AI use is not an operating model.
Companies are buying seats, deploying copilots, and watching usage rise. But adoption is not output. When AI work lives in private chats and disconnected automations, leaders cannot see who owns the work, what it costs, or whether it creates value.
People should manage AI, not babysit prompts.
Employees understand the goals, context, standards, and exceptions that define a job. Their role should be to assign responsibility, establish boundaries, review evidence, and improve performance. AI should execute the work, retain context, and escalate when human judgment is required.
AI needs a real job.
A worker needs more than instructions. It needs a role, a manager, defined responsibilities, identity and access, configurable workflows, guardrails, a budget, and persistent memory. That structure turns a model into accountable labor.
AI work should earn its place.
Leaders should be able to connect AI spend to completed work, quality, human intervention, and business outcomes. Every AI deployment needs KPIs. Every employee’s use of AI should produce measurable leverage. The question is no longer whether people are using AI. It is whether the work is worth it.