Don't use your most powerful and expensive AI model for every task. A crucial skill is model triage: using cheaper models for simple, routine tasks like monitoring and scheduling, while saving premium models for complex reasoning, judgment, and creative work.

Instead of chasing the latest hyped AI model, focus on building modular, system-based workflows. This allows you to easily plug in new, better models as they are released, instantly upgrading your capabilities without having to start over.

AI agent platforms are typically priced by usage, not seats, making initial costs low. Instead of a top-down mandate for one tool, leaders should encourage teams to expense and experiment with several options. The best solution for the team will emerge organically through use.

An AI tool's inability to perform a task a month ago doesn't mean it can't today. The guest notes Copilot went from producing useless spreadsheet templates to fully functional models in months. Users should periodically re-test tools on previously failed tasks to leverage rapid, often unannounced, improvements.

Don't view AI tools as just software; treat them like junior team members. Apply management principles: 'hire' the right model for the job (People), define how it should work through structured prompts (Process), and give it a clear, narrow goal (Purpose). This mental model maximizes their effectiveness.

Unlike traditional, long-lasting infrastructure, AI skills have a short half-life due to rapid model updates and changing contexts. Treat them as iterative, ephemeral assets that must be re-evaluated on a monthly basis to remain effective.

Instead of perfecting a single prompt, treat AI interaction as a rapid, iterative cycle. View the first output as a draft. Like managing an employee, provide feedback and refine the result over several short cycles to achieve a superior outcome, which is more effective than front-loading all effort.

Just as you use different social media apps for different purposes, you should use various specialized AI tools for specific tasks. Relying on a single tool like ChatGPT for everything results in watered-down solutions. A better approach is to build a toolkit, matching the right AI to the right problem.

A significant source of competitive advantage ("alpha") comes from systematically testing various AI models for different tasks. This creates a personal map of which tools are best for specific use cases, ensuring you always use the optimal solution.