By Ross Williams

I remember watching one of the early GPT-3 demos a few years ago – fluent language, confident tone, and seemingly instant expertise. It felt like magic. So, naturally, I tried it on a task I thought it might actually be able to help with: writing a product description for a new analytics dashboard. It generated a perfectly polished paragraph, filled with buzzwords and jargon … and completely misunderstood what the product did.

It wasn’t that the model was broken. It just didn’t know anything real about the business, the audience, or the data powering the dashboard. It had the form of intelligence, but none of the substance.

That moment stuck with me—and it’s a pattern I still see today, even with the most advanced models. The challenge isn’t fluency. It’s context.

The AI Boom is on Multiple Fronts, and Yet …

According to the 2025 BOND AI Trends report, the boom is happening on two fronts. First, usage and capital investment are growing at unprecedented rates. The top six U.S. tech firms are now investing over $200 billion in CapEx—much of it for AI infrastructure. Second, AI performance is compounding faster than any prior technology wave, with training compute growing 360% annually and model performance surpassing human benchmarks on standardized tests like MMLU.

Meanwhile, consumer adoption of ChatGPT has outpaced every internet-era app we’ve ever seen. It took the internet 23 years to reach 90% of its users outside North America. It took ChatGPT just three.

And yet … most enterprise marketing teams still can’t get a clean answer to a basic analytics query without an engineer in the loop.

Why? Because context hasn’t caught up. The models are ready, but the infrastructure isn’t.

Context: The Missing Link in Enterprise AI

There’s been real progress in how we get models to work with enterprise data. Retrieval-Augmented Generation (RAG) has become the go-to approach for injecting relevant information into LLMs—essentially allowing models to look things up, rather than relying solely on what they were trained on. It’s a powerful advancement, if not a complete solution. RAG gets us part of the way to context-aware AI, but it still relies quite a bit on how well the underlying data is prepared and managed.

Most implementations still require you to move or reformat your data—copying it into vector stores, stripping out structure, or sacrificing governance controls just to make it “usable.” And even then, you’re often left managing token bloat, hallucinations, and stale context.

What’s missing isn’t just retrieval—it’s real-time, policy-aware, business-specific context that connects models to the freshest, most relevant slice of data without compromising performance or compliance.

That’s the gap. And it’s where a new kind of infrastructure needs to emerge—one that treats context not as a workaround, but as a core design principle.

Real-Time Context, Not Real-Time Everything

We often hear that real-time is the gold standard. But in practice, what teams really need is real-time context – fast, filtered access to what matters now. Not every row of telemetry. Not every customer record ever created. Just the relevant slice, delivered at the right moment, in a format AI can understand.

That’s the frontier where I see the most innovation happening – not in the model arms race, but in prompt optimization, token governance, and metadata-based query orchestration.

The good news? We don’t have to rebuild the stack to get there. We just have to make it smarter.

Enterprise AI doesn’t fail because of bad models. It fails because we treat data like an asset to store instead of a resource to activate.

The next wave of value creation won’t come from pushing more data into more dashboards. It’ll come from systems that make your data understandable to AI, useful to humans, and aligned to your business in real time.

AI mastered language years ago. Now it needs to master your business.

That’s the frontier that matters.