How to Decide What Work AI Should Do for You: The AI Deputization Audit

The shift in artificial intelligence from raw model capability to contextual execution is transforming how professionals automate recurring workflows. New tools like GrokBot’s "Teach-a-Task" and ChatGPT’s "Computer History" allow AI to learn by observing user behavior, moving beyond simple prompt-based automation. To navigate this, the "Deputization Audit" provides a framework for evaluating tasks based on frequency, teachability, checkability, stakes, and human necessity. Meanwhile, the release of Gemini 3.7 Flash underscores a growing industry emphasis on speed, though recent performance data indicates that model efficiency and token usage often outweigh raw price-per-token savings. Ultimately, successful AI integration requires moving beyond generic automation toward intentional, high-stakes deputization, where users identify specific blockers—such as legacy software or complex processes—that these new observational tools are uniquely positioned to resolve.