Industry · Power, gas, water and renewables O&M

Energy & utilities

Inspection, dispatch and safety Q&A — AI assists, people make safety calls.

Book an industry diagnostic →See the sample case

Pain points

What keeps owners up at night

01

Large volumes of scattered inspection records

02

Dispatch coordination by phone and chat groups

03

Many safety procedures, hard to look up and train

04

Manual logs and reports

Roles

Which roles AI transforms, and which it augments

“Transform”: AI executes, people spot-check. “Augment”: AI drafts and prompts, people decide.

RoleModeWhat AI does / what people keep
Field inspectorAugmentPhoto/voice to inspection logs; AI flags anomalies
DispatcherAugmentAI rolls up conditions and drafts suggestions; dispatchers decide
Safety trainerAugmentAI provides procedure Q&A and tests
Log & reporting clerkTransformLogs and reports generated automatically
Service hotlineAugmentAI handles standard enquiries and repair logging

Department agents

AI employees we typically deploy

Field Log AgentMonitoring AgentDispatch Assistant AgentSafety Q&A AgentReporting Agent

Safety actions, dispatch orders and equipment operations are always decided by certified staff. AI never executes safety or control actions automatically; deployments are private and follow MLPS and industry security requirements.

Super AI Workbench

Operations & safety board: the workbench for this industry

Demo UI with placeholder numbers.

BOOK.AI · Operations & safety boardDemo UI · placeholder numbers
  • Cockpit
  • AI employees
  • Workflows
  • Knowledge
  • Audit log
Revenue¥ ——
Labor cost¥ ——
Output / person——
Open exceptions——

AI task completion by department (illustrative)

Sales
Ops
Finance
HR
Supply

AI assistant · Monday briefing

  • Inspection completion and anomaly list
  • Dispatch tickets and response times
  • Safety training and test completion
  • Auto-generated logs and reports

Sample case

How we would approach this industry

Sample scenario · not a real client

A regional utility (sample)

This is a sample scenario that illustrates our approach in this industry — not a real client. All results are modeled per company during the diagnostic.

  1. 01 Discover (2–3 weeks): map the organization and break down roles such as “Field inspector” and “Dispatcher” into tasks and time share.
  2. 02 Build (90-day pilot): launch Field Log Agent and Monitoring Agent in parallel with the existing process.
  3. 03 Adopt & improve (months 4–12): extend to Dispatch Assistant Agent and Safety Q&A Agent; adjust headcount only after each pool meets its guardrails.
  • Change in execution labor cost
  • Change in core process hours
  • Modeled payback

These metrics are modeled with your company’s real data during the diagnostic.

FDE in practice

Public FDE practice in this industry

Third-party cases from public reports — unrelated to the BOOK.AI sample above — to show how this industry is approaching AI.

Case information is compiled from public reports; figures are as reported by the original sources, and all rights belong to their owners. BOOK.AI was not involved in any of these projects.

Image from the source page: State Grid Corporation of China
© People’s Daily; rights reserved by the owner · Image source

Public case · not a BOOK.AI clientEnergy & utilities · China

State Grid Corporation of China × Baidu AI Cloud

FDE intensity: MediumCo-build (joint development)

Manual tower climbs reduced by 40%; 5 million towers inspected a year

As reported by People’s Daily · 2025-06-04 · media report

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Background

Grid inspection is vast and climbing towers is risky; service enquiries are heavy.

How the FDE work was done

Built a “6541” layout centred on the Guangming power model with two-tier HQ–province compute centres.

Results (as reported by People’s Daily)

Covers HQ and 27 provincial companies; 5M towers inspected a year; manual climbs −40%; Q&A response rate 90%+.

Takeaways

HQ-to-province two-tier rollout is a model for large groups.

Provider
Baidu AI Cloud
Delivery mode
Co-build (joint development)
Region
China
Image from the source page: State Grid Corporation of China
© People’s Daily; rights reserved by the owner · Image source
Image from the source page: State Grid Corporation of China
© People’s Daily; rights reserved by the owner · Image source

Read the original ↗Original title (Chinese): 百度智能云携手能源行业头部企业 以大模型助推产业智能化升级

English summary by BOOK.AI; figures as reported by People’s Daily. Quotation marks indicate wording from the source.

Image from the source page: Longyuan Power (CHN Energy)
© People’s Daily; rights reserved by the owner · Image source

Public case · not a BOOK.AI clientEnergy & utilities · China

Longyuan Power (CHN Energy) × Baidu AI Cloud

FDE intensity: MediumCo-build (long-term)

Inspection efficiency up 6–10x across 200+ wind farms

As reported by People’s Daily · 2025-06-04 · media report

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Background

Remote, scattered renewable sites made manual inspection slow.

