Transitioning from AI User to AI Manager
Nate Herk | AI Automationgo watch the original →
the gist
To avoid displacement by AI, shift from manual task execution to managing agentic workflows by treating AI as a high-level employee that requires onboarding, clear goal-setting, and iterative feedback.
The Shift to Agentic Workflows
The primary shift in AI utility is moving from static chat interfaces to agentic tools that can reason, plan, and execute multi-step tasks across local files and external applications. Unlike traditional automation that requires rigid, pre-defined instructions, agentic AI operates by receiving a high-level goal and autonomously determining the necessary steps to reach it. This capability allows users to offload complex, time-consuming workflows—such as data analysis, application prototyping, and lead generation—to an AI that acts more like a co-founder or specialized employee than a simple chatbot.
Implementing the Manager Mindset
To effectively leverage these tools, users must adopt a management framework rather than a technical one. This involves three core phases:
- Onboarding: Treat the AI as a new hire by providing context about your business, specific goals, preferred communication styles, and constraints. The quality of output is directly proportional to the depth of the context provided.
- Iterative Execution: Select one repetitive, real-world task to automate. Do not accept the first output blindly; review the results, provide specific corrections, and iterate until the output meets your professional standards. This process builds trust and refines the AI's performance.
- System Stacking: Once individual tasks are mastered, connect the AI to your primary toolset (e.g., email, CRM, analytics platforms) to allow it to pull data and execute actions without manual file transfers or copy-pasting. Maintain control by requiring the AI to request permission before executing sensitive actions.
Measuring Performance
Success in this transition is measured by quantifiable time savings or output increases. Users should establish a baseline for a specific task—such as the time required to generate and enrich 50 leads—and track performance metrics monthly. If targets are not met, treat the AI workflow as a system that requires debugging and optimization rather than a failed experiment.