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The hallucinations have decreased, and the manual verification workload by "people" has reduced. The reference presentation rate has improved, and the accuracy of responses has increased. We will introduce a case of "AI-Readiness" that addresses the concern of "image data not being searchable." In our support for AI projects in the manufacturing industry, a common issue we hear is, "We have attempted to implement generative AI and build RAG, but we are not getting the desired output." By translating complex internal documents into a "system language" that AI can understand through "AI-Readiness," we can build a pipeline for data preprocessing. This enables existing RAG and AI models to decode input data in detail, leading to solutions. [Challenges] ■ Projects are halted due to multiple factors such as data deficiencies, evaluation, and improvement cycles. ■ Image data is not searchable. *For more details, please download the PDF or feel free to contact us.

This document is a white paper that clarifies the actual state of AI utilization in the BtoB sector based on survey data, providing essential know-how to achieve AI utilization that leads to results. Currently, the shortage of AI talent is seen as a bottleneck, but the real challenge lies in the transition from the implementation phase to the operational phase (establishment and improvement). While rule development is progressing, the assurance of data quality and reliability has not kept pace, revealing a structure where improvement cycles do not operate due to the inability to measure ROI and outcomes. This document explains the importance of an AI-ready data infrastructure and how to visualize your company's current position using frameworks such as FAIR Assessment and FAIR Index. [Contents] ■ The actual state of AI utilization in the BtoB sector ■ The real barriers to promoting AI ■ The importance of an AI-ready data infrastructure *For more details, please download the PDF or feel free to contact us.