Predictive maintenance and reporting automation lead AI adoption in energy industry

Energy companies are looking for smarter ways to streamline reporting, improve forecasting and manage fragmented data systems. Generative AI is moving beyond experimentation into structured enterprise initiatives aimed at solving concrete operational bottlenecks.

In the study Generative AI Adoption in an Energy Company: Exploring Challenges and Use Cases, submitted in arXiv, researchers present a multi-department case study detailing how one Nordic energy company prioritized AI-driven reporting automation, predictive maintenance and retrieval-augmented workflows.

From experimentation to operational use cases

Generative AI has quickly moved from experimental tool to strategic priority across industries. However, most existing research focuses on technical performance or individual productivity gains, offering limited visibility into how companies actually integrate AI into everyday workflows. The Tampere University team sought to address that gap through a qualitative, multi-department case study embedded within a mid-sized Nordic energy company.

Over a four-week on-site period in early 2025, researchers conducted 16 semi-structured group interviews across nine organizational functions, including customer operations, infrastructure management, data integration, financial planning, energy trading, workforce planning, strategic development and business feasibility analysis. Participants were primarily senior professionals with extensive industry experience, providing cross-functional and leadership-level perspectives.

The company operates across energy trading, heating, solar and wind power, integrating more than 100 digital platforms and serving thousands of customers. Its distributed cloud-based infrastructure presented both opportunities and constraints for AI adoption. Management had expressed interest in identifying feasible generative AI applications while understanding the organizational challenges that might shape implementation.

Through thematic analysis of interviews, internal documents and field observations, the researchers identified 41 AI-related use cases. These were consolidated into six major categories: reporting, retrieval-augmented generation and agentic solutions, predictive maintenance, anomaly detection, budgeting and forecasting, and department-specific cases.

Three cross-departmental priorities emerged clearly: automation of reporting tasks, predictive maintenance to reduce downtime, and enhanced forecasting capabilities for strategic planning.

Reporting, forecasting and data fragmentation as key entry points

Manual and repetitive reporting surfaced as the most pressing operational burden. Employees described extensive time spent preparing financial reports, trading summaries, technical network documentation and compliance records. Many of these processes relied on repeated data validation and spreadsheet-based reconciliation.

Reporting use cases were ultimately assigned the highest priority in the company's internal assessment because they directly support board-level decision-making. Automating data retrieval, document generation and cross-system reconciliation was seen as a practical starting point for generative AI adoption.

Forecasting and predictive analytics formed the second major cluster of needs. Units involved in long-term infrastructure planning and energy trading described reliance on scenario modeling extending years into the future. Participants emphasized the importance of improving demand forecasts, revenue planning, risk assessment and asset lifecycle projections. Predictive maintenance was particularly relevant for electricity distribution networks, where early fault detection can prevent service disruptions and manage cost exposure.

Data fragmentation represented another recurring theme. With more than 100 interconnected systems, the organization faced persistent challenges in integrating data flows and monitoring errors. Manual oversight of data ingestion pipelines and activity logs consumed substantial time. Participants indicated that AI-driven monitoring and automated categorization of system errors could reduce workload while improving reliability.

Compliance and validation tasks also featured prominently. Invoice verification, fraud detection, billing validation and anomaly monitoring required structured review of large volumes of data. Employees viewed AI-assisted anomaly detection as a way to reduce manual checking while maintaining human oversight.

Across these categories, the research found that employees favored incremental integration. Rather than replacing existing systems, participants expected AI tools to operate as assistive layers embedded within current workflows. This incremental mindset reflected concerns about reliability, governance and organizational readiness.

Five organizational themes shape AI adoption

The researchers synthesized 125 unique codes from interview transcripts into five overarching themes that characterize AI adoption dynamics within the company.

The first theme, manual and repetitive work, captured the widespread expectation that AI could automate verification, reporting and routine validation tasks. These were seen as realistic early wins.

The second theme, forecasting and predictive analytics, reflected shared interest in strengthening data-driven planning across multi-year horizons. Participants linked AI models to improved scenario planning and proactive maintenance strategies.

The third theme, data fragmentation and integration, highlighted structural barriers. Disconnected systems and siloed datasets limit scalability of AI initiatives unless addressed through unified data access mechanisms.

The fourth theme, compliance and validation, underscored the regulatory demands faced by energy firms. AI-driven anomaly detection and risk surveillance were viewed as tools to enhance operational stability and pricing decisions.

The fifth theme, organizational and infrastructure readiness, captured employee expectations around gradual implementation. Staff emphasized that AI tools should suggest and assist rather than operate autonomously without supervision.

Two pilot systems demonstrate practical feasibility

To move beyond conceptual mapping, the research team developed two pilot systems designed to demonstrate practical applicability within existing workflows.

The first pilot, described as an intelligent email clone system, addressed high volumes of customer communication handled manually by staff. Using a retrieval-augmented generation architecture, the system embedded historical email data and internal documentation into a secure vector database. Upon receiving a new email, agentic frameworks retrieved relevant context and generated draft responses aligned with the company's communication style.

Human-in-the-loop supervision was central to the design. Generated drafts were reviewed and edited by employees before approval, ensuring accountability and compliance. Performance evaluation using BERTScore indicated an 89 percent semantic alignment between generated responses and reference emails, suggesting strong contextual accuracy.

The second pilot focused on autonomous text and data retrieval. Employees frequently struggled to locate documents across distributed repositories. The RAG-based chatbot system enabled natural language queries, retrieving relevant files and generating context-aware summaries. By reducing manual search efforts, the system aimed to enhance knowledge accessibility and operational efficiency.

Neither pilot was deployed into production, but both served as proof-of-concept demonstrations illustrating how generative AI and agentic workflows could integrate with existing infrastructure.

Implications for industry and research

Unlike previous work focused on software development environments, this case highlights requirements-intensive challenges in a data-heavy energy utility context.

For practitioners, the findings suggest that successful adoption depends less on technical sophistication and more on alignment with cross-departmental priorities. Reporting automation, predictive maintenance and forecasting offer shared value across units, increasing the likelihood of internal support.

For researchers, the study identifies three future directions. First, longitudinal analysis is needed to examine how identified use cases evolve into deployed systems. Second, cross-sector comparisons could determine which adoption barriers are specific to energy utilities and which are common across industries. Third, replication across multiple organizations would strengthen generalizability.

The authors acknowledge limitations. As a single-company case study, findings may not generalize universally. Some interviews were not audio-recorded due to organizational constraints, potentially affecting transcript richness. Nonetheless, data triangulation through internal documents and field observations strengthened validity.