AI Economics for Manufacturing & Robotics
In industrial systems, software debt translates directly into physical production downtime. When edge inference latency or IoT ingestion bottlenecks occur, physical yield drops and hardware is damaged.
Edge Computing Constraints
Industrial IoT and robotics often operate in latency-sensitive, bandwidth-constrained environments. Cloud reliance creates physical production risks.
Latency Liability
In manufacturing, inference latency isn't just a bad user experience—it's physical damage or yield reduction. This creates an extreme penalty for non-deterministic AI.
Hardware Integration Debt
Bridging modern AI models with legacy PLCs and SCADA systems from the 1990s creates massive, fragile integration layers.
Automation Margin Erosion
Robotic process automation often masks broken underlying processes. The compute cost of continuous computer vision models often exceeds the human labor saved.
How I Help Industrial Companies
- → Calculate the exact Margin Collapse caused by cloud inference latency
- → Audit the integration debt between modern AI and legacy SCADA systems
- → Evaluate local SLMs (Small Language Models) for edge deployment economics
- → Quantify the true ROI of robotic process automation vs. process refactoring
Need a sector-specific audit?
I run R&D capital audits tailored to your industry's cost structures, compliance requirements, and scaling patterns.
Richard Ewing — AI Economist & Capital Auditor