What Most Industries Are Still Missing About Generative AI

Industry giants—Google, Amazon, Apple, Microsoft, Netflix, and Tesla—aren’t just deploying generative AI. They’re profiting from it.

These companies are operating with a different kind of infrastructure. Behind the scenes, their AI strategies are deeply embedded into how they operate, generate revenue, and engage with customers. They’re not just running large language models—they’ve built ontologies, knowledge graphs, context engines, and strong data governance frameworks. It’s not accidental that they move faster or make smarter decisions at scale. It’s structural.

Meanwhile, in sectors like government, healthcare, hospitality, and retail banking, we’re not seeing the same story. The use of generative AI remains isolated—pilots here, experiments there—but it hasn’t reached operational scale. And that’s where the opportunity lies.

So, what’s missing?

The flashiest part of AI is the output—but the heavy lifting happens before that. When I meet with teams trying to scale AI, the same core gaps come up over and over again.

These are the systems Big Tech has quietly invested in for years:

• Ontologies – to create shared language and meaning across data systems
• Knowledge graphs – to unify information from different sources and surface relationships
• Context engines – to keep outputs relevant, timely, and business-aware
• Data governance – to ensure trust, consistency, and compliance
• Connectors – to actually make this work across existing systems and workflows

These are foundational. But in a lot of sectors, they’re still being treated like optional add-ons—or worse, afterthoughts.

A simple example with billion-dollar implications

A bank’s AI model spots a $556/month auto loan. It recommends selling the car via Bluebook, netting the customer $2,500, and rolling them into a new, better loan—right through that same bank. Multiply that by a million customers.

That’s not just insight. That’s billions in potential loan originations.

This is the kind of downstream value generative AI can unlock—if the right systems are in place.

The opportunity ahead

Most sectors—public and private—are still figuring this out. The technology’s available. The strategies are known. What’s missing is the structure to support it.

And that’s where we focus at Cyberhill—helping organizations build the architecture for AI that doesn’t just run, but returns value.

Generative AI isn’t the goal—it’s a tool. The real work is what we do with it.