This case study explores Hitachi's comprehensive journey in implementing and evolving industrial AI solutions, offering valuable insights into the practical challenges and approaches of deploying LLMs and AI systems in industrial settings. The presentation, delivered by a Hitachi AI research leader, outlines three distinct phases of industrial AI evolution, with particular emphasis on how generative AI and LLMs are being integrated into existing industrial processes. **Overview and Context** Hitachi approaches AI implementation across four key areas: * Support functions (legal, HR, finance) * Internal core processes * Product integration * Digital offerings for customers The industrial AI context presents unique challenges that differ significantly from consumer applications: * Reliability requirements are extremely high due to safety implications * Data availability is limited (small data scenarios) * Deep domain knowledge integration is crucial * System complexity involves multiple interconnected components * Data types are heterogeneous (sensor data, event data, maintenance records, operational data, acoustic data, vision data, multimodal documents) **Practical Implementation Examples** A significant implementation example involves their fleet maintenance solution deployed at one of the country's largest fleet companies: * Deployed across 700 shops * Services over half a million trucks * Originally used traditional classification approaches for repair recommendations * Recently augmented with generative AI to incorporate service manual understanding * Demonstrates hybrid approach combining historical repair data with LLM-processed manual information The system evolved from a pure classification model to a more sophisticated solution that can: * Process driver complaints * Analyze fault codes * Incorporate historical repair data * Leverage service manual content through LLMs * Handle new truck models with limited historical data **LLM Integration for Knowledge Extraction** A notable innovation involves using generative AI for automatic fault tree extraction from manuals: * Converts technical documentation into structured graph representations * Enables efficient querying of complex technical information * Provides traceability back to source documentation * Maintains human oversight and verification capabilities **Process Transformation Approach** The case study emphasizes that AI implementation success depends on careful process transformation: * Changes are implemented step-by-step rather than wholesale * Each process modification must be carefully validated * Benefits accumulate over time through multiple small improvements * Integration must consider existing workflows and human factors **Evolution to Industrial AI 2.0** The integration of generative AI has expanded capabilities across the entire industrial value chain: *Upstream Processes:* * Material sciences and discovery * RFP response automation * Design optimization *Downstream Applications:* * Customer support enhancement * OT software code completion * Process knowledge extraction * Speech and metaverse integration **Future Direction: Industrial AI 3.0** Hitachi's vision for the future involves: * Integration of virtual and physical agents * Multi-scale, multi-objective AI systems * Enhanced human-AI collaboration * Focus on frontline worker augmentation * Robust reliability and fault tolerance mechanisms **Technical Challenges and Solutions** Key technical focus areas include: * Development of domain-specific small LLMs * Industrial multimodal foundation models * Physics-informed machine learning * Simulation and reinforcement learning * Energy-efficient machine learning implementations **Implementation Considerations** The case study highlights several critical factors for successful industrial AI deployment: * Balance between model size and domain specificity * Integration of temporal and causal relationships * Importance of human domain expertise * Need for verifiable and explainable results * Focus on cost-effectiveness and practical value **Lessons Learned** Important insights from the implementation include: * Small, specialized models often outperform large generic ones in industrial settings * Success requires diverse expertise beyond just machine learning * Traditional AI techniques remain valuable alongside newer generative approaches * Human expertise and domain knowledge are crucial for effective deployment The case study demonstrates how industrial AI implementation requires a careful balance between innovation and practicality, with a strong emphasis on reliability, domain expertise, and systematic process transformation. It shows how LLMs and generative AI can augment rather than replace existing industrial AI solutions, leading to more comprehensive and effective systems.