Many companies moved to the cloud expecting a cleaner architecture. What they got, in most cases, was the same architecture on someone else’s servers. The monolith became a set of virtual machines; the nightly batch job became a scheduled function. The seams stayed where they were.
For years that was fine. Then AI arrived, and AI is unforgiving about seams.
Why AI finds the cracks
An AI assistant that answers a customer’s question needs the order history, the policy document and the ticket thread, at the same moment, in a form it can read. If those live in three systems that only sync overnight, the answer is wrong by design. The model is not the problem. The architecture is.
- Data in silos. Each team’s system holds its own version of the customer.
- Batch thinking. Data that arrives tomorrow is useless to an agent that acts now.
- No clear APIs. Agents need stable, permissioned ways to read and write, not screen scraping.
What to fix first
Start with the one workflow you want AI to improve and trace every piece of data it needs. Where does it live, how fresh is it, and who is allowed to see it? That map tells you which integrations to build first, and it is usually far smaller than a full re-platforming.
The fastest AI projects we see are the ones that fix one data path properly, then reuse it.
Cloud gave you elastic infrastructure. Making it AI-ready means connecting the data, exposing it safely and treating integration as product work, not plumbing. Do that for one process, measure the result, and the next one gets cheaper.