--- title: Build vs. Buy for Enterprise AI author: True Horizon AI url: https://truehorizon.ai/news/build-vs-buy-for-enterprise-ai description: "Every company scoping an AI program ends up at the same fork: build or buy. The real questions is - Which parts of this are specific to your business, and which parts are the same for every company?" --- # Build vs. Buy for Enterprise AI > Every company scoping an AI program ends up at the same fork: build or buy. The real questions is - Which parts of this are specific to your business, and which parts are the same for every company? Every $100M+ company scoping an AI program ends up at the same fork: build it ourselves, or buy it. It feels like the responsible question to ask, but it's the wrong one. Companies that treat it as a straight either-or tend to lose money both ways, either spending too much on a build they can't staff, or buying something that leaves them looking exactly like their competitors. The question that actually helps is narrower. Which parts of this are specific to your business, and which parts are the same for every company? ## Building it all yourself is harder than the board thinks On a slide, building in-house looks like control. In practice it's a hiring problem you can't solve and a system you have to keep alive forever. A small team of AI engineers costs well into seven figures a year before they ship anything, and that talent is the scarcest thing in the market right now. The failure numbers are bad. Gartner's 2026 survey of IT leaders found that [only about 28% of AI use cases fully meet their ROI targets](https://www.gartner.com/en/newsroom/press-releases/2026-04-07-gartner-says-artificial-intelligence-projects-in-infrastructure-and-operations-stall-ahead-of-meaningful-roi-returns), with most stalling before they pay off. Forrester expects [three out of four companies that try to build their own AI agents to fail at it](https://www.forrester.com/blogs/predictions-2025-artificial-intelligence/). The model is rarely the reason. Building the thing is the easy part, and operating it, securing it, and keeping it running for years is the part nobody budgets for. Within a couple of years, most in-house teams spend more time keeping old systems alive than building anything new. ## Buying everything off the shelf leaves you generic There's a quieter mistake on the other side. If you buy your whole AI stack from a vendor, you end up with the same capability any competitor can buy, on terms the vendor sets and can change. You've handed over the standard parts, and along with them, the one part that was supposed to make you different. That's fine for the standard stuff, where being the same as everyone else costs you nothing. It's a problem when the workflow you outsourced was your actual advantage, because now the thing that set you apart runs on someone else's schedule, at someone else's price, for as long as they decide to keep offering it. ## What's yours versus what's everyone's Almost every AI program is two kinds of work mixed together. Some of it is the same for every company: the model, the servers, the security and compliance setup. You should buy that, because building it yourself means paying to recreate something a better-funded company already runs. The rest is specific to you: your data, your processes, the way your teams actually work. That's the part worth building, because it's the only part a competitor can't just go buy too. Gartner now describes the choice as build, buy, or blend, and the blend is where most AI programs that work end up. You buy the standard parts and build the part that's yours. ## The talent question usually decides it For most companies, one hard fact settles it: you can't hire the team. In [CIO.com](http://CIO.com)'s 2026 survey of tech leaders, [a lack of in-house AI talent was the number one thing blocking their AI plans](https://www.cio.com/article/4165232/whats-holding-back-enterprise-ai-shortage-of-talent-cios-say.html). If you can't realistically hire and keep a senior AI team for years, then "build it ourselves" isn't a plan, and the realistic path is to buy the standard parts and bring in a partner to build and run the custom part for you. That's the same call you already made with the cloud. You didn't build your own data centers. You rented them from people who run them better at scale, and kept your attention on what made you different. ## How True Horizon does it This is the work we do. We don't hand you a product and make your business fit around it. We start with how your company actually runs, the data you have and the way your teams operate, and we build the AI around that. We connect it to the systems you already use, set up the security and compliance, and then run and maintain it after launch. You get a system built for your business without having to hire and keep an AI team to do it. It's how we built Avalara's AI across eight departments. ## How to decide for your own program Run each piece of the program through three questions. Is this part specific to us, or the same thing everyone else has? Can we staff and maintain it for years, not just launch it? And if we buy it, what does it cost us to switch when the vendor changes the terms? Answer those honestly, piece by piece, and the build, buy, and blend lines sort themselves out.The companies that get this right don't pick a side. They buy the standard parts without overthinking it, and they spend their limited talent only on the parts that were ever going to be theirs. If you're weighing build versus buy and want help drawing that line, [take our AI assessment](http:///assessment) and we'll map which parts of your program are worth owning. Source: https://truehorizon.ai/news/build-vs-buy-for-enterprise-ai