[Paper] Generative AI Adoption in an Energy Company: Exploring Challenges and Use Cases
Source: arXiv - 2602.09846v1
Overview
A recent qualitative study dives into how a large energy company is beginning to weave generative AI—especially large‑language‑model (LLM)‑driven agents—into its day‑to‑day operations. By interviewing employees across nine departments, the authors map out concrete pain points, promising use‑cases, and the incremental steps needed to turn AI ideas into working tools. The findings give tech leaders a ready‑made checklist for piloting generative AI in similarly regulated, data‑heavy industries.
Key Contributions
- Empirical map of AI‑ready tasks – identifies reporting, forecasting, data wrangling, maintenance, and anomaly detection as the top domains where GenAI can add value.
- Workflow‑centric adoption framework – proposes a step‑by‑step, “plug‑into‑existing‑processes” approach that reduces disruption and builds trust among non‑technical staff.
- Cross‑departmental perspective – gathers insights from 16 semi‑structured interviews across nine functional areas, highlighting both common and department‑specific needs.
- Practical entry‑point taxonomy – offers a structured list of low‑risk, high‑impact pilot ideas that can be benchmarked across other energy or heavy‑industry firms.
- Foundation for comparative research – sets a baseline for future studies that want to compare AI adoption trajectories between sectors (e.g., manufacturing, utilities, finance).
Methodology
The researchers conducted a four‑week field study at a mid‑size energy provider. Their data collection mix included:
- Sixteen semi‑structured interviews with employees ranging from front‑line technicians to senior analysts.
- Internal documentation review (process manuals, KPI dashboards, maintenance logs) to contextualize interview statements.
- Researcher observations of daily workflows, focusing on where data is created, transformed, and consumed.
The interview transcripts were coded using a thematic analysis framework. Themes were iteratively refined until the team converged on a set of “AI‑useful” activities and the perceived barriers to adoption.
Results & Findings
| Area | How GenAI Helps | Example Pilot |
|---|---|---|
| Reporting | Auto‑summarize weekly performance metrics, generate narrative explanations for dashboards. | LLM‑driven “report‑bot” that drafts monthly compliance summaries. |
| Forecasting | Augment statistical models with natural‑language explanations of trend drivers. | Prompt‑based tool that translates weather‑model outputs into actionable load forecasts. |
| Data Handling | Clean, tag, and enrich raw sensor streams using few‑shot prompting. | Automated data‑quality assistant that flags missing values in SCADA logs. |
| Maintenance | Generate check‑list steps from equipment manuals; suggest parts based on failure histories. | Agent that proposes a work order after detecting a vibration anomaly. |
| Anomaly Detection | Explain why a metric deviates from baseline, suggest root‑cause hypotheses. | Chat interface that takes an outlier alert and returns a concise diagnostic narrative. |
Participants consistently emphasized incremental integration: start with “assist‑only” bots that surface suggestions, then evolve to autonomous agents once trust is earned. The study also uncovered cultural hurdles—fear of job displacement and the need for clear governance around AI‑generated content.
Practical Implications
- Rapid Prototyping Roadmap – Developers can spin up LLM‑powered micro‑services (e.g., using LangChain or LlamaIndex) that hook into existing data pipelines, delivering immediate productivity gains without overhauling legacy systems.
- Compliance‑Friendly Design – By keeping AI in an “advisory” role initially, firms can satisfy regulatory auditors who demand human oversight of critical decisions.
- Skill‑Shift Guidance – The findings suggest a training focus on prompt engineering and AI‑augmented troubleshooting rather than full‑stack AI development, lowering the barrier for adoption among domain experts.
- Cost‑Benefit Prioritization – The identified high‑impact use‑cases (reporting & anomaly explanation) typically have low compute costs but high ROI, making them ideal first‑mile pilots.
- Cross‑Industry Playbook – Other sectors with similar data‑intensive workflows (manufacturing, transportation, utilities) can reuse the taxonomy to fast‑track their own GenAI pilots.
Limitations & Future Work
- Single‑company scope – Results stem from one energy provider, so findings may not generalize to smaller utilities or those with vastly different tech stacks.
- Short‑term horizon – The study captures early perceptions; long‑term adoption dynamics (e.g., model drift, governance evolution) remain unexamined.
- Tooling specificity – Participants discussed concepts rather than concrete platforms, leaving implementation details (model selection, latency constraints) open.
Future research could expand the sample to multiple energy firms, conduct longitudinal tracking of pilot outcomes, and benchmark different LLM architectures (open‑source vs. commercial) for the identified use‑cases.
Authors
- Malik Abdul Sami
- Zeeshan Rasheed
- Meri Olenius
- Muhammad Waseem
- Kai-Kristian Kemell
- Jussi Rasku
- Pekka Abrahamsson
Paper Information
- arXiv ID: 2602.09846v1
- Categories: cs.SE, cs.CY
- Published: February 10, 2026
- PDF: Download PDF