CJ CheilJedang launched its Food AI 360 platform globally on June 6, integrating real-time trend analysis, product planning, and consumer response prediction into a single workflow. The rollout gives the company a systematic way to shorten the gap between market signals and finished goods as it expands K-food into the United States, Japan, and Europe.
How the platform processes market data
The system combines work from the food marketing division and two technology units, aiX and DXf, to map the entire development cycle. It tracks shifts in consumer interest and flags trends with high growth potential. Product teams feed those insights into a Concept Studio module that visualizes features and packaging layouts. The platform runs simulated consumer response checks before any prototype leaves the lab, flagging design flaws while production costs remain low.
Tracking results across global markets
CJ CheilJedang tested the system domestically last June before rolling it out to its U.S. operations this past June. American teams now scan local dining habits and purchasing data to shape marketing campaigns and formulation choices. The company plans to extend the tool to Japan and European branches next.
Early output includes Matcha Hetbahn, which targets health-conscious buyers seeking convenient plant-based options, and Bibigo Salmon Steak, which capitalized on a shift toward seafood proteins. Both products followed AI-generated roadmaps rather than traditional focus groups. "We have built a system that can reflect consumer needs in products more quickly and precisely based on AI," a CJ CheilJedang official said. "We will continue to strengthen K-food competitiveness in global markets based on differentiated product development."
Teams looking to replicate this end-to-end approach often start by mapping their own data streams through focused training on AI for Product Development. The system also supports brand extensions, pushing established labels like Hetbahn and Dasida into snack and beverage categories by comparing regional taste preferences against existing formulations.
Why this matters for product development professionals
The platform treats consumer feedback as a continuous input rather than a post-launch audit. Product managers can apply the same workflow to validate concepts, adjust specifications, and schedule launches without waiting for full market release. Professionals interested in building similar internal systems should review the AI Learning Path for Product Managers to see how cross-functional teams structure data collection, concept testing, and iteration cycles.
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