The AI Build vs. Buy Decision: A RevOps Leader's Practical Framework

Revenue Operations leaders are under immense pressure to deploy AI solutions that drive growth. But one critical question stands between strategy and execution: should you build AI capabilities in-house or buy them from a vendor?

This is not just a budget conversation. It is a strategic choice that will shape your team's resources, your maintenance burden, and your go-to-market agility for years to come.

In this episode of The Go-to-Market Podcast, host Amy Osmond Cook, Co-Founder and CMO at Fullcast, sits down with Cory Coult, Senior Director of Revenue Operations at Avalara, to break down a pragmatic framework for making this decision with confidence.

Treat Your Revenue Engine Like a Product You Own

The role of RevOps has fundamentally changed. What was once a support function focused on reporting and system administration has evolved into something far more strategic.

"I feel like the best operators are the ones that are weaving them together in a single, unified view for the whole business. And that's extremely challenging."

Coult describes experiencing three distinct eras in revenue operations. The first focused on basic administration. The second centered on productivity and scaling. Now, we have entered the era of go-to-market engineering, where RevOps professionals must think like product managers rather than system administrators.

This shift is critical because every AI initiative you launch becomes a product you must maintain indefinitely.

"You're building products inside of your business that you don't just build, ship, and leave alone. You have to maintain it and manage it all the time."

Before you decide to build or buy, ask yourself: does your team have the capacity to own this product for the long term?

Set Expectations With the 'C Product' Approach

One of the biggest reasons AI projects fail is the mismatch between leadership expectations and operational reality. Coult offers a powerful framework for managing this gap.

"I'm building a C product. Expect a C product. It takes a lot to go get it to a B and an A. But if you want a quick C product, I can go deliver that."

The takeaway is simple. Do not promise transformative results from day one. Launch with a functional first draft, prove value, and then invest in iteration. The path from C to B to A depends on adoption and demonstrated ROI.

"A lot of people start a project assuming A, and you get a C, and then that's where the trouble starts."

Action Step: Before your next AI initiative, align with leadership on what a "C product" looks like. Define the criteria for investing further in a B or A version.

Use This Three-Step Framework to Decide

Coult outlines a practical, repeatable process for evaluating AI solutions.

Step 1: Build Internally to Validate the Business Case

Before evaluating external vendors, attempt to build a minimum viable version with your existing tools and team.

"If I can do it myself with my team, I'll try that to do it myself with the tools we have. Now, if we get a D product, I might say, hey, we might want to go buy enterprise class products."

This internal trial serves as a proof-of-concept. It de-risks larger investments and reveals technical complexities you may not have anticipated.

Step 2: Weigh the Trade-Offs Between Enterprise and Startup Vendors

When your internal build proves value but lacks scalability, the focus shifts to buying. But not all vendors are created equal.

"You go with the innovative ankle biters that are new, or you go with the behemoths that are established but behind in skills."

Enterprise solutions offer stability but often lag in AI innovation. Startups deliver cutting-edge capabilities but carry acquisition or failure risks. The right choice depends on your organization's risk tolerance.

Step 3: Balance Speed, Cost, and Quality With the Right Talent

When asked how to achieve all three simultaneously, Coult identifies two critical factors.

"I think it comes down to the talent. The person in the chair really dictates a lot of those three. The second is great communication and the agreement of what this is and what to expect."

Action Step: Before launching any AI initiative, document the expected speed, cost, and quality trade-offs. Assign clear ownership to a team member who can champion the project through iteration.

Know When to Build and When to Buy

Coult provides concrete guidance on where each approach makes sense.

Build when protecting core intellectual property. Messaging, brand voice, and product positioning are unique to your organization. Building internally gives you complete control over AI output.

"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."

Buy when accelerating specialized functions. Complex, standardized capabilities like territory planning and capacity modeling benefit from vendors who have solved these problems at scale.

"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."

The Bottom Line

The leaders who will thrive are not just deploying AI for their teams. They are personally engaged with the technology and building disciplined systems for deciding, testing, and scaling solutions.

"If you're not using AI against itself, meaning like go have AI research it for yourself, you're going to get behind."

The build vs. buy decision is not a one-time procurement event. It is the beginning of a continuous cycle of innovation that will define your operational future.