Your Next Job Title Is Orchestrator
A company with a website receives customer feedback every day, and in the usual process that feedback takes a long and well-worn path: support tags it, a manager summarizes it, product discusses it, someone opens a Jira issue, engineering schedules it, a developer changes the code, QA and security review it, and release engineering ships it. For a genuinely simple request, that can take weeks. Sometimes it takes months.
We have accepted this arrangement because it is built around a chain of meetings, and by the time the ticket has passed through all those teams, thirty or forty people may have touched it. What is surprising is how little discussion or debate the process often produces. The meetings perform a ritual: pass the ticket along, reduce a problem that has already faded from the customer’s memory to a code change, and move it toward release.
The usual promise of generative AI is that it will make this same process faster, and most organizations believe exactly that. Give the developer Cursor or Codex, let a model summarize the support queue and draft the tests and write the release notes, and keep every meeting and handoff exactly where it is while reducing the typing in between. That is assistance. It does not really transform how work is done. And the promise of generative AI is much larger than just assisting people in how they currently do their jobs. What has arrived is something that can help us automate, orchestrate, and create systems that can develop, operate, and improve — where improving means agents proposing changes to code, prompts, skills, and configuration, not rewriting their own weights or shipping unreviewed work. Work that currently takes weeks or months to move through meetings, handoffs, planning, implementation, and review can be explored, built, and tested in minutes. You do not get there by typing faster.
