Enterprise AI is evolving rapidly. Models are becoming more capable. Interfaces are improving. But for legal teams, the core problem hasn't changed: finding the right information, in the right place, at the right time.

Over the last year, we watched a pattern repeat itself across large enterprises: AI initiatives that looked extraordinary in controlled demos struggled once they met the realities of production environments.

In Mata v. Avianca, lawyers submitted a brief filled with very convincing judicial opinions — which had the unfortunate drawback of being completely imaginary. ChatGPT had helpfully hallucinated answers to their questions.

The problem is no longer lack of data. It's no longer an inability to retrieve data. Today's problem is knowing which data to trust.

Every day, your people lose the equivalent of a full feature movie just searching for information. According to a Forrester report, workers lose about 2.4 hours a day simply trying to find the right document or answer buried in an email chain.

Chatbots and copilots have captured the imagination of business leaders and employees alike. But when you ask them a complex, business-critical question, they fail — because they're only as good as the data they see.

You don't have a 'data problem.' You have hundreds of them — one for every app, system, and silo your teams use every day. And each one is a small tax on productivity, decisions, and momentum.

CTOs already see what's coming: agentic AI will be embedded in core business processes. But the leap from generative novelty to autonomous utility exposes a hard truth — our data infrastructure isn't ready.

AI deployment doesn't require centralization. It requires access. The common guidance is to clean up your data stack before deploying AI — but that advice gets the order exactly backwards.