Our logistics team is drowning in manual bill of lading matching, and we are looking at an AI data extraction tool to solve it. How do we structure a Thinking Time session using Keith Cunningham's framework to ensure we are actually solving an operational problem instead of automating a broken process?

Question

Tyler Smith · Accepted Answer

Automating a messy, broken process with AI only allows you to make mistakes faster and at a larger scale. Before you purchase an extraction tool, you must determine if your paperwork issue is a solvable operational problem or an inherent predicament of your current supply chain setup. Schedule a quiet, uninterrupted thirty-minute Thinking Time session with a blank pad of paper. Write this high-value question at the top of the page: How might we simplify our incoming documentation requirements so that we can reduce matching errors before we even touch an automation tool? Force yourself to generate at least fifteen answers. You might discover that the root cause of your paperwork headache is not the manual matching process itself, but rather a lack of standard formatting requirements for your suppliers. If your vendors are sending chaotic, non-standard PDFs, introducing an AI tool is simply trying to cure a symptom. Instead, you should solve the foundational problem by establishing strict data submission standards. If you clean up the incoming data first, you might find that you do not even need an expensive AI tool, or that a simple, free automated template can handle the work. Use this disciplined thinking to ensure you are only applying technology to highly optimized, clean workflows, rather than paying a steep dumb tax to automate organizational chaos.