CB-1 — Field execution & sampling intelligence.

AI grades display photos, verifies sampling with photos, GPS and time stamps, and analyses on-site feedback. Today: • DSRs and merchandisers submit a high volume of execution photos across TT/MT. Teams manually review displays, POSM (Point of Sale Materials) and promotion execution for compliance and reward schemes, making QC slow and difficult to scale. • Sampling needs fuller visibility of what happens on the ground and what consumers say, in time to act. Plan vs. reality gap: execution happens far from the brand team. Reported traffic, sampling hits and activity need verification against what actually happened in market. Consumer feedback from PG conversations, survey forms and activation reports is fragmented and unstructured. Teams spend significant time consolidating it manually, and patterns surface too late to change the campaign. • Sampling and field activation activities do not yet have end-to-end visibility from stock allocation through execution. Evidence is often dispersed across photos, files, and separate reports; location, timing, inventory, and results require manual verification. Audits take place after the event, so issues are identified late, execution quality is difficult to validate, and investment cannot easily be linked to outcomes.

• Reduce manual QC with clearer execution assessment and more scalable visibility auditing. Teams shift from checking photos to improving execution. • Reduce manual consolidation and analysis. Faster insights after activations. Transparent, evidence-based execution reporting – fewer gaps between plan and reality. Real-time optimization of sampling execution and communication. Higher ROI on activation investment through faster, data-driven decisions – and stronger new product launches. Sampling investment is verified with evidence from the field, and every consumer interaction can become launch learning. • Improve visibility of inventory and execution progress. Identify locations or activities that do not meet standards earlier. Reduce manual checks and broad-based audits. Focus audit resources on higher-risk cases. Create consistent and traceable execution evidence. Measure sampling effectiveness more accurately and optimize future allocation. • Every execution checked, at scale: AI automatically assesses and grades photos against execution standards, detects potential duplicate/old submissions, and creates real-time visibility tracking. • Priority activations are traceable from plan and stock through location, evidence, and results. Exceptions are flagged early enough for corrective action rather than only during post-campaign review. Success is measured through the share of activities with valid evidence, exception resolution time, reduction in manual audits, shrinkage or deviation rates, and effectiveness per sample distributed.

• DSRs, merchandisers and teams doing execution QC across TT/MT • Brand, trade and activation teams running sampling campaigns • Sampling and field activation teams; Field teams and managers setting execution standards

Note: Synthetic data could be potentially provided during the process, upon approval of the Nestlé team and in compliance with data law.

• To be determined Note: Nestlé's current environment includes Microsoft Azure and Microsoft Copilot. Solutions that can work with this environment are welcome, and final integration and deployment requirements will be confirmed with the Nestlé team.