Generative AI Adoption in an Energy Company: Exploring Challenges and Use Cases
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866912894531141632 |
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| author | Sami, Malik Abdul Rasheed, Zeeshan Olenius, Meri Waseem, Muhammad Kemell, Kai-Kristian Rasku, Jussi Abrahamsson, Pekka |
| author_facet | Sami, Malik Abdul Rasheed, Zeeshan Olenius, Meri Waseem, Muhammad Kemell, Kai-Kristian Rasku, Jussi Abrahamsson, Pekka |
| contents | Organisations are examining how generative AI can support their operational work and decision-making processes. This study investigates how employees in a energy company understand AI adoption and identify areas where AI and LLMs-based agentic workflows could assist daily activities. Data was collected in four weeks through sixteen semi-structured interviews across nine departments, supported by internal documents and researcher observations. The analysis identified areas where employees positioned AI as useful, including reporting work, forecasting, data handling, maintenance-related tasks, and anomaly detection. Participants also described how GenAI and LLM-based tools could be introduced through incremental steps that align with existing workflows. The study provides an overview view of AI adoption in the energy sector and offers a structured basis for identifying entry points for practical implementation and comparative research across industries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_09846 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Generative AI Adoption in an Energy Company: Exploring Challenges and Use Cases Sami, Malik Abdul Rasheed, Zeeshan Olenius, Meri Waseem, Muhammad Kemell, Kai-Kristian Rasku, Jussi Abrahamsson, Pekka Software Engineering Computers and Society Organisations are examining how generative AI can support their operational work and decision-making processes. This study investigates how employees in a energy company understand AI adoption and identify areas where AI and LLMs-based agentic workflows could assist daily activities. Data was collected in four weeks through sixteen semi-structured interviews across nine departments, supported by internal documents and researcher observations. The analysis identified areas where employees positioned AI as useful, including reporting work, forecasting, data handling, maintenance-related tasks, and anomaly detection. Participants also described how GenAI and LLM-based tools could be introduced through incremental steps that align with existing workflows. The study provides an overview view of AI adoption in the energy sector and offers a structured basis for identifying entry points for practical implementation and comparative research across industries. |
| title | Generative AI Adoption in an Energy Company: Exploring Challenges and Use Cases |
| topic | Software Engineering Computers and Society |
| url | https://arxiv.org/abs/2602.09846 |