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The journey to a comprehensive company operating system doesn't start with a grand design. Instead, identify one highly tedious, repeatable task, like triaging Slack requests, and build a simple automation for it. This creates immediate value and momentum.
Instead of an abstract, top-down AI strategy, a practical starting point is to identify the most tedious, repetitive tasks your team performs. Focusing automation efforts on these "chores" provides a tangible win, builds momentum, and offers a low-risk environment for learning AI tools.
The best candidates for automation are rote, repetitive tasks where your brain is disengaged. If a process demands constant thought, adaptation, and complex decision-making, it is highly variable and a poor fit for automation, as you will likely never capture all its requirements.
If you struggle to see your work in terms of 'workflows,' try this: at the end of each day, tell an AI like Codex what you did. After a week, ask it to analyze the transcripts and suggest the most repetitive, time-consuming tasks to automate first.
Shift automation from an ad-hoc tech project to a core management responsibility. Mandate that department leads systematically eliminate monotonous tasks, forcing teams to focus exclusively on high-value, strategic work.
Traditionally a developer tool, scheduled tasks ('cron jobs') can be adopted by non-technical managers to automate repetitive oversight. For example, a cron job can scan a Slack channel at noon and automatically flag team members who missed their daily check-in.
Instead of struggling to find use cases for a new AI tool, instruct the agent to analyze your existing workflows in apps like Slack, Gmail, and Notion. The agent can then propose personalized, high-value automations, effectively telling you how to best use it for your specific needs.
Onboard users (or yourself) to an AI agent like a new human teammate. Start with easy, high-frequency tasks (e.g., summarizing Slack threads). Progress to harder, multi-step tasks (e.g., scheduling a meeting based on replies). Only then, attempt to automate an entire workflow (e.g., running daily growth experiments).
Frame tasks as a chain of "and then" actions an infinitely staffed team would perform. For example, a customer query in Slack is answered, "and then" AI turns it into a help article, "and then" it becomes SEO content. AI makes these previously cost-prohibitive workflows achievable.
To find high-impact automation opportunities, identify tasks you never want to do again—your "anti-to-do list." This framework, which could include manually sorting Slack or entering action items into Asana, provides a clear and motivating starting point for using AI to improve your daily work.
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.