# Before Building AI Training Tools, Observe Workers First
Companies rushing to build artificial intelligence modules for employee training often skip a crucial step: actually watching workers do their jobs. The approach outlined in this guidance emphasizes direct observation of real workflows before designing any digital learning tool.
The practice involves shadowing employees during their normal workday. A trainer or instructional designer must position themselves strategically, maintain distance when appropriate, and resist the impulse to take constant notes. The clipboard stays in the car. Instead, the focus centers on understanding how workers actually make decisions, where bottlenecks occur, and which moments create real learning opportunities.
This observational method identifies the two or three critical decision points where training intervention matters most. Rather than building comprehensive modules covering everything a worker does, effective AI tools target the specific moments where employees struggle or where mistakes happen. A finishing technician's workflow differs vastly from a parts-counter representative's, yet both benefit from training designed around their actual pain points.
The questions trainers ask during observation matter enormously. Asking "Why do you do it this way?" often yields unhelpful answers. Better questions explore context: "Walk me through what happens when that system goes down" or "Tell me about the last time this process failed." These prompts reveal real obstacles and decision trees that should drive tool design.
This approach reflects a broader shift in corporate learning away from one-size-fits-all training toward job-embedded, workflow-specific instruction. AI modules built on superficial understanding of worker tasks tend to miss the mark, creating tools employees avoid or complete without actually changing performance.
Organizations serious about using AI for training improvement must invest time in observation first. The upfront investment in shadowing workers saves money and time later by ensuring training tools address real needs rather than imagined ones. Skipping this step results in expensive, unused technology and workers who continue struggling with the same problems.