POSCO Future M is implementing Palantir's artificial intelligence (AI) platform to drive digital transformation in its sales, marketing, and manufacturing processes.
On October 21, POSCO Future M announced it had signed a contract to adopt Palantir's data operations and analytics platform, Foundry, along with its AI platform, AIP (Artificial Intelligence Platform).
Palantir's Foundry integrates and analyzes distributed data within organizations to facilitate its use in operations. AIP combines generative AI to support the utilization of AI agents and automation in business tasks.
Initially, POSCO Future M plans to apply the platform in its sales and marketing sectors, utilizing AI for customer interactions and collaboration. After validating the results, the company will consider gradually expanding the application to manufacturing processes.
Earlier in July, the company presented its digital innovation vision, dubbed 'BoT (Battery of Things) Ecosystem Game Changer,' and has been pursuing an AI transformation (AX) project based on enterprise-wide process innovation (PI) since August.
The introduction of this platform aims to unify the dispersed data across business sectors and establish an operational framework supported by AI from planning to execution, while final decision-making will remain with human personnel.
POSCO Future M aims to enhance customer responsiveness in sales and marketing by over 60% and improve manufacturing productivity by 30% through the adoption of AI. The company also plans to reduce product development time by half compared to current levels.
In the long term, it is pursuing the establishment of a platform that supports collaboration with clients and suppliers by utilizing accumulated data from product production to sales.
Additionally, during the 'AI Festival' held on October 13, POSCO Future M selected 28 exemplary cases of AI utilization developed by its employees. Notably, a system was developed to analyze the heater status of the calciner used in cathode material production, predicting failure risks and inspection priorities using AI.
* This article has been translated by AI.
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