Build vs. buy: What revenue teams get wrong in AI decisions

“Why are we paying for that when we could just build it ourselves?"

It's a fair question most revenue leaders have right now. AI has made building look cheaper and faster than ever. Your engineers are eager, a rep can ship something functional over the weekend, and the board wants to know why the software bill keeps climbing.

But getting this call wrong is expensive. Build the wrong thing, and now you own unpredictable maintenance while your team works around a tool that’s never quite finished. Buy the wrong thing, and you're paying for something nobody adopted.

So how do you actually decide whether to build or buy?

Join three leaders who make this call inside their own organizations for an honest conversation about when to build, when to buy, and how to tell the difference before you commit. You'll leave with a practical framework for developing your AI roadmap.

We’ll cover:

• The questions to ask before you decide: Insights into evaluating any AI capability, the kinds of considerations that separate a good build decision from a bad one, and what questions most teams never ask.
• The costs nobody forecasts: The hidden weight of a build decision and the equally real risks of buying, so you can weigh both honestly before you commit.
• How the best teams avoid the false binary: Why the smartest organizations rarely choose to go all-build or all-buy and how they decide which layers to own versus outsource.

Featured speakers:

• Shefali Raghavan, Senior Vice President, GTM Strategy & Operations, G2
• Kelly Sarabyn, Director, Technology Partnerships, Hubspot
• Graham Miller, GTM AI Strategy & Enablement, Gong