Tracking operations is a daily challenge for energy utilities
These reports then feed into the annual activity summaries that team leaders produce for clients. It’s a time-consuming task: “Depending on the technician’s workload, the quality of the reports can vary, sometimes detailed, sometimes more concise. Yet clients want documents that reflect as faithfully as possible the work performed,” explains Pierre Smith, Technical Account Manager at Orange Business. To meet this challenge, the energy company turned to generative AI to support its field teams and save time.
Objectives
Long-term support to set up and tailor the LLM and mobilize teams
Working side by side with its partner throughout the project, Orange Business immersed itself in the energy provider’s operations to better understand its challenges.
To feed the AI, Orange Business AI experts gathered data from 30,000 field service reports, supplemented by the energy provider’s customer service notes and operating procedures. The next step was essential data curation: isolating the most relevant information and grounding the model in an industry glossary. The team refined the prompt engineering (how instructions are phrased for the AI) and adjusted the model’s temperature (the parameter that controls output variability), enabling precise calibration of the LLM. This tuning delivers reliable answers while limiting “hallucinations,” that is, instances where the AI generates plausible but inaccurate information.
Thanks to a continuous improvement loop built on regular check-ins and feedback between the partners, the quality of the results steadily improved. “Beyond the purely technical aspects, change management plays a key role,” notes Pierre Smith. “Dialogue with technician teams and transparency about our AI-training approach helped bring employees on board and ease adoption of the LLM.”
A sovereign LLM to accelerate intervention report writing
The energy provider and Orange Business configured Orange Business’s generative AI software to automatically generate field service reports and then summarize them into annual activity reports.
The LLM integrates with the technician’s line-of-business application: the technician enters field data (worksite location, time on site, equipment inspected, etc.), and the AI then structures a client-ready report in real time. To do this, it draws on data from existing reports and on maintenance procedures specific to each piece of equipment. These reports are then sent to team leads, who consolidate them into comprehensive activity reports automatically generated by the LLM. Each customer site thus receives an annual report summarizing the service interventions performed and offering recommendations to ensure equipment upkeep. Simple on the surface, this AI conceals real complexity: the documents must be grounded in verifiable data, follow a logical structure, and be tailored to each customer. They also undergo systematic human validation. “For the proof of concept (PoC) of this tool as well as its scaling, we worked with LightOn’s Alfred LLM hosted on Cloud Avenue,” says Pierre Smith. “This Orange Business hosting solution ensures the security and sovereignty of sensitive data through French-based hosting.”
Tailored reports, generated in a flash
Key figures
10%
time freed up for team leads to focus on other tasks
Under 2 minutes
to generate a fieldservice report
30000
fieldservice reports analyzed
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