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.

Customers describe an idealized version of their world in interviews. To understand their true problems and workflows, you must be physically present. This uncovers the crucial gap between their perception and day-to-day reality.

Critical process knowledge often exists only in operators' heads—the nuances and undocumented tricks that ensure success. Systematically capturing this 'tribal knowledge' during tech transfer is crucial for preventing hard-to-diagnose failures at the new site.

To build coordinated AI agent systems, firms must first extract siloed operational knowledge. This involves not just digitizing documents but systematically observing employee actions like browser clicks and phone calls to capture unwritten processes, turning this tacit knowledge into usable context for AI.

Before any AI is built, deep workflow discovery is critical. This involves partnering with subject matter experts to map cross-functional processes, data flows, and user needs. AI currently cannot uncover these essential nuances on its own, making this human-centric step non-negotiable for success.

AI tools like LLMs thrive on large, structured datasets. In manufacturing, critical information is often unstructured 'tribal knowledge' in workers' heads. Dirac’s strategy is to first build a software layer that captures and organizes this human expertise, creating the necessary context for AI to then analyze and add value.

The most effective way to build a powerful automation prompt is to interview a human expert, document their step-by-step process and decision criteria, and translate that knowledge directly into the AI's instructions. Don't invent; document and translate.

Companies often believe their processes are linear and simple, like a 7-step plan. However, process mining reveals the messy reality: a complex, 20-step workflow with numerous exception loops that handle the majority of cases. Exposing this gap is the first step to optimization.

Official process documents are misleading. A crucial FDE task is observing employees to understand the complex, exception-filled reality of how work gets done. This undocumented knowledge, often in one person's head, is essential for building effective AI systems that don't break on edge cases.