Before automating a manual process, leaders should deeply engage with the people on the line. These operators possess invaluable, often un-documented, knowledge about process nuances and potential failure modes that are critical for a successful automation project.

Applying AI to an inefficient workflow with unnecessary approvals or handoffs won't solve the core problem. Teams must first optimize their manual processes to be efficient before looking to AI for automation. This ensures AI adds value rather than just automating existing flaws.

Many companies rush to automate messy processes, which only locks in inefficiency. Instead, learn and refine the process by doing it manually first, as early Amazon and DoorDash did. Only automate once the system is optimized, using technology to speed up good systems, not paper over bad ones.

Before building expensive hardware, validate your automation concept by having a person simulate the robot's functions and limitations. This low-cost method tests the system workflow in a real environment, uncovering hidden requirements and process flaws before a single line of code is written.

A core step in Elon Musk's scaling algorithm is to 'Automate Last.' Tesla discovered that automating a process before it's manually optimized is a recipe for disaster. The Model 3 production crisis was only solved when they abandoned the over-automated line and started building cars by hand in a tent.

Tesla’s core principle to "automate last" came from the disastrous Model 3 launch, where a pre-automated production line failed, forcing the company to build cars by hand in a tent to survive. The experience proved that automating a flawed process only speeds up failure, cementing the need to perfect a manual process first.

Before implementing AI automation, you must validate and refine a process manually. Applying AI to a flawed system doesn't fix it; it just makes the system fail more efficiently and at a larger scale, wasting significant time and resources.

AHC treated its first prototype house as a "sandbox," designing and building it one floor at a time. After assembling the first floor, they used the learnings to redesign the second, and again for the third. This sequential iteration within a single project dramatically accelerated process improvements, cutting assembly time by 70% from the first to third floor.

Don't assume AI can effectively perform a task that doesn't already have a well-defined standard operating procedure (SOP). The best use of AI is to infuse efficiency into individual steps of an existing, successful manual process, rather than expecting it to complete the entire process on its own.