Hybrid Manufacturing
The Digital Backbone: Bridging the Manufacturing Expertise Gap with Prescriptive AI
The manufacturing sector is currently facing an existential challenge: the "brain drain." With approximately 30% of the workforce nearing retirement, the industry is losing decades of unwritten "tribal knowledge." In an environment of tightening margins and increasing complexity,…
Stop Feeding Garbage to Your AI: Why 80% Accuracy in Document Processing is Now a Failure
A fundamental change in expectations is reshaping how manufacturers evaluate artificial intelligence systems. The long-standing benchmark of 80% accuracy in document processing is no longer acceptable. In the context of AI-driven product and supply-chain decisions, this…
The New Industrial Trade: Your Data Scientist is Useless Without Your Floor Technician
We are getting the conversation about data infrastructure backwards. The chatter online is saturated with debates on cloud versus edge, the merits of one database over another, and the magical promise of AI algorithms. These are…
EU Data Regulation Is Quietly Changing Who Controls Industrial Machine Data
Why access to machine data is becoming a strategic issue For years, many manufacturers operated with limited access to the data generated by their own machines. Interfaces were proprietary, extraction was restricted, and meaningful reuse often…
The 40% Problem in Industrial AI
IIoT World examines why most industrial AI initiatives stall at roughly 40% of their potential, and what manufacturers who push past that wall do differently with contextual intelligence on the shop floor. Why Many Projects Stall…
What Industry Is Starting to Value Differently
For decades, industrial economics was simple. Hardware was expensive. Downtime was expensive. Skilled people were expensive. Decisions, by comparison, were cheap — meetings were free, delays were tolerated, and judgment scaled through hierarchy. That balance is…
Why Stale Data Is More Dangerous Than Downtime
Stale data is more dangerous than downtime in manufacturing because downtime is visible and gets immediate attention, while data decay goes unnoticed. When operators unknowingly act on outdated or incomplete data, quality suffers first, followed by…
When Industrial AI Learns the Wrong Lessons
Industrial AI learns from history — but in most plants, that history is a mess. Behind every AI pilot, there’s a decade of process data, maintenance logs, and scanned reports that were never designed to work…
AI May Be Ready for Manufacturing. But Is Manufacturing Ready for AI?
For the past few years, manufacturers have heard the same message on repeat: AI is ready for industry. But a quieter question is now being asked inside control rooms and boardrooms alike — is industry ready for AI?…
Industrial AI’s Next Phase: Proof Before Scale
At Infinite Uptime’s CXO Circle in Bangkok, the discussion around industrial AI felt different. The room was filled with people who run plants. They weren’t asking whether AI belongs in manufacturing anymore. They were asking how…
By 2028, Plants Won’t Just Run Themselves—They’ll Tell You Why
By 2028, AI-driven industrial plants will not only run autonomously, they will explain their decisions in real time. At the Honeywell User Group in The Hague, Honeywell Chief Product Officer Claudia Chandra and Adlib CEO Chris…
The AI Scaling Problem Nobody’s Solving: Why 140 Applications Delivered Zero Scale
An upstream oil company in Asia built 140 AI applications across 11 offshore assets. Every application was critical to operations. None of them scaled beyond the site where it was built. Jason Schern, Field CTO at…