Webinar Announcement: 'Siemens Expert Talk: Why AI in Large Enterprises Fails to Reach Production'

Q: What are the key facts in this article?

This webinar, the first in the 'Siemens Expert Talk' series, addresses why many large enterprises struggle to move AI projects from Proof of Concept (PoC) to full production. It explores how to integrate fragmented data and implement AI into business processes using Siemens' RapidMiner and Mendix platforms.

Q: What is the direct answer?

This webinar, the first in the 'Siemens Expert Talk' series, addresses why many large enterprises struggle to move AI projects from Proof of Concept (PoC) to full production. It explores how to integrate fragmented data and implement AI into business processes using Siemens' RapidMiner and Mendix platforms.

Q: What is the source and date?

Source: https://prtimes.jp/main/html/rd/p/000004944.000054842.html | Date: Date unavailable

■ Large Enterprises Are Increasing AI Investment While large enterprises are increasing their investment in AI, many cases fail to progress from Proof of Concept (PoC) to production, resulting in a lack of tangible outcomes. Although expectations for AI are high, challenges remain in embedding it into daily operations and scaling it across the organization. For many companies, connecting AI investment to production deployment and business results has become a critical theme. ■ Fragmented Data Hinders Business Implementation One reason AI adoption is stalling is that data remains siloed within departments and systems, preventing the cross-functional use of necessary information. Compounded by workplace environments that are risk-averse and challenges regarding trust and literacy in AI, it becomes difficult to move from simply creating an AI model to maintaining its use in the field. ■ A Three-Layer Foundation for Leveraging AI in Management: 'Data Integration,' 'Development & Operations,' and 'Application Implementation' This seminar serves as the introductory session for the first 'Siemens Expert Talk,' outlining the fundamental structure required to leverage AI for business management. Based on Siemens' RapidMiner and Mendix, we will explain the overall picture of how to integrate fragmented data, how to make AI development and operations sustainable, and how to implement them into actual business processes. We will use case studies to show how the three layers of 'Data Integration,' 'Development & Operations,' and 'Application Implementation' interlock to connect AI with business operations, ensuring that projects do not stop at the PoC or partial-use stage but lead to production deployment and business value. ■ Recommended for: - Corporate planning and DX department personnel in large enterprises who want to leverage AI for management. - IT department personnel considering infrastructure development to support AI production deployment, taking into account fragmen

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