Enterprise AI: Build or Buy?
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Let’s cut to the chase. Most AI projects fail because they take forever and cost way more than expected. You know the story. Your team gets excited about AI. Someone calls a vendor. You hear promises of "quick deployment" and "rapid time to value." Six months later, you're still in implementation inferno, your budget has doubled, and your team is burned out.
It doesn't have to be this way.
There's a better approach called managed AI delivery, and it fundamentally changes how AI gets built, deployed, and actually used in your business. It’s not just faster, though it is way faster. But it’s a smarter approach, with less risk, and with pricing that actually makes sense.
Which is why we want to spend some time talking about what managed AI delivery really is, why it works, and how it might be exactly what your organization needs.
The problem with how most vendors talk about "managed" services is they use the word to mean almost anything. One company says "managed" means they'll help with your implementation. Another says it means they'll host your solution. A third says it means unlimited support. Nobody knows what they're actually getting.
Managed AI delivery is more specific. It means a vendor takes responsibility for getting you from "here's what we need" to "here's your AI solution actually working in production" without you building a whole new engineering team or turning your entire business upside down. The vendor doesn't just hand you software and disappear. They're accountable for outcomes.
The tangible difference is that managed delivery means the vendor has real skin in the game. If it doesn't work, they don't get paid. If it takes longer than promised, that's their problem to solve. This fundamental difference changes how vendors approach your project. They're not incentivized to sell you something and move on. They're incentivized to deliver results, because that's how they make money.
What does this actually look like in practice? Your vendor sits down with your team and figures out which AI use cases will actually move the needle for your business. They're not trying to sell you the most impressive technology, rather they're identifying the stuff that makes money or saves time.
Instead of building from scratch, managed delivery uses pre-built components that snap together into your specific solution. Think of it like AI building blocks. Your vendor has done this pattern hundreds of times. They know what works and what doesn't. It's radically different from the "we'll sell you our software and hope for the best" model that dominates enterprise tech.
If we’re being honest, the traditional AI implementation timeline is brutal. A typical custom AI project involves months of planning, development, testing, integration, and deployment. You're looking at six to nine months minimum, and that's if everything goes smoothly. Which it rarely does.
Managed AI delivery flips this on its head, and there's a few reason why:
Here's where managed delivery gets really different. The pricing actually makes sense. Traditional software pricing is backward. You pay upfront, whether it works or not. You pay per user, even if only half your users actually use it. And if the implementation goes sideways? Too bad, you're still writing checks.
With outcome-based pricing, you don't pay until the solution delivers what it promised. If your AI is supposed to reduce manual work by 80 hours per week but only saves 40 hours, you pay for 40 hours of value, not 80.
This seems radical until you realize what's actually happening:
This sounds almost too good to be true, and you might be wondering how vendors can afford to do this. The answer is simple, they've solved the hard parts. They've done this enough times that they know they can deliver. They're confident enough to tie their compensation to results, because they're not worried they won't hit them.
If you decide to build AI in-house, you should consider all the associated upfront costs. You'll need to hire two to three specialized AI engineers for six to nine months. Then add infrastructure, including cloud resources, tools, and platforms. Don’t forget you've got integration work, getting your AI to actually talk to your existing systems.
But here's the hidden cost nobody talks about: opportunity cost. Your team is dealing with this instead of focusing on business priorities that actually generate revenue. Then after launch, you need permanent AI staff to maintain what you've built, because custom code breaks.
And here's the real kicker. Custom development usually takes longer than promised, costs more than budgeted, and still needs significant fixes after launch. You'll probably spend even more than these estimates.
Compare that to managed delivery. Your implementation costs run either a flat fee or outcome-based pricing depending on the vendor. Infrastructure is typically included in what the vendor provides. Integration is handled by the vendor. Launch is handled by the vendor. Post-launch support and maintenance? The vendor maintains it.
When you compare the two, the math isn't even close. And that's assuming your DIY solution is actually working well, which it often isn't. If you need to change it or expand it? You hire the same expensive team again and start the cycle over.
If you're tired of custom development timelines, blown budgets, and vendor solutions that don't quite fit your business, managed AI delivery might be exactly what you need.
The first step is simple. Talk to us about your specific situation. We'll show you what's possible, how fast you could move, and what managed delivery would actually cost for your use case.
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