인과 AI가 제조업의 ＂두뇌 유출＂을 완화하고 고급 의사결정을 촉진하는 방법

This IDC Perspective focuses on providing insights from an organization that has found success with causal AI, exploring how organizations can take advantage of this innovation in the competitive marketplace. For instance, how can it impact the insecurities of the labor pool? How can it leverage a company's unique knowledge base to outcompete rivals in decision-making speed? And how difficult is it to implement and manage these causal models?We are all hearing about the fantastic capabilities of artificial intelligence (AI) in the realm of machine learning and generative AI, but there is another form of AI that enables massively fast and accurate automated reasoning and decision-making: causal intelligence or causal AI. What's interesting about causal intelligence is that it is designed to capture and exploit the explicit knowledge that is held inside a company by its professional experts and in its IP and processes. This knowledge is an organization's unique information asset; causal intelligence can enable superior performance if the company is "smarter" than its competition (in terms of its ability to make better, faster, and more complex decisions).Causal intelligence has an impact in two key areas:The ability to capture, protect, and support an organization's business knowledgeThe ability to apply that business knowledge to solve business problems at speeds and complexities well beyond current human skill levelsCompanies that adopt the use of causal intelligence can address the increasing loss of critical knowledge as aging knowledge workers retire, primarily in manufacturing and operations."Causal models have been in background of AI solutions. They should be put on the table as a powerful tool to help make business decisions and deliver a strategic advantage," says Bob Multhaup, adjunct research advisor with IDC's IT Executive Programs (IEP).

Executive Snapshot

Situation Overview

• What Is Causal AI?
• How Causal Intelligence Can Address Both Labor and Knowledge Problems
• Examples of Successful Causal AI Initiatives
  • Automated Touchless Order Management
  • Optimal Raw Material Procurement
  • Product Quality Testing and Optimization
  • Manufacturing Process Improvement

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• Synopsis