Over the past year, we’ve seen unprecedented investment in AI. Larger models. Bigger data centers. More impressive demos. And yet, inside most real enterprises, AI is still struggling to deliver consistent, operational value.
This isn’t because the models aren’t smart enough. It’s because something more fundamental is missing.
The AI Adoption Gap Nobody Talks About
Recent studies suggest that the majority of companies experimenting with generative AI have yet to see measurable returns. That’s not for lack of ambition or funding. It’s because AI systems are being dropped into environments they don’t understand.
Enterprise work is:
- Contextual
- Interdependent
- Constantly changing
- Governed by constraints, approvals, and real consequences
Most AI systems, however, are trained on static data and evaluated in controlled settings. They’re very good at sounding right. They’re much worse at operating correctly inside live workflows.
Why “More Training Data” Isn’t the Answer
In response, AI labs are spending billions on increasingly specialized training data: expert annotations, detailed rubrics, and simulated environments where models can “practice” tasks. This has created an entire new industry supplying human-generated training data. It’s booming.
But there’s a catch.
These environments are expensive to build, difficult to maintain, and quickly drift from reality. As soon as processes change, tools evolve, or priorities shift, the training signal becomes outdated.
The problem isn’t a lack of intelligence. It’s a lack of grounding.
The Missing Layer: A System of Information
Most enterprises already run on systems of record — ERP, scheduling tools, document repositories, ticketing systems. These systems are excellent at storing data, but they do a poor job of explaining how work actually flows across teams and time.
What’s missing is a System of Information.
A System of Information sits above systems of record and focuses on:
- How work is sequenced and connected
- What dependencies and constraints exist
- When work is truly ready to proceed
- How changes propagate downstream
- Why decisions were made in context
Without this layer, AI systems have no reliable way to understand the state of work or the consequences of action. They operate in fragments, not systems.
Making Work Legible to Machines (and Humans)
For AI to scale into real enterprises, human work needs to be:
- Legible — explicit rather than implicit
- Structured — connected rather than siloed
- Continuously updated — reflective of reality, not plans
This isn’t just an AI problem. Humans struggle in the same environments for the same reasons. The difference is that people can compensate with experience and intuition. AI cannot.
A System of Information provides the shared context both humans and machines need to reason effectively.