Supported use cases for SAP AI Joule

In industrial environments, unexpected machinery failures can result in expensive downtime along with SAP AI Joule marks a pivotal advancement in enterprise AI, serving as a dynamic copilot that augments user productivity, enhances decision-making accuracy, and accelerates workflow automation across SAP’s cloud portfolio. By embedding generative AI and conversational interfaces into core business applications, Joule is redefining how enterprise users interact with data and processes. This review synthesizes Joule’s supported use cases, experimental impacts, architectural principles, and prevailing challenges. While early results are promising, sustained success will depend on advancements in AI transparency, domain adaptation, ethical governance, and scalability. Future research must prioritize explainable AI, federated learning approaches, and dynamic adaptability to truly unlock Joule’s transformative potential for the intelligent enterprise.

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Publication Details

Journal
International Research Journal on Advanced Engineering Hub (IRJAEH)
Published
2025-05-05
DOI
https://doi.org/10.47392/irjaeh.2025.0303
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
0.00
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article

Supported use cases for SAP AI Joule

Anish Gupta
International Research Journal on Advanced Engineering Hub (IRJAEH)
Big Data and Business Intelligence
article

Supported use cases for SAP AI Joule

Anish Gupta
article en

Abstract

In industrial environments, unexpected machinery failures can result in expensive downtime along with SAP AI Joule marks a pivotal advancement in enterprise AI, serving as a dynamic copilot that augments user productivity, enhances decision-making accuracy, and accelerates workflow automation across SAP’s cloud portfolio. By embedding generative AI and conversational interfaces into core business applications, Joule is redefining how enterprise users interact with data and processes. This review synthesizes Joule’s supported use cases, experimental impacts, architectural principles, and prevailing challenges. While early results are promising, sustained success will depend on advancements in AI transparency, domain adaptation, ethical governance, and scalability. Future research must prioritize explainable AI, federated learning approaches, and dynamic adaptability to truly unlock Joule’s transformative potential for the intelligent enterprise.

International Research Journal on Advanced Engineering Hub (IRJAEH)Vol. 3(05)
Dr. A.P.J. Abdul Kalam Technical University (IN)
Openalex Percentile: Top 84%
Big Data and Business Intelligence
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Supported use cases for SAP AI Joule — Anish Gupta · International Research Journal on Advanced Engineering Hub (IRJAEH) (2025) | TGRS Research Map | TGRS