Full Breakdown
AI Hype Meets Manufacturing Reality
7/13/2026, 5:59:56 AM
Data Highlighting Early Failures
- A January Forbes article warned that “For AI to be useful, it has to operate inside production systems, grounded in real data and real workflows, with humans accountable for outcomes. When applied this way, AI helps people move faster and see more clearly. It doesn’t replace judgment.”
- S&P Global’s executive survey reported that 42 % of organisations abandoned most of their AI initiatives in 2025, up from 17 % in 2024.
- A 2024 RAND report found that more than 80 % of industrial AI projects fail, citing process complexity, poor data quality and lack of real-world context.
- Ford’s experience illustrates the issue: after deploying AI-driven design tools, the company reinstated over 350 veteran engineers to improve data collection and model training.
Official Views on the AI Market
Jeremy Grantham, founder of Grantham, Mayo, & Co., cautioned that “AI was similar to the invention of railways or the internet in that everyone overinvests and, when they realise it is a utility, such as electricity, they understand that there is not much money to be made from the invention itself, except for the companies that build services around it.”
Charles Poon, vice president of vehicle hardware engineering at Ford, admitted, “Mistakenly, we thought that by just introducing artificial intelligence and adjusting the design requirements that we had, that that would produce a high-quality product.”
Criticism and Market Skepticism
City analysts and financial economists are increasingly vocal that the AI boom may be unsustainable. They point to the mismatch between AI’s promise and its current capability to handle variable, non-repeatable manufacturing tasks. The consensus among these critics is that the sector’s over-optimism could precipitate a correction, though the timing remains uncertain.
Outlook
The article concludes that the AI bubble “is likely to burst at some point, though when remains unknown,” underscoring the need for realistic expectations and stronger integration of human expertise in AI deployments.
