Instead of businesses relying on purely digital AI assistants that reason from text-based training data, physical AI systems could compress years of R&D into months by running thousands of automated physical experiments, analyzing sensor feedback, and iterating designs in real manufacturing environments - transforming how we develop everything from spacecraft components to pharmaceutical compounds. This means a materials science team asking "design a lighter alloy for rocket engines that withstands 3000°C" could receive designs validated through actual physical testing cycles rather than purely computational simulations, or drug discovery labs could have AI systems that learn from wet lab results rather than just molecular databases.

Start mapping your organization's workflows that currently require expensive physical prototyping, materials testing, or manufacturing trial-and-error - whether that's product engineering, quality control, R&D experimentation, or process optimization - and document the specific physical constraints, sensor data, and success metrics you're optimizing for, because when physical AI infrastructure matures, these documented experimental workflows become automation opportunities that require deep integration between AI systems and physical testing environments that your competitors who haven't mapped their processes will take years to build.