Frame your interaction with AI as if you're onboarding a new employee. Providing deep context, clear expectations, and even a mental "salary" forces you to take the task seriously, leading to vastly superior outputs compared to casual prompting.

To truly leverage AI, professionals must change their approach to tasks. Instead of automatically assuming personal responsibility, the first question should be whether an AI tool can perform it. This proactive mindset shift unlocks significant productivity gains by automating routine work.

Don't limit an AI agent to tasks you can already imagine. After providing full context on your work, ask it open-ended questions like, “How can you make my life easier?” This strategy of “hunting the unknown unknowns” allows the AI to suggest novel, high-value workflows you wouldn't have thought to request.

To find tasks ripe for AI automation, simply screen record yourself performing a repetitive, hour-long task. Then, upload the video to a multimodal LLM like Gemini 3 and ask it what parts can be automated and how much time you could save. This provides concrete, actionable suggestions.

Instead of telling an AI what to do, reverse the prompt. Describe your role, daily friction, and pain points, then ask the AI to devise solutions. This leverages the AI's creativity to generate novel approaches you might not have considered.

To begin automating work with AI, record yourself performing a task on video (e.g., using Loom) while narrating the process. An AI can then analyze the transcript to identify the repeatable steps and logic, which forms the basis for building a custom, automated "skill" that mirrors your workflow.

To discover prime candidates for automation, record a screen video of yourself performing a repetitive, manual task. You can upload this video (up to an hour long) to Google Gemini, which will analyze the workflow, break it down into steps, and provide a concrete plan for how to automate it.

Use AI on your own process to accelerate client work. Record discovery calls, generate transcripts, and feed them into an LLM. Ask it to identify the highest-value automation opportunities and map out the step-by-step workflow based on the client's own words.

To bridge the AI skill gap, avoid building a perfect, complex system. Instead, pick a single, core business workflow (e.g., pre-call guest research) and build a simple automation. Iterating on this small, practical application is the most effective way to learn, even if the initial output is underwhelming.