AI Makes The Build-Versus-Buy Decision Higher Stakes

Dan Faulkner is the CEO of SmartBear, helping teams build, test, and ship quality software.

​As AI makes software cheaper to build, companies are reassessing whether to build their own software tools or buy them from others.​

McKinsey’s latest data shows 32% of organizations decided against off-the-shelf software and built their own using AI. This dynamic sent SaaS stocks plummeting earlier this year, but many have regained some ground (registration required) as reality sets in.

However, it can be technically hard, time-consuming and even more expensive long term to build software than to buy it from a specialized vendor. MIT Research last year found that “external partnerships with learning-capable, customized tools” reached deployment 67% of the time, compared to 33% for internally built tools.​

I recently came face to face with the buy-versus-build decision myself. We needed a data-labeling tool, and a team set out to build it internally. As soon as I found out, I killed the project. It didn’t make business sense to move the intellectual horsepower of our R&D off what brings our customers value, just to potentially save on tech spend.​

Pressure To Go With AI​

What worries me about the uptick in build decisions is that many leaders are still blindly trusting AI. My company, SmartBear, recently surveyed 1,436 technology professionals whose organizations use AI in development. The research found that 73% of tech leaders say they have a lot or complete confidence that AI-generated code behaves as intended, compared with 52% of practitioners. ​

It’s this overconfidence in AI that can lead companies to build when they should buy.​

The best leaders, however, will weigh all aspects of the build-versus-buy decision, including potential impact on application integrity, meaning whether software works as it is supposed to, and risk to mission-critical applications.​