The AI Stack
Enterprise adoption is already happening. The question is whether it will become a coherent system.
Artificial intelligence rarely enters a company through a single decision. It arrives through subscriptions, experiments, local applications, embedded software features and individual initiative. By the time an organization begins discussing an AI strategy, teams may already be using several models across different tasks and under very different conditions.
That early experimentation is useful, but it also creates the first layer of technical and organizational debt. Tools are selected without common criteria, data moves through systems that were not assessed together, and local solutions begin to influence business processes before the company has defined how they should be governed.
Issue 12 examines the architecture that must now emerge around this activity.
The central question is not whether enterprises should choose commercial platforms or open-weight models, cloud infrastructure or local servers. Most will eventually use a combination of them. The more important decision is how these components will be connected, supervised and assigned to the right kinds of work.
This requires more than an approved-tools list. It requires a control layer capable of managing access, data, model selection, traceability and the actions performed by increasingly autonomous systems. It also requires professional oversight supported by automated supervision, particularly when AI begins to operate inside consequential processes.
Companies still have an opportunity to design this environment before scattered use becomes fixed infrastructure. The decisions made now will determine how easily they can expand AI without losing control of their data, systems and responsibilities.
—The Editor