the 2026 GTM Benchmarks Report notes, “AI amplifies what’s already there. It doesn’t create strategy.” This makes operational implementation vital. This article breaks down Coult’s strategic framework for making the right AI build vs. buy decision, transforming a risky choice into a calculated, value-driven process. The choice between building and buying an AI solution is a strategic decision that defines a company’s operational future.
Your Revenue Engine Is Now a Product: The New Mindset for AI-Powered RevOps
The role of Revenue Operations has fundamentally shifted. What was once a support function focused on reporting and system administration has evolved into something far more strategic. Understanding this evolution is essential before any build vs. buy decision can be made. To make the right AI decision, RevOps leaders must shift their mindset from system administration to product management, treating their revenue engine as an engineered product.
From Reactive Reporting to Proactive Engineering: The New Mandate for RevOps
Coult describes experiencing three distinct eras within revenue operations. The first focused on basic administration and keeping systems running. The second centered on productivity scaling and doing more with existing resources. Now, we have entered the era of go-to-market engineering.
“I feel like the best operators are the ones that are weaving them together in a single, unified view for the whole business,” Coult explains. “And that’s extremely challenging.”
The challenge lies in transforming legacy CRMs from reactive lookback tools into proactive, predictive engines. This requires RevOps professionals to think like product managers rather than system administrators. When leaders want insights into their forecast, someone must layer technologies like Gong, Outreach, and Salesforce together, extract data proactively, and make it actionable. This integration challenge sits at the heart of every build vs. buy decision.
Why Every AI Initiative Should Launch as a ‘C Product’
Coult introduces a powerful framework for managing leadership expectations around AI implementations. He calls it the “C product” approach.
“I’m building a C product. Expect a C product,” he states plainly. “It takes a lot to go get it to a B and an A, and the amount of work that I go put into it will go deliver that B or an A. But if you want a quick C product, I can go deliver that.”
This philosophy is a reality check for teams dreaming of a silver bullet. Rather than promising transformative results from day one, leaders should set expectations that initial implementations are functional first drafts. The path from C to B to A depends on adoption and proven value. This iterative approach is crucial for avoiding AI project failure, which often stems from a mismatch between expectations and reality that can burn out teams and erode leadership’s trust.
“A lot of people start a project assuming A, and you get a C, and then that’s where the trouble starts,” Coult warns.
The Hidden Tax of Innovation: Factoring in Maintenance and Governance
Building AI solutions creates ongoing obligations that many organizations underestimate. Coult emphasizes this point directly.
“You’re building products inside of your business that you don’t just build, ship, and leave alone,” he explains. “You have to maintain it and manage it all the time.”
This maintenance tax includes data integrity, model updates, security, and process governance. If you add a new field to your system, you do not just ask another team to build it. You own the data in that field indefinitely. This reality must factor into total cost of ownership calculations when deciding whether to build or buy.
A Pragmatic Framework for the AI Build vs. Buy Decision in Revenue Operations
With the right mindset established, Coult outlines a step-by-step process for evaluating AI solutions that RevOps leaders can apply immediately. A practical build vs. buy framework starts with an internal proof-of-concept, weighs vendor trade-offs, and balances speed, cost, and quality against available talent.
Step 1: Build to Validate: Can You Create a Viable ‘C Product’ In-House?
Before evaluating external vendors, Coult recommends attempting to build a minimum viable version internally. The goal is not creating a perfect, scalable product. Instead, it validates the business case, reveals technical complexities, and establishes a baseline for what “good” looks like.
“If I can do it myself with my team, I’ll try that to do it myself with the tools we have,” Coult shares. “Now, if we get a D product, I might say like, hey, we might want to go buy enterprise class products.”
This internal trial serves as a proof-of-concept that de-risks larger investments. This initial build phase helps you determine whether you need to commit to a custom LLM or if a model-agnostic approach is a better fit.
Step 2: Weighing the Trade-Offs: Enterprise Behemoth vs. Cutting-Edge Startup
When the internal build proves value but lacks scalability, the focus shifts to buying. Coult identifies a key dilemma in this phase.
