CIOs Scrapping DIY AI for Commercial Solutions

It’s Not You, It’s the AI-Powered POC

Companies once dove headfirst into homegrown AI projects, buoyed by big promises and even bigger hype. But lately, many are slamming the brakes on in-house proofs of concept (POCs) and opting for ready-made AI offerings instead. According to Gartner’s John‑David Lovelock, the share of organizations developing their own AI tools tumbled from roughly 50 percent in late 2023 to about 20 percent by the end of 2024. And IDC research paints an even starker picture, finding that a whopping 88 percent of AI POCs never graduate to widescale deployment. High failure rates. Low returns. Ouch.

Why DIY Often Falls Short

Brain Drain Meets Budget Pain

Developing AI in‑house isn’t just about code and computers. You need brilliant data scientists, seasoned ML engineers and a pile of resources to glue it all together. Scott Wheeler, cloud practice lead at Asperitas Consulting, says many clients—especially in financial services and insurance—have found the “juice isn’t worth the squeeze.” They simply can’t compete for talent (or budget) against AI juggernauts. In my experience, when you’re already stretched thin, a year‑long AI build feels more like a millstone than a springboard.

Expectations vs. Reality

Did you think a quick POC would revolutionize your operations overnight? You’re not alone. Early 2024 saw bosses and board members breathing down CIOs’ necks to conquer “big hairy problems” with generative AI—only to discover the outputs were underwhelming at best. Carmel Wynkoop of Armanino recalls that initial POCs often yielded “quite low” quality results, leaving everyone asking, “Is this thing even useful?”. Sometimes, the simplest use cases—like summarizing reports or automating routine tasks—offer far more bang for the buck than grandiose AI dreams.

The Market Pushes Back

From Buyer to Target

Here’s the twist: today it’s the software vendors doing the chasing. Lovelock notes that virtually every enterprise application now ships with a gen AI toggle—and sales teams are more than happy to flick it on (for an extra fee) without so much as a heads‑up. You might log in one morning to find a new “AI assistant” button and a slightly heftier invoice. Kind of like adding guac to your burrito—useful, perhaps, but not exactly what you had in mind.

The Vanishing Incentive

As off‑the‑shelf AI becomes ubiquitous, the drive to reinvent the wheel dwindles. Why build when you can buy? Armanino’s Wynkoop points out that most targeted functionality—like intelligent data extraction or automated customer insights—is now baked into mainstream platforms. That erases much of the rationale for costly, time‑consuming in‑house builds.

Aiming Smaller—and Smarter

Quick Wins Over Grand Plans

Instead of tackling colossal projects from day one, many organizations are finding success by starting small. Pick a few workflows that can be streamlined quickly—invoice processing, basic help desk support or sales‑order triage—and let the AI prove its worth on a manageable scale. When those tiny victories pile up, you’ll build genuine momentum (and credibility) for bigger bets later on. Win a little. Learn a lot. Repeat.

Custom Models on Proprietary Data

That said, there’s still room for niche in‑house AI work. Companies like Indicium are training commodity models on their own datasets to unlock unique insights—think customer‑behavior patterns or specialized risk assessments—that generic tools can’t match. Daniel Avancini, Indicium’s chief data officer, says focusing on one “really big ROI” project often outperforms scattering resources across dozens of half‑baked POCs. It’s a classic case of depth over breadth.

Balancing Control and Convenience

It’s tempting to fully outsource AI capabilities. Yet, giving up complete control can have downsides—vendor lock‑in, unexpected costs and configuration limits, to name a few. In my experience, hybrid strategies often win: buy core AI services for broad capabilities, then layer on bespoke tweaks via in‑house teams (or trusted partners) where it counts. Keeps you nimble. Keeps you in the driver’s seat.

Looking Ahead

Will DIY AI ever stage a comeback? Possibly—if platforms open up more transparent customization paths and talent becomes more accessible. But for now, the lesson is clear: when POCs cost more than they’re worth, sometimes the best choice is to click “Buy” instead of “Build.” And hey, that’s okay. After all, AI is meant to spark innovation, not induce headaches.

Unexpected takeaway: Sometimes the most radical innovation lies not in inventing new algorithms, but in mastering how you integrate and scale what already exists. In 2025, the smartest CIOs know that pragmatism trumps prestige every time.