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Inside the Data Bottleneck Slowing Visual and Physical AI

A new white paper from IEEE Spectrum and Wiley, sponsored by Voxel51, reports that 78% of more than 700 surveyed practitioners already see measurable value from visual and physical AI, while 74% still consider the field underinvested. The survey finds that successful teams invest nearly 3x more time in data work than struggling teams, and that data problems cause the majority of model failures, with 92% of practitioners believing the field is heading toward greater emphasis on data curation.

read1 min views1 publishedAug 12, 2026
Inside the Data Bottleneck Slowing Visual and Physical AI
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Download this complimentary White Paper today! #

This white paper reports how more than 700 practitioners build visual and physical AI, why models fail, and where data work decides production success.

**What you will learn about: **

  • Why 78% of teams already see measurable value from visual & physical AI, while74% still consider the field underinvested relative to its opportunity. - Why the teams that ship successfully invest nearly 3x more time in data work than teams that struggle - Where 92% of practitioners believe the field is heading next

Click ‘LOOK INSIDE’ to Download Now. #

LOOK INSIDE IEEE Spectrum and Wiley are proud to bring you this White Paper, sponsored by Voxel51

More Information #

The last decade of AI progress was built on text, but the frontier has shifted toward data from the physical world. Video, LiDAR point clouds, sensor streams, and other high-dimensional data now drive systems that perceive, reason, and act in physical space. This report, based on a 2026 survey of more than 700 professionals, documents how teams actually build physical AI today. It finds that data problems cause the majority of model failures, and that curating data matters more than chasing larger architectures. Annotation remains costly and wasteful, because teams often label everything and then discard much of it before production. The findings show why data work, not data collection, separates teams that ship from teams that stall.

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