AI adoption is not limited to tech and white-collar work; it has become a universal business consideration. For example, a lumber mill in Vermont is using AI to sort planks, a task for which they struggled to hire skilled labor. This shows AI is being deployed as a practical solution to specific, localized labor shortages in legacy industries.

Effective enterprise AI needs a contextual layer—an 'InstaBrain'—that codifies tribal knowledge. Critically, this memory must be editable, allowing the system to prune old context and prioritize new directives, just as a human team would shift focus from revenue growth one quarter to margin protection the next.

Just as Kaizen and “China cost” revolutionized physical product businesses over 40 years, AI is initiating a similar, decades-long optimization cycle for intellectual property and human-centric processes. Companies that apply this “digital Kaizen” to lean out workflows will gain a compounding cost and efficiency advantage, similar to what Danaher achieved in manufacturing.

The founder of Phaja, an AI for data center optimization, highlights the aging workforce ("white hair") and skilled labor shortage in the industry. This frames AI agents as a critical tool for augmenting a retiring workforce and preserving institutional knowledge, going beyond simple cost savings.

Shift your view of AI from a passive chatbot to an active knowledge-capture system. The greatest value comes from AI designed to prompt team members for their unique insights, then storing and attributing that information. This transforms fleeting tribal knowledge into a permanent, searchable organizational asset.

Manufacturing faces a crisis as veterans with 30+ years of experience retire, taking unwritten operational knowledge with them. Dirac's software addresses this by creating a system to document complex assembly processes, safeguarding against knowledge loss and enabling less experienced workers to perform high-skill tasks.

The critical barrier to AI adoption isn't technology, but workforce readiness. Beyond a business need, leaders have a moral—and in some regions, legal—responsibility to retrain every employee. This ensures people feel empowered, not afraid, and can act as the human control layer for AI systems.

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.

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.