Transition from Traditional SAP/SaaS-Based Systems to AI/ML-Based Enterprise Structures for Energy Companies

As the global energy industry navigates unprecedented challenges- ranging from decarbonization and renewable integration to regulatory demands and consumer expectations- the limitations of legacy SAP/SaaS systems have become increasingly evident. This article explores how energy enterprises are transitioning toward AI/ML-based digital structures, emphasizing scalable system architecture, workforce upskilling, and organizational relearning. Drawing upon real-world examples from leading global and Indian companies, recent policy developments, and academic studies, the article illustrates how AI/ML adoption transforms energy operations. It presents comparative analyses, enriched system requirement insights, capacity scaling, and case examples highlighting cost reductions, efficiency gains, and sustainability improvements. Visuals, tables, and references are integrated to provide clarity and depth, making this piece relevant to both academic researchers and business leaders.

Authors

Publication Details

Journal
Mineral Metal Energy Oil Gas and Aggregate
Published
2025-09-18
DOI
https://doi.org/10.18311/2meoga/2025/v2i3/51245
Primary Topic
Reservoir Engineering and Simulation Methods
Type
article
Field-Weighted Citation Impact
0.00
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article

Transition from Traditional SAP/SaaS-Based Systems to AI/ML-Based Enterprise Structures for Energy Companies

V.K. Khandelwal
Mineral Metal Energy Oil Gas and Aggregate
Reservoir Engineering and Simulation Methods
article

Transition from Traditional SAP/SaaS-Based Systems to AI/ML-Based Enterprise Structures for Energy Companies

V.K. Khandelwal
article en

Abstract

As the global energy industry navigates unprecedented challenges- ranging from decarbonization and renewable integration to regulatory demands and consumer expectations- the limitations of legacy SAP/SaaS systems have become increasingly evident. This article explores how energy enterprises are transitioning toward AI/ML-based digital structures, emphasizing scalable system architecture, workforce upskilling, and organizational relearning. Drawing upon real-world examples from leading global and Indian companies, recent policy developments, and academic studies, the article illustrates how AI/ML adoption transforms energy operations. It presents comparative analyses, enriched system requirement insights, capacity scaling, and case examples highlighting cost reductions, efficiency gains, and sustainability improvements. Visuals, tables, and references are integrated to provide clarity and depth, making this piece relevant to both academic researchers and business leaders.

Mineral Metal Energy Oil Gas and Aggregate
Openalex Percentile: Top 67%
Reservoir Engineering and Simulation Methods
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Transition from Traditional SAP/SaaS-Based Systems to AI/ML-Based Enterprise Structures for Energy Companies — V.K. Khandelwal · Mineral Metal Energy Oil Gas and Aggregate (2025) | TGRS Research Map | TGRS