How the FDE work was done

Built a three-tier video-recognition system around site needs as an extensible operations platform.

Results (as reported by People’s Daily)

Model accuracy 95%+; response from hours to minutes; inspection efficiency 6–10x.

Takeaways

Long-term partnership and steady iteration is what energy clients value.

Provider
Baidu AI Cloud
Delivery mode
Co-build (long-term)
Region
China
Image from the source page: Longyuan Power (CHN Energy)
© People’s Daily; rights reserved by the owner · Image source
Image from the source page: Longyuan Power (CHN Energy)
© People’s Daily; rights reserved by the owner · Image source

Read the original ↗Original title (Chinese): 百度智能云携手能源行业头部企业 以大模型助推产业智能化升级

English summary by BOOK.AI; figures as reported by People’s Daily. Quotation marks indicate wording from the source.

Screenshot of the source page (captured 2026-10-19): bp
© Palantir (page screenshot; content © source) · Image source

Public case · not a BOOK.AI clientEnergy & utilities · UK

bp × Palantir

FDE intensity: MediumFDE / 5-year strategic partnership

No figures disclosed (“model-based digital twin of bp’s oil and gas production activity”)

As reported by Palantir · 2024-09 · provider-published

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Background

Complex production decisions need a trusted data foundation and transparent AI advice.

How the FDE work was done

Added AIP on top of a digital twin, with tooling to keep AI recommendations transparent and traceable for human decisions.

Results (as reported by Palantir)

No quantified results disclosed.

Takeaways

Digital twin plus AI advice plus human decisions suits high-risk industries.

Provider
Palantir
Delivery mode
FDE / 5-year strategic partnership
Region
UK
Screenshot of the source page (captured 2026-10-19): bp
© Palantir (page screenshot; content © source) · Image source

Read the original ↗Palantir IR - News

English summary by BOOK.AI; figures as reported by Palantir. Quotation marks indicate wording from the source.

Public case · not a BOOK.AI clientEnergy & utilities · China

China National Petroleum Corporation (CNPC) × CNPC (with R&D partners, provider not listed)

FDE intensity: LightIn-house team / joint R&D

123 scenarios; 23 digital employees; inversion efficiency 10x+ (as reported)

As reported by CNPC News Center · 2025-12-29 · company statement

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Background

Heavy computation in exploration and large volumes of official documents.

How the FDE work was done

On the Kunlun model foundation, the group launched digital employees for exploration, refining and office work.

Results (as reported by CNPC News Center)

123 scenarios, 23 digital employees, inversion efficiency 10x+, document quality +20%.

Takeaways

A scale model for SOE energy: build the foundation, then deploy digital employees by business line.

Provider
CNPC (with R&D partners, provider not listed)
Delivery mode
In-house team / joint R&D
Region
China

Read the original ↗Original title (Chinese): 中国石油新闻中心：昆仑大模型应用报道

English summary by BOOK.AI; figures as reported by CNPC News Center. Quotation marks indicate wording from the source.

Screenshot of the source page (captured 2026-10-19): CHN Energy
© National Energy Administration (page screenshot; content © source) · Image source

Public case · not a BOOK.AI clientEnergy & utilities · China

CHN Energy × In-house R&D with universities and institutes

FDE intensity: LightIn-house team / joint R&D

13 scenarios, 41 agents; violation alerts in 15 seconds

As reported by National Energy Administration · 2025-07-04 · government site

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Background

High safety risk, hard trading decisions and reactive maintenance in power generation.

How the FDE work was done

Rolled out in layers with standardised datasets; real-time feedback and expert input drive monthly model iterations.

Results (as reported by National Energy Administration)

Technical-supervision efficiency +100%+; violation alerts within 15 s; 85% visual accuracy.

Takeaways

Expert input and monthly iteration keep models close to the business.

Provider
In-house R&D with universities and institutes
Delivery mode
In-house team / joint R&D
Region
China
Screenshot of the source page (captured 2026-10-19): CHN Energy
© National Energy Administration (page screenshot; content © source) · Image source

Read the original ↗Original title (Chinese): 国家能源集团发布全球首个千亿级发电行业大模型“擎源” 驱动发电行业迈向智能化新阶段---国家能源局

English summary by BOOK.AI; figures as reported by National Energy Administration. Quotation marks indicate wording from the source.

See public FDE practice across all 14 industries →

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