Global Journal of Engineering and Technology Research (GJETR) · Research article · Open access
Artificial Intelligence and Automation in Enterprise CRM Systems: A Systematic Review of Salesforce Governance, Operational Efficiency, And Customer Engagement in the Renewable Energy Sector
Abdullahi Olalekan Abdulkareem, Olabanji John Ogunyemi, Esther Uzoka, Toussida Fatah Tanguy Minoungou, Harouna Wendpanga Yann Christian Sankara, Chukwunonyelum Rosalyn Ezeako
5 November 2026 · Vol. 2, Issue 11, pp. 634-656
DOI: 10.65150/EP-gjetr/V2E11/2026-01 (registration with Crossref in progress)
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What is this article about?
Renewable energy enterprises manage long customer relationships that run from lead capture through permitting, construction, commissioning, billing, and service, and many now run them on cloud CRM platforms such as Salesforce that offer workflow automation and artificial intelligence. This systematic review, reported to PRISMA 2020, asks what peer-reviewed evidence exists on AI and automation in enterprise CRM, how it bears on governance, data security, operational efficiency, and customer engagement, and how far the evidence reaches renewable energy.
Article at a glance
- Published
- 5 November 2026
- Volume / issue / pages
- Vol. 2, Issue 11, pp. 634-656
- ISSN
- 3051-3782 (online) · 3051-3774 (print)
- Licence
- CC BY 4.0 — free to read and reuse with attribution
- Peer review
- Double-blind, at least two independent reviewers
- Publisher
- EP Journals Group
Abstract
Renewable energy enterprises manage long customer relationships that run from lead capture through permitting, construction, commissioning, billing, and service, and many now run them on cloud CRM platforms such as Salesforce that offer workflow automation and artificial intelligence. This systematic review, reported to PRISMA 2020, asks what peer-reviewed evidence exists on AI and automation in enterprise CRM, how it bears on governance, data security, operational efficiency, and customer engagement, and how far the evidence reaches renewable energy. Searches on 22 October 2026 identified 2,809 records (165 from 29 web queries and 2,644 from a supplementary collection of journal records); after screening and full-text assessment, 14 studies published between 2020 and 2026 were included and synthesised thematically. Only one study, a design study on an electricity marketing system in China, concerned an energy business, and none examined Salesforce Flow or a renewable energy developer. Eight of the 14 studies were rated low quality, and the quantified efficiency results came from synthetic or simulated data. Five themes emerged: efficiency gains that are mostly self-reported, engagement through prediction and personalisation, governance that is discussed more than tested, integration as the main scalability limit, and adoption barriers led by data quality and skills. The paper sets out a research agenda of eight priorities for secure and intelligent CRM in renewable energy enterprises.
Keywords
- customer relationship management
- Salesforce
- artificial intelligence
- process automation
- renewable energy
- data governance
- PRISMA 2020
- thematic synthesis.
Cite this article
Abdullahi Olalekan Abdulkareem, Olabanji John Ogunyemi, Esther Uzoka, Toussida Fatah Tanguy Minoungou, Harouna Wendpanga Yann Christian Sankara, & Chukwunonyelum Rosalyn Ezeako (2026). Artificial Intelligence and Automation in Enterprise CRM Systems: A Systematic Review of Salesforce Governance, Operational Efficiency, And Customer Engagement in the Renewable Energy Sector. Global Journal of Engineering and Technology Research, 2(11), 634-656. https://www.ep-journals.org/articles/artificial-intelligence-and-automation-in-enterprise-crm-systems-a-systematic-ac6fe41f
@article{Abdullahi2026,
title = {Artificial Intelligence and Automation in Enterprise CRM Systems: A Systematic Review of Salesforce Governance, Operational Efficiency, And Customer Engagement in the Renewable Energy Sector},
author = {Abdullahi Olalekan Abdulkareem and Olabanji John Ogunyemi and Esther Uzoka and Toussida Fatah Tanguy Minoungou and Harouna Wendpanga Yann Christian Sankara and Chukwunonyelum Rosalyn Ezeako},
journal = {Global Journal of Engineering and Technology Research},
year = {2026},
volume = {2},
number = {11},
pages = {634-656},
url = {https://www.ep-journals.org/articles/artificial-intelligence-and-automation-in-enterprise-crm-systems-a-systematic-ac6fe41f}
}Published in Global Journal of Engineering and Technology Research (GJETR)
ISSN 3051-3782 (online) · 3051-3774 (print)
Questions about this article
Who wrote this article?
Abdullahi Olalekan Abdulkareem, Olabanji John Ogunyemi, Esther Uzoka, Toussida Fatah Tanguy Minoungou, Harouna Wendpanga Yann Christian Sankara, Chukwunonyelum Rosalyn Ezeako.
Where and when was it published?
In Global Journal of Engineering and Technology Research, Volume 2, Issue 11, pages 634-656, published by EP Journals Group on 5 November 2026.
Is this article free to read?
Yes. It is open access under a Creative Commons Attribution 4.0 (CC BY 4.0) licence, so anyone can read, share and reuse it with attribution.
What is the DOI of this article?
10.65150/EP-gjetr/V2E11/2026-01. Registration with Crossref is in progress.
How do I cite this article?
APA: Abdullahi Olalekan Abdulkareem, Olabanji John Ogunyemi, Esther Uzoka, Toussida Fatah Tanguy Minoungou, Harouna Wendpanga Yann Christian Sankara, & Chukwunonyelum Rosalyn Ezeako (2026). Artificial Intelligence and Automation in Enterprise CRM Systems: A Systematic Review of Salesforce Governance, Operational Efficiency, And Customer Engagement in the Renewable Energy Sector. Global Journal of Engineering and Technology Research, 2(11), 634-656. https://www.ep-journals.org/articles/artificial-intelligence-and-automation-in-enterprise-crm-systems-a-systematic-ac6fe41f
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