In my experience working with AI automation, the most important first step is to clearly define the problem you want to solve. Writing down every manual task and understanding why it happens helps you get a clear picture of the process flow. For example, tracking accounts receivable or maintaining spreadsheets can be tedious but highly automatable once you break it down. One of the biggest misconceptions is that automation requires heavy coding skills. Nowadays, tools such as Claude Co-worker or Codex allow even those with limited technical background to assess the problem, gather needed files like Excel sheets and emails, and create automated solutions. Combining these AI tools with a thorough walk-through of the tasks alongside the team member who performs them can fully map out the process and expose automation opportunities. A ‘harness’ concept—essentially a system holding all the AI skills, evaluations, and tasks together—is useful to centralize automation components. Once built, you can run these processes on schedules like cron jobs or within existing IT frameworks, significantly reducing manual workload. Personally, I recommend starting small by automating repetitive daily tasks such as generating invoices, tracking license expirations, or compiling weekly reports. This saves time for your Business Affairs team or other departments, allowing them to focus on higher-value work. Remember, automation is iterative; continuously refine and repeat your processes to maximize benefits. Finally, don’t hesitate to reach out for guidance or experiment with AI tools firsthand. Automating anything is more accessible than ever, and investing time upfront can yield lasting productivity improvements.