Most companies want to add AI before they connect the systems it depends on. That order rarely works because AI can only use the data it can reach. For most firms, that data sits across dozens of separate tools. Companies ran an average of 106 SaaS applications in 2024, according to BetterCloud. Each tool holds part of the data, and a few of them connect to the others. 

An AI model trained in a demo runs on clean and well-prepared data. A live system instead pulls from many sources that conflict, duplicate, and delay. The model then produces weak results and the project is blamed on the AI. The real cause is usually the disconnected data underneath rather than the model itself. A model is only as good as the data that reaches it in production. 

Connection means joining those systems so data moves between them in a usable form. It also means resolving one customer across the records that each system stores separately (identity resolution). The work is unglamorous, and it is the work that decides whether AI succeeds later. A connected base lets an AI project start from data it can actually trust. This is why the connected enterprise comes before the intelligent one in practice. 

We connect the systems first and add the intelligence once the data is ready. L1nks is our no-code platform for joining systems without a large engineering effort. The platform deploys on the infrastructure a business already owns and avoids a migration. The order matters because intelligence added to disconnected data produces unreliable results. 

Next month we look at AI agents and the work they can take on.