> For the complete documentation index, see [llms.txt](https://doc.duaer.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://doc.duaer.com/guides/energy-industry-llm-use-case-selection.md).

# How to Choose LLM Use Cases for the Energy Industry in 2027: Start with Value and Difficulty

Drawing on Duaer's documentation, this guide explains how energy companies can list candidate LLM use cases, rank them by value and difficulty, check whether the underlying data is usable, and select a first pilot with clear boundaries.

## The Question Is Not Whether to Use It, but Which Use Case Comes First

Over the past two years, almost every energy company has heard that large language models are coming to power plants and grids. Once a project is approved, though, the question quickly becomes concrete: in an industry this large, where should an LLM be used first? Many teams instinctively pick the use case that sounds most “intelligent,” such as automated dispatch or system-wide optimization. Six months later, the data pipelines still aren't connected, the security review hasn't passed, and there is nothing substantial to show.

What makes the energy sector different is its very high bar for safety and stability. Production data is scattered across SCADA, DCS, equipment asset registers, paper procedures, and years of maintenance records. An LLM can do many things, but the ones that actually run inside an organization are usually a small set of use cases with clearly defined boundaries. So the real question is not “Can energy companies use LLMs?” but “Which one should we use first?”

## Common LLM Use Cases in the Energy Industry

Listing the candidate use cases in full is the first step in selection. Based on typical energy business operations, they fall into a few broad categories.

The first category is Q&A on safety procedures and work permits. Frontline staff need to quickly look up procedure clauses, maintenance ticket requirements, and emergency plans. This content is mostly text, relatively standardized, and easy to keep under control: the model only provides reference sources and citations, while people confirm every critical action. The second category is an equipment maintenance knowledge assistant, which organizes historical defect records, inspection reports, and vendor manuals into a searchable knowledge base that helps engineers troubleshoot. The third category is summarization of inspection records and alarm tickets, for example compressing on-site inspection notes and shift handover logs into structured summaries to reduce the writing burden on operators. The fourth category is review of contracts, tenders, and procurement documents. New energy projects involve many contract clauses, which makes them well suited to clause comparison and risk flagging. The fifth category is first drafts of carbon emissions and environmental reports, where the framework is generated from ledger data and specialists verify each item.

Two categories offer high value but are not suitable as starting points. One is load, generation, and electricity price forecasting, which depends on time-series data and specialized modeling, so an LLM mainly serves to assist with interpretation. The other is real-time control and dispatch decisions, which carry the strictest compliance requirements, since any error can directly affect grid operations. They can stay on the candidate list, but they should not be tackled first.

## Rank by Value and Difficulty, and Verify Data Availability First

When ranking, it is best to score each candidate on two dimensions, from 1 to 5. Value depends on three things: how many hours of real work it saves each week, how much it reduces errors or missed findings, and how many people it reaches. Difficulty depends on four things: whether the required data already exists and can be accessed, how many systems must be integrated, whether the security classification requires changes to control links, and how many approval layers are involved.

A common misconception is that high security sensitivity automatically means high difficulty. A read-only procedure Q&A tool touches safety-related content, yet it does not require changes to any production system, so its approval path may actually be quite clear. The real difficulty usually comes from data and interfaces rather than from the word “safety” itself. When every candidate is plotted on a table using these two dimensions, the area of high value and low difficulty tends to become obvious quickly. That is the likely scope for the first pilot.

Energy companies generally do not lack data; they lack usable data. The same piece of equipment may have three different IDs across three systems. Historical defect records may consist largely of scanned files and handwritten notes. Legacy systems may have no open interfaces. Much of the expertise may exist only in the heads of veteran employees. If these issues are not checked during the selection stage, projects often discover mid-way that the necessary knowledge is not in any system at all.

A low-cost way to test this is to have business staff submit 20 real questions, such as “What are the common causes of overheating in a certain transformer model?” or “Which signatures does a certain type of work permit require?” Then check, for each question, whether the answer can be found in existing documents, who maintains those documents, and when they were last updated. Only use cases where answers can be found and someone maintains the source material are worth moving into a pilot. If answers cannot be found, the problem is not the model; it is that data governance needs to catch up first.

