As important as this sounds it is more important.
Companies differ with each other because of internal processes, priorities, decision making, and resource allocation. We can think of a company itself as a unique AI model trained on X years of training data.
What is the difference between WalMart and Target? They hire from the same pool of people. They source goods from the same suppliers. Their prices can only differ by so much. The companies choose where to open stores, how big to make a store, what goods to assort, what promotions and sales to do, etc.
Those all sound like AI decisions. In fact they seem pretty easy to prompt and model.
But the answers if you're either of those companies or another competitor depend only on company data and history. The public models are trained on a myriad of sources but they aren't models for any one company. A company using a model "as is" to make an important decision is almost like asking your competitors to make the decision for you.
This is effectively the #1 problem for AI agents in the enterprise.
As we go from agentic coding (where a large amount of context is in the code base, and users are technical enough to get the rest to the agent easily) to a world of knowledge work agents, the context problem becomes much more acute.
We see this every day with customers at Box. For existing digital knowledge, it’s often fragmented across legacy systems or environments that don’t play nice with agents, and have access controls that don’t map to the real work that needs to be done, which become a huge hurdle for getting agents the context they need. This has to all get moved to modern, secure cloud environments.
But also, companies often haven’t captured and digitized some of the critical context that agents need to work with. Decisions, processes, and workflows often live in people’s heads and tribal knowledge that need to get turned into unstructured data for agents.
This is actually one of the biggest points of leverage for applied AI companies, because they can work to specialize in getting agents exactly the information and domain expertise they need. But it’s also one of the reasons why FDEs and new system integrator plays will also work so well right now.
The companies that figure this out will be able to get the most out of AI going forward.