Past tech solutions for fragmented industries like logistics often failed because they required universal adoption of a new platform. AI can succeed by meeting users in their existing, messy channels—email, texts, calls. It automates work within current workflows rather than forcing a difficult behavioral change, lowering adoption barriers.

Getting traditional companies to adopt AI for their entire production process is a big ask. A "land and expand" strategy is more effective: start by offering the tool for pre-visualization. This provides immediate value with low perceived risk, building trust for deeper integration later.

The initial step in modernizing is not to rebuild, but to understand. AI can ingest source code, user manuals, and even screen recordings to map existing processes and identify optimization opportunities, ensuring the new system improves upon the old rather than just replicating it.

Drawing a parallel to the slow adoption of PCs in the 90s, Boris Churney argues companies won't see AI productivity boosts by simply layering it onto old workflows. The biggest benefits come from placing AI at the core of the business and redesigning processes around its capabilities, eliminating old bottlenecks entirely.

Just as early electricity merely replaced steam engines in old factory layouts, the first wave of robotics just swapped a human for a robot. The new frontier is redesigning the entire factory from scratch with the primary goal of maximizing robot utilization, a fundamental shift that unlocks massive productivity gains.

The historical adoption of electricity in factories shows that true productivity gains came from redesigning the factory floor, not simply replacing steam engines. Similarly, companies must fundamentally re-engineer processes around AI to unlock its transformative potential.

A major bottleneck in deploying industrial automation is that existing heavy machinery is often not 'drive-by-wire.' This necessitates the difficult and time-consuming process of installing mechanical and hydraulic actuators to allow software to control physical systems. This retrofitting reality is a core challenge for the physical AI industry.

The "re-industrialization" push often focuses on advanced AI, but many legacy sectors haven't changed in 50 years and still use clipboards. The biggest initial wins come from low-hanging fruit like basic digitization and better hiring, which can yield massive returns before complex AI is even needed.

Senra Systems is installing camera systems to gather data on manual assembly tasks today, even though the robotic dexterity to automate them doesn't exist yet. This strategy ensures they will have the proprietary dataset needed to train AI models and be first to automate when the technology matures.