## How to Choose the First Pilot

The screening criteria for a first pilot can be simplified to six points: the business boundary is clear; there are defined users; results can be checked manually; it does not directly trigger production operations; the data scope can be closed within a single department; and someone is willing to give feedback every week. Use cases that meet these conditions are typically procedure Q&A, defect knowledge retrieval, or inspection log summarization. What they have in common is that the output is a “reference for people,” not a “decision made on their behalf.”

Once you have a candidate list and a scoring table, the next step is to place them somewhere that can be revised repeatedly and worked on by several people, recording each use case's data sources, owner, and scoring rationale. This can be done directly in Duaer: run through the process of listing use cases, scoring them, and choosing one pilot, validate with a small set of real documents, and then decide whether to expand. For more on how to rank use cases, see [How to Prioritize Enterprise AI Use Cases in 2027: Look at Value and Difficulty Before Choosing a Pilot](https://doc.duaer.com/guides/enterprise-ai-use-case-prioritization/).

## How to Measure Whether a Pilot Deserves Scaling, and Compliance Boundaries

Pilot metrics should be defined before launch, not after the fact when you go looking for justifications. At minimum, track five items: weekly active users and usage frequency; the share of answers accepted by frontline staff in sampled evaluations; average handling time for similar tasks, compared before and after using the same sample of tasks timed side by side; a log of errors and omissions, graded by severity; and safety-related incidents as a baseline metric, which must remain at zero.

It is especially important to remember that an answer that “looks reasonable” is not the same as an answer that “can be used.” In the energy sector, a plausible response that cites the wrong clause can carry a high cost. For this reason, the pilot should require every answer to include its source, and wrong answers should be archived separately as the basis for later improvement. If a use case shows low usage frequency and an error rate that stays high after two or three months, scope should be decisively reduced rather than forcing expansion into more systems.

Whichever use case is chosen, several boundaries should be written into the project charter: production data should remain within the company's domain as far as possible, with access permissions tiered by role; model output must not directly trigger any control command; all queries and answers should be logged to support later audits; and any content involving sensitive equipment or safety procedures must be confirmed by the relevant specialist department before updates. These requirements are not meant to slow the project down. They make it possible for the pilot results to be accepted by the security team and management, so the project can move to the next stage.

## One Thing You Can Do This Week

Form a small group of 8 to 10 people, including frontline team leaders, equipment engineers, IT staff, and safety managers. Each person submits two business questions they are asked most often, and these are consolidated into a candidate list of around 15 use cases. For each candidate, answer three questions: which document contains the answer and who maintains it; which systems would need to be connected; and how serious the impact would be if the answer were wrong. Finally, score the candidates by value and difficulty, select one as the first pilot, and write down its success metrics.

If you want to move this exercise directly into a shared working environment, you are welcome to sign up for a trial at [https://www.duaer.com](https://www.duaer.com) and validate with a small batch of your own procedure and defect documents before deciding how much to invest next.

LLM adoption in the energy industry is not about who has the most ambitious use case. It is about who chooses the first use case wisely: one with clear boundaries, results that can be checked, and room for continuous improvement.

## Questions

### How should an energy company choose its first LLM use case?

Usually, start with a use case that has clear boundaries, where results can be checked manually, and which does not directly trigger production operations, such as safety procedure Q&A, equipment defect knowledge retrieval, or inspection log summarization. When selecting, score candidates on value and difficulty, then consider whether the required data already exists and is accessible.

### Can a large language model directly take part in grid dispatch or equipment control?

It is not recommended as a starting point. Real-time control and dispatch decisions carry the strictest compliance requirements, and any error can directly affect grid operations. A safer approach is to have the model provide only reference sources and citations, keep people in charge of every critical action, and ensure the model output never directly triggers a control command.

### How can you tell whether an LLM pilot is worth scaling?

Define metrics before launch. At a minimum, track weekly active users and usage frequency, the share of answers frontline staff accept, before-and-after handling time for similar tasks, error records graded by severity, and safety-related incidents as a baseline. If usage stays low and the error rate cannot be brought down over time, narrow the scope promptly.