There’s an age-old debate in technology that’s been around longer than Clippy himself: should you build, or should you buy?
Historically, it’s meant choosing between custom code and vendor software. But in the age of AI, that choice suddenly carries much higher stakes and far fewer easy answers. Build in-house, and you risk blowing your budget on talent, data pipelines, and pilots that never quite make it to production. Buy off the shelf, and you might find yourself with a tool that doesn’t really fit your business without heavy customization.
This week on AI Knowhow, host Courtney Baker sits down with David DeWolf and Mohan Rao to unpack the “build vs. buy” dilemma in the AI era and to explore what the question really reveals about leadership, learning, and strategy.
New MIT research suggests that two-thirds of vendor-led AI projects succeed, compared to only about one-third of in-house efforts. But as both David and Mohan point out, that statistic only matters if you’re starting with the right question. Instead of asking “Should we build or buy?” leaders should be asking “What problem are we trying to solve?”
AI success, they say, starts with business outcomes, not architecture diagrams. For some companies, that may mean partnering with a vendor to gain speed and scale. For others, it may mean building bespoke systems to solve unique challenges. The best choice depends less on control and more on clarity.
Compounding Intelligence
Mohan reframes the debate in a way that’s particularly relevant for executives: the real goal isn’t ownership. It’s compounding intelligence.
“The question,” he says, “is how do you create a learning organization where humans and machines work together to continuously improve?” Building can offer control, but it also brings hidden costs in data infrastructure, governance, and maintenance. Buying gets you value faster, assuming you choose the right vendor, but it introduces dependencies that must be carefully managed.
Ultimately, leaders should choose whichever path compounds their organization’s intelligence fastest.
The Takeaway
AI decisions today will define competitiveness tomorrow. But as David and Mohan emphasize, the best strategies start with business problems, not technology decisions. Whether you’re buying a tool or building your own, the goal is the same: compound your intelligence faster than your competitors — without losing sight of the human ingenuity that drives your business forward.
In the News: The Trillion-Dollar Signal
Citigroup recently projected that big tech will spend $2.8 trillion on AI infrastructure by 2029, a staggering figure that signals AI has officially moved past the point of experimentation. As David explains in our News segment, that level of investment rivals some of the biggest infrastructure shifts in modern history, from broadband to cloud computing.
But the lesson isn’t about the size of the spend. It’s about what comes next. Massive investment alone won’t deliver results. The true differentiator, David suggests, will be how effectively companies integrate AI into their operations and align it with human capital. In other words, the winners won’t just have the most powerful tools. They’ll be the ones who use them to make human work more creative, more dignifying, and more fulfilling.
A Prediction Fulfilled
The episode closes with a fun full-circle moment. David revisits a prediction he made nearly two years ago: that AI would soon follow its own version of Moore’s Law, doubling in capability every few months.
New research confirms it: AI performance is now doubling roughly every four to seven months, according to a nugget in this NYT article. That exponential pace means professional services leaders must rethink how they adapt, learn, and lead in a world where the technology itself is evolving faster than most organizations can plan.
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Show Notes
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- Connect with David DeWolf on LinkedIn
- Connect with Mohan Rao on LinkedIn
- Connect with Courtney Baker on LinkedIn
- Get a guided Knownwell demo
- Follow Knownwell on LinkedIn
