The expectation is no longer should we use AI, it’s why haven’t you already? That kind of pressure leads to rushed deployments, overworked teams and misaligned expectations.

Because LLMs don’t play by the same rules as traditional software.They’re unpredictable. Messy. Sometimes magical.

That difference is easy to underestimate, and that’s where things start to break.

Here are four reasons why enterprise AI projects fail (and how to avoid the same traps).

Making your engineers do everything

AI engineering isn’t just a task for engineers.

It takes input from people who understand the context, goals, and requirements. This input is usually what will make or break your application.

Because, engineers don’t want to test prompts, and they shoudn’t be bothered for every time you want to make a tweak to a prompt. That's a poor use of their time.

One of our customers RelyHealth deploys healthcare solutions 100x faster, because they don’t overburden their engineers:

"Before Vellum, we had to manually build every workflow from scratch. Now, a single content engineer can do what used to take a dozen engineers months. We use the Workflow Builder to test what’s possible and scale once we prove it works." - Prithvi Narasimhan, CTO & Co-Founder at Rely Health told us.

Product managers, legal, ops are some of the subject matter experts that play a big role in making sure what you’re building is actually usable, safe, and aligns with real business needs.

This is extremely important, because so much of the AI development process is about getting alignment—on behavior, tone, safety, and outcomes. Your company should adopt tooling that let non-technical folks test prompts, prototype logic, and validate early assumptions.

Engineers should focus on executing that alignment: implementing the logic, adding guardrails, and making sure they integrate the right CI/CD systems to monitor for production reliably.

It’s faster, and everyone’s already aligned on what “good” looks like.

Thinking AI engineering is just software engineering

It’s easy to assume that building AI features should be just like building traditional software: define the specs, write the code, run some tests, and ship. But that model falls apart fast when you're working with LLMs.

In software, you control for deterministic outcomes. Give it input A, expect output B. Testing is straightforward. But LLMs are generative, they can give different answers to the same input.

You’re not just testing logic, you’re judging behavior: is the answer relevant, useful, and correct? Does it sound natural? Does it stay within bounds?

On top of that, what’s “correct” is often subjective. Two outputs might both be acceptable, or neither might really work. And when things go wrong, it’s rarely obvious why. Tracing the issue could mean digging through prompts, outputs, tool usage, or even an agent’s internal reasoning steps, across hundreds of runs.

The main takeaway here is that AI development is less about writing perfect code and more about tuning systems to behave in a certain way. That takes experimentation, iteration, and a lot more time.

So if you’re leading a team, don’t expect the same timelines as traditional feature work. Make space for prototyping, prompt testing, and evaluation before anything hits production.

Using premature technology too soon

Everyone wants an AI agent that just does everything for them. This need is well justified, because we all desire more efficiency, and automation.

But, we end up conflating novelty with maturity. Just because something is possible and exciting doesn’t mean it’s production-ready.

Take MCP, for example. It’s a protocol that lets agents interact with tools through a single, structured interface. It’s a smart idea and opens up all kinds of new possibilities.

But as with any new tech, the hype is ahead of the maturity.

Reciting some arguments from the community: the MCP protocol doesn’t work well with serverless setups, adds complexity to systems already using REST APIs, and introduces more instability into an already fragile AI stack. This only makes things more scary in production.

So while all of this seems exciting (and we’re definitely experimenting with it!), make sure you align your team to build with the technology that works and can be easily manageable in production.

Launching without an observability plan

There’s this idea that you can launch quickly and fix things as they come up. But AI systems don’t work like that. They behave differently than traditional software. They change with data, they react in unexpected ways, and they’re sensitive to all kinds of edge cases.

If you don’t have a plan to observe, debug, and respond to real-world production requests, you’re going to be in serious problem when something breaks. And it will break. That’s not a sign of failure; that’s just the nature of AI in the real world.

You need visibility.

You need a system that tracks what’s happening, catches when the model drifts or starts returning bad outputs, and helps your team debug quickly. You also need clear playbooks for what to do when things go sideways.

So, treat observability like a launch blocker, not a nice-to-have. It’s the difference between feeling in control vs. reactive chaos once your users are in the loop.

Vellum Makes Enterprise AI Easier

Enterprise AI projects often stall because teams underestimate the unique challenges of working with LLMs.

Traditional software expectations like predictable timelines, well-defined testing paths, and mature tooling don’t directly map to AI.

Successful teams acknowledge this gap and adjust their approach: distributing workload across roles, prioritizing careful evaluation over quick launches, avoiding premature technologies, and making observability essential from day one.

Vellum helps teams avoid these common pitfalls by enabling early collaboration, rapid prototyping and validation of AI ideas, and built-in observability — all from one place.

With Vellum, your team can build AI systems confidently, moving beyond hype and delivering real, sustainable value. Book a call with one of our experts here!