On average, companies ran 2 proofs of concept last year, but only 23.1% reached production - the rest disappeared into the “Bermuda Triangle” of feasibility studies. In October 2026, 21% said they use AI that has not helped (earlier surveys: 0 to 8%), and fully successful projects dropped from 58% (September 2025) to 32%.
Why it Happens: AI Without Context
In this talk in the Talk Lounge, Philip Privalov put it simply: AI is rarly the problem. What projects lack is meaning and data context. A signal like State = 4 is worthless as long as the system does not know which machine and which state it refers to.
Five Real Cases from the Booth
Different industries, the same question: how do we get value from our data? Open the cases that sound like your situation.
An interim manager told us about an AI system he had tested. Without real production context, it concluded that the best way to avoid errors was for the line to stop completely, which is logical for the model, but not in practice.
What this means for you: If your AI tool does not know your process, its answers will not fit your line.
Visitors from India and Morocco made it clear: data-driven machine optimization is no longer a privilege of high-wage countries. Efficiency and transparency are now key to competing globally.
What this means for you: Your competitors abroad are digitalizing too, so waiting is the bigger risk.
A well-known premium manufacturer needs a concept for highly automated assembly by year-end. For gluing and riveting, capturing process data and quality parameters from the very first second is decisive.
What this means for you: Plan data capture together with the line, not after it, because adding it later costs more.
Check Your Project with Us
Took the test and found an open question? Bing it to a short call - we will look at your use case and your data together.
