I have also been experimenting with Copilot Cowork and the idea of treating an AI tool less like autocomplete and more like a collaborator that needs direction.

The difference is subtle but important. A collaborator can help explore options, implement a slice of work, explain trade-offs, and check assumptions. But it also needs context, constraints, review, and correction. Without those, it can be confidently wrong in ways that look productive until someone reads the detail.

The capability curve is moving quickly. Coding assistants are becoming more agentic: planning work, making coordinated changes, running checks, responding to feedback, and taking on larger slices of delivery. That is a different thing from a faster editor.

The most useful pattern so far is to be explicit about the outcome, let the tool handle a bounded piece of work, then review the result as if a junior team member had produced it. That review step is not optional. It is where quality, security, maintainability, and business intent get tested.

For leaders, this is the part worth paying attention to. AI may change how software work is organised, but it does not remove the need for clear ownership. If anything, it makes ownership more important.

As code generation gets cheaper, more ideas will make it to prototype. That is useful, but it also means organisations will need stronger habits around prioritisation, review, assurance, and deciding what not to build.