The AI Graveyard Problem
Most enterprise AI investments never reach users. Models built by data science teams sit unused while business users struggle. Here's why—and what to do about it.
A familiar story
The board approved $5 million for AI. The data science team spent 18 months building models. The press release went out. And now?
The models sit in Jupyter notebooks. Maybe a handful of data scientists can access them through command-line interfaces. Business users—the people who were supposed to benefit—have never seen them.
This is the AI Graveyard. Every large organization has one.
The scale of the problem
The math doesn't work. Organizations are spending billions on AI capabilities that almost no one actually uses. The models exist. The value doesn't reach the business.
Why AI projects die
After working with enterprises for 18 years, we've seen the pattern repeat across industries. The failure modes are consistent:
The last-mile problem
Data science teams build models. They don't build user interfaces. The gap between "working model" and "usable product" is enormous—and nobody owns it.
Access fragmentation
Legal has one AI tool. Marketing has another. Engineering uses three. Nobody knows what's available, who can access what, or how to request access to something new.
Governance paralysis
Security wants to control AI usage. Legal has concerns about data handling. Compliance needs audit trails. The result? Everything gets blocked while committees meet.
Vendor lock-in fear
Organizations don't want to bet everything on OpenAI or Anthropic or Google. So they build bespoke infrastructure that takes years—and by the time it's done, the models have changed.
Missing metrics
Who's using AI? For what? How much does it cost? Most organizations can't answer these questions. Without visibility, there's no way to measure ROI or justify continued investment.
It's not a model problem
Here's what executives often miss: the AI models are fine. Claude, GPT-4, Gemini—they're capable enough for most enterprise use cases. The bottleneck isn't intelligence.
Most organizations don't need to build more models. They need to actually deploy the ones they have.
What successful deployment looks like
The organizations that actually get value from AI share common patterns:
One interface, many models
Users don't care which model answers their question. They want a single place to go—web, desktop, or mobile—that connects them to whatever AI capability they need.
Role-based access
Legal sees the legal model. Interns see the approved-for-interns model. IT controls who sees what without blocking everyone while they figure it out.
Complete audit trails
Every query, every response, every model, every user—logged and exportable. Compliance gets what they need. Business gets usage data. Everyone wins.
BYOK flexibility
Bring your own keys. Your API contracts, your cost controls, your data agreements. The deployment layer shouldn't force you to change vendors.
Days, not years
Deployment should take days, not months. If your rollout timeline is measured in quarters, you're building too much custom infrastructure.
Stop building. Start using.
There's a fundamental choice every enterprise faces: spend another two years building AI infrastructure, or deploy what you have in two weeks.
Every month spent building custom deployment infrastructure is a month your organization isn't getting value from AI. Your competitors are moving faster—not because they have better models, but because they're actually using the ones that exist.
The AI Graveyard grows larger every quarter. The question isn't whether you have good models. It's whether anyone can use them.
Deploy AI in days, not years
Legion connects your models to your users—with governance built in.