Conference season just wrapped, and I spent most of it on the road — and in the room for more keynotes than I can count. One word ran through nearly all of them. Vendors could not stop talking about “context.”
Here’s why: An AI model will hand you a fluent, confident answer without knowing enough about your business to get that answer right, and context is what closes that gap. It’s the information a system pulls together at the moment it has to act. For instance, a support agent about to approve a refund needs the return policy, the customer’s history, information about the product in question and the rule that allows the exception. Give it just one wrong piece and the answer can come back confident and wrong. This is a nuisance for many people who are using generative AI purely to draft text. But with agents empowered to issue a refund or update a sales forecast, that mistake can cost a business a lot of money.
So just about every vendor in data, analytics and AI now promises to deliver trusted context, and each one says that its product or platform should be the place where that context lives. The money these vendors are investing backs up their talk. IBM paid about $11 billion for Confluent to put real-time data under its agents, and Salesforce spent roughly $8 billion on Informatica for a governed data foundation beneath its own. And those are just two big examples of the several acquisitions that have occurred lately.
The trouble is that context is a loose word, loose enough that companies that operate in storage, databases, business intelligence, observability, data protection, governance, security and business applications can all claim to sell it. The tricky part is, they’re all telling the truth. But if you ask how they actually produce context, they part ways fast, because they are solving different problems.
Rival Camps Are Selling The Same Word
One vendor camp says you earn context by defining it before the AI ever runs. You decide what your terms mean and write them down in a central location so every tool draws on the same definitions. The most established version is the semantic layer, where a term like “active customer” or “net revenue” gets one agreed definition instead of the five conflicting ones scattered across dashboards. Analytics leaders like Qlik, Strategy, ThoughtSpot and Domo, semantic specialists like AtScale, and the big cloud providers have all sold some form of this for years, and now they are messaging it as context for AI.
Push past the semantic layer and you reach the ontology, a map of how the whole business connects, so the software can follow the chain from customer to order to product to contract without guessing. That part is newer, and for many of these vendors it still sits on the roadmap rather than in the product. Palantir is the exception that built its business on ontology, modeling a company’s entire operation before turning AI loose on it. The payoff is trust, since a system looking up a defined meaning is not guessing. The drawback is that modeling a business by hand has always been slow and expensive, and it is sure to be one of the first line items cut when budgets tighten.
Then there is the retrieval camp, which finds up-front modeling too slow and bets on the model instead. You point the AI at your documents, tickets and logs and let it pull in whatever looks relevant. This is the basic approach of the now well-known retrieval-augmented generation. It’s why a new role — the context engineer — showed up this year to design what an agent sees and when. The appeal is speed, because there’s no year-long modeling project. You aim it at your data and go.
