Procter & Gamble adopts Labelbox as enterprise-wide AI training data platform
P&G collects large amounts of unstructured data (images, videos, and text) across prototyping, testing, and manufacturing, and needed a central platform to label this data for AI model training while maintaining strict data privacy, security, and full process transparency.
Previous labeling partners operated as black boxes — data was sent out and returned labeled with no visibility into the labeling process or how data was being enriched.
After three years of expansion, Labelbox became an enterprise-wide corporate solution deployed across all P&G business units globally, described as a transformation in how they work and driving faster model iteration and time to market.
Frequently asked questions
What did this team achieve with this AI workflow?
After three years of expansion, Labelbox became an enterprise-wide corporate solution deployed across all P&G business units globally, described as a transformation in how they work and driving faster model iteration…
What tools did this team use?
Labelbox.
What results were reported?
Platform expansion duration: three years; Iteration speed improvement: increasing the speed of iteration, which leads to faster time to market with better insights; Workflow transformation: transformation in how we do our work; Model iteration pace: accelerate the pace of their model iterations and time to market (source-reported, not independently verified).
What failed first in this deployment?
Previous labeling partners operated as black boxes — data was sent out and returned labeled with no visibility into the labeling process or how data was being enriched.
How is this quality assurance AI workflow structured?
Collect unstructured data → Assign internal or external labelers → Label and enrich data with tags → Monitor data maturity → Recalibrate labeling processes → Generate AI training data.