The “build vs. buy” debate is as old as software itself. At its core, it’s an investment decision: what are you buying, what’s the reward, and what’s the risk?
For decades, the tradeoff has been relatively stable. Building means control, differentiation, and long-term flexibility — but at the cost of upfront investment, ongoing maintenance, and talent risk. Buying means faster time to value and lower operational burden — but at the cost of dependency, customization limits, and ongoing vendor fees.
Same as it ever was. Except now, we’ve introduced AI into the equation — and the math feels… different.
The promise of AI is that you can build more with less. A small team with the right tools can produce what used to take dozens of engineers and millions in investment. That’s real. We’re seeing it everywhere — from startups building products end-to-end on GPT APIs to enterprises spinning up internal copilots in a sprint.
But the economics only look cheaper on the surface. Because what’s really changed isn’t the cost of building, it’s the cost of starting. You can now get a working prototype in days. But what about the next 18 months?
AI reduces the cost of proof-of-concept, but it doesn’t eliminate the cost of ownership. And when ownership isn’t part of your core competency, those costs compound quickly.
Core vs. Context
That’s where the real question lives: Is this thing you’re building core to your business — or just context?
If it’s core, it’s a strategic asset. You invest in it because it differentiates you.
If it’s context, it’s infrastructure. You invest in it only to the degree it supports your mission.
AI blurs that line, because it’s easy to imagine every automation as “strategic.” But just because something uses AI doesn’t make it core.
If you’re a property management company or a real estate investment firm, your core business isn’t machine learning. It’s asset performance, tenant experience, and investor returns. So if your engineering team is spending cycles training models to code invoices, what’s the opportunity cost? What value-generating work isn’t getting done while that side project lives in “R&D limbo”?
We see this all the time at PredictAP.
A prospect says, “We’re building our own AI invoice processor.” And we get it — it’s tempting. They’ve seen GPT-4 classify invoices pretty well. Maybe they’ve even got a working demo.
But then we ask a few questions:
Usually, the answer is some version of: “We haven’t gotten that far yet.”
That’s because the hard part of AI in accounts payable isn’t getting a model to read an invoice — it’s building a reliable system that can read every invoice, every time, no matter how messy the data, the format, or the accounting rules.
And building that system requires deep domain expertise, a steady stream of labeled data, and years of iteration. That’s what we do full time. That’s our core.
So when a company decides to “build,” what they’re really doing is starting a second business — one focused on document understanding, compliance, and model operations. If that’s not your mission, it’s a distraction disguised as innovation.
So maybe the right framing isn’t build vs. buy — it’s invest vs. allocate. Building is an investment: high risk, potentially high reward, but it ties up resources and demands ongoing reinvestment. Buying is allocation: lower risk, faster payoff, but with tradeoffs in control.
The right choice depends on where you want to place your bets.
If the technology is truly core to your strategy, build. If it’s not, buy the best solution on the market — and focus your energy on what is core.
Because in the time of AI, the cost of starting has gone down, but the cost of distraction has gone way up.
AI doesn’t change the build vs. buy question — it just makes the wrong answer easier to start.
The fundamentals remain: