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AI gets more interesting when it meets the physical world

September 30, 2026 Emily Chen Clock

AI Meets the Physical World: A Journey from Manual to Automated

AI (read this guide) becomes truly fascinating when it intersects with the tangible aspects of our lives. This article explores what three years of digitizing a traditionally manual industry taught us about data, agents, and the potential of AI in transforming business models.

Digital Transformation in BRKZ

At BRKZ, we faced a common challenge: a deeply manual building-material procurement process filled with fragmented supply chains, phone calls, WhatsApp messages, spreadsheets, PDFs, and countless people involved. Our solution? Spend the first three years bringing our core workflows onto shared systems to create a digital trail for every transaction.

This digital transformation resulted in tens of millions of structured data points across products, suppliers, transactions, and payment behavior. Even though RFQs (Requests for Quotation) and delivery notes can still arrive via WhatsApp, agents now convert these inputs into structured records, enabling the business to take meaningful actions.

The AI Journey: Systemize, Capture, Understand, Automate, Agentize

I've identified a common progression for traditional industries embracing AI:

  1. Systemize: Organize and streamline core workflows, ensuring every step leaves a digital trail.
  2. Capture: Collect vast amounts of structured data from these processes.
  3. Understand: Analyze the captured data to gain insights.
  4. Automate: Implement automated systems to perform repetitive tasks based on established patterns.
  5. Agentize: Empower agents to make decisions within predefined limits, leveraging the insights gained from steps 1-4.

AI's Impact on Industries

The potential of AI lies in its ability to fundamentally alter a company's operating model, especially in sectors where technology has historically struggled:

  • Physical goods management
  • Fragmented supply chains
  • Credit and logistics

By first systemizing and capturing data, we can then move towards understanding the context within which AI models operate. This contextual awareness is crucial for developing specialized, industry-specific AI solutions that provide real value.

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