“You go with the innovative ankle biters that are new, or you go with the behemoths that are established but behind in skills,” he observes.
Enterprise solutions offer stability and proven track records but often lag in AI innovation. Startups deliver cutting-edge capabilities but carry acquisition or failure risks. This often comes down to a choice between an org-wide solution from an enterprise player or a specialized AI point solution from a startup. The right choice depends on organizational risk tolerance and specific use case requirements.
Step 3: Balancing the Triangle: How Talent and Expectations Drive Speed, Cost, and Quality
When asked how to achieve speed, cost efficiency, and quality simultaneously, Coult identifies two critical factors.
“I think it comes down to the talent. Like the person in the chair really dictates a lot of those three,” he explains. “The second I think is great communication and the agreement of what this is and what to expect.”
Buying often prioritizes speed while building allows more control over quality and long-term cost, assuming you have the right talent. This strategic balancing act was also a key topic of discussion on The Go-to-Market Podcast with guest Aditya Gautam, who noted that compliance and data complexity are often the deciding factors for building in-house.
Putting the Framework Into Action: Where to Build, Where to Buy, and How to Win
Theory becomes valuable only through application. Coult provides concrete examples of when each approach makes sense. The right choice is use-case dependent: build to protect core intellectual property like brand messaging, but buy to accelerate complex, specialized functions like GTM planning.
When to Build: Owning Your Core Messaging and Intellectual Property
Coult shares a specific example of building an in-house “letterhead” system for generating AI sales copy.
“Instead of having a company try to go build my content, I want to go do that myself because I have all the knowledge of what the company is, what we’re trying to do, and our spiel,” he explains.
The reasoning is clear: messaging, brand voice, and product positioning represent core intellectual property. Building internally provides complete control over AI output, ensuring brand consistency and protecting the unique GTM narrative. Marketing-approved branding can instantly trickle down into every seller communication, creating a unified go-to-market motion.
For teams that need this capability without the development overhead, a unified platform like Fullcast Copy.ai can provide a centralized environment for GTM content creation.
When to Buy: Accelerating Specialized Functions Like GTM Planning
Not everything should be built internally. Complex, standardized functions often benefit from specialized vendors.
“You can build AI agents for capacity planning and territories and things like that,” Coult acknowledges. “You guys are a specialized solution. That’s why we have people. Whereas if you try to go build that yourself, you’re going to get a subpar C product because you’re building it for what you think it is.”
Territory planning, capacity modeling, and compensation design are operationally intensive. A dedicated platform has solved these problems at scale, offering an “A product” out of the box. This is where a dedicated platform for RevOps can reduce planning cycles from months to weeks, as seen with companies like Udemy.
The Leader’s Edge: Why You Must Use AI to Learn About AI
Coult closes with an urgent call to action for personal development.
“If you’re not building skills that are your skills, like you’re going to be behind with your AI,” he states. “There are 1,000 new tools every day. There’s a new company every day. You have to understand where they are.”
He advocates for building personal AI agents to automate research, summarize industry news, and maintain a competitive edge. The leaders who will thrive are not just deploying AI for their teams. They are deeply, personally engaged with the technology themselves.
“If you’re not using AI against itself, meaning like go have AI research it for yourself,” Coult advises, “you’re going to get behind.”
Final Thoughts
The question is no longer if RevOps should adopt AI, but how it should be woven into the operational fabric of the business. The build vs. buy debate is the first, most critical thread in that process. It forces leaders to look inward at their team’s capabilities, their company’s core intellectual property, and their tolerance for risk.
Ultimately, the most successful leaders will be those who treat this not as a one-time technology procurement, but as the beginning of a continuous cycle of innovation. They will not just ask whether to use AI for a task. They will build a disciplined, repeatable system for deciding, testing, and scaling solutions. This operational rigor, combined with a personal commitment to continuous learning, is the real competitive advantage.
Ready to take the next step? It is time to embed AI as the operational backbone of your GTM organization.