Sectoral bottleneck: How data accessibility is stalling industrial AI development globally

Limited data access, OEM restrictions and real-time data challenges are stalling industrial AI adoption globally.

- Anil Agrawal

The unprecedented worldwide technological transformation and rising demand are prompting industries to reimagine their production processes.

Manual operations are being integrated with automated solutions, but the next stage of development in industrial AI has stalled for several reasons. However, the focus remains fixated on reducing operating times, enhancing production capabilities, and cutting downtimes. While, in theory, industrial AI not only addresses these issues but also enhances overall manufacturing capabilities, its development is being stalled for a number of reasons.

Let us first, at a high level, examine why industrial AI is lagging overall compared to other fields.

Lack of industrial understanding

The key reason is that people who run industries are not very knowledgeable about AI and those who know AI have very little understanding of industrial processes. Industries are extremely diverse and complex. Understanding them requires an in-depth understanding of several complex fields, such as thermodynamics, kinetics, vibration, heat transfer, fluid mechanics, structural dynamics, electromagnetics, materials science, and more.