Nice — below is a compact, sourced market reference for the “AI in Energy” market with company references + values (where public) and the sections you requested. I pulled recent market reports, company results and industry press; the five most load-bearing facts are cited inline so you can follow up.

This versatile research report is presenting crucial details on market relevant information, harping on ample minute details encompassing a multi-dimensional market that collectively maneuver growth in the global AI in Energy market.

This holistic report presented by the report is also determined to cater to all the market specific information and a take on business analysis and key growth steering best industry practices that optimize million-dollar opportunities amidst staggering competition in AI in Energy market.

Read complete report at: https://www.thebrainyinsights.com/report/ai-in-energy-market-14726

Short note: most vendors do not break out “AI in energy” as a separate P&L line. I therefore give (A) published market-size estimates, (B) the relevant companies that show up repeatedly in industry reports, and (C) company-level revenue (or recent public metrics) as a scale proxy. Where possible I mark whether figures are company-wide (proxy) vs. AI-energy product lines (rare).

Market snapshot (size & growth — varying definitions)

• MarketsandMarkets: AI in energy ~USD 8.91 billion in 2024, projected to USD 58.66 billion by 2030 (CAGR ~36.9% — broad definition including software, services, platforms).

• Other published estimates (examples): Precedence Research: USD 18.1B (2025) → USD 75.5B by 2034 (CAGR ~17%). Coherent Market Insights estimates ≈USD 18.1B (2025) with ~17.4% CAGR to 2032. Different vendors use different scopes (device-only, software-only, energy-IT + services), so pick the scope you prefer. 

Leading companies (names that appear most across reports) — company-level values / notes

(These firms are the primary incumbents / platform providers / integrators in AI-for-energy solutions. Values shown are company-wide revenue or public metric — not AI-in-energy-only revenue.)

• Schneider Electric (France) — global energy-management & digital-energy leader (large AI/EMS programs; also acquired AutoGrid earlier and has extensive software). Revenue ~€38B (2024) (company FY results). Company-level revenue used as scale proxy. 

• Siemens / Siemens Energy (Germany) — strong in grid automation, digital twins and industrial AI. Siemens Group revenue €75.9B (FY2024); Siemens Energy separately reported ~€34.5B (FY2024).

• GE Vernova (US) — power/grid hardware + software and electrification software push; revenue ≈ USD 35B (2024) for GE Vernova. 

• ABB (Switzerland) — electrification & grid automation with AI-enabled control and asset-management offerings; revenue ≈ USD 32.9B (2024).

• Big Tech / Cloud (Microsoft, Google/Alphabet, AWS) — provide AI/ML platforms, cloud compute and energy-industry AI partnerships (Azure, Google Cloud). Example scale: Microsoft revenue ≈ USD 245B (FY2024); Alphabet ≈ USD 350B (2024) — these are company-wide and indicate the cloud scale supporting energy AI. 

• C3.ai (US) — pure-play AI platform firm with energy & oil & gas verticals; FY2025 revenue ≈ USD 389M (useful as an AI-software benchmark). 

• Notable specialist vendors / utilities & startups often named in vendor lists: AutoGrid / Uplight (VPP & DERMS), Bidgely (disaggregation & customer analytics), Grid4C (AI for grid & DER), Innowatts (load forecasting), Origami Energy (flexibility orchestration), SparkCognition, Uptake (asset-analytics). Many of these are private — revenue is rarely public but they appear repeatedly in market reports. 

Recent developments

• Rapid increase in deployments for grid-stability & DER orchestration (DERMS / VPP solutions) and many acquisitions/partnerships as incumbents buy or partner with specialists (example: AutoGrid → Uplight deal / Schneider activity in this space historically). 

• Utilities piloting generative-AI assistants and predictive-maintenance use cases to manage aging grids under climate stress and data-center electrification pressures. Utilities are cautious but accelerating trials.

Drivers

• Rapid electrification & renewables growth — variable supply/demand requires smarter forecasting and real-time dispatch.

• Need for grid flexibility (DER integration, VPPs) — AI improves forecasting, aggregation and market participation of distributed resources.

• Opex savings & predictive maintenance — AI reduces outages and maintenance costs for expensive grid assets. 

Restraints

• Data quality, legacy SCADA & siloed systems — utilities often lack modern, consistent data to train robust models. 

• Regulatory uncertainty & competition concerns (e.g., regulator scrutiny about algorithmic market behaviour / tacit collusion). UK regulator Ofgem and others are watching AI’s market impact. 

• Skills gap & integration complexity — utilities/asset owners struggle to hire ML/ops skills and integrate models into operations safely. 

Regional segmentation analysis

• North America: large share today (fast cloud adoption, many pilots, data-center grid stress use cases). MarketsandMarkets and others mark NA as leading. 

• Asia-Pacific: fast growth (APAC adoption driven by renewables & electrification; large energy asset buildouts). Precedence & other reports identify APAC as critical growth region.

• Europe: strong regulatory focus, innovation clusters (UK, Nordics) and utilities piloting AI — but careful on competition / consumer protections.

Emerging trends

• DER orchestration & VPPs scaling to commercial size (AI for bidding, scheduling and balancing).

• Generative-AI for operational assistants and knowledge-capture (field technicians, control-room augmentation).

• AI × digital twins for asset life-extension, planning and resilience modeling.

Top use cases

1. Load & renewable forecasting (short-term and day-ahead) to reduce imbalance costs.

2. DERMS / VPP orchestration and market participation (aggregation & bidding)

3. Predictive maintenance & asset health (transformers, turbines, solar inverters).

4. Customer energy analytics & demand response (disaggregation, personalized demand-response offers).

Major challenges

• Proving robust, repeatable business cases (payers want clear ROI and operational safety evidence).

• Regulatory & market-design barriers to letting AI-driven systems act autonomously in wholesale markets.

• Energy cost of large AI models — paradox where AI increases electricity demand; this creates sustainability tradeoffs and transparency pressure.

Attractive opportunities

• Grid-scale flexibility markets & monetization of DERs (platforms that successfully aggregate and bid DERs to markets).

• Cloud + edge AI platforms for utilities (managed AI stacks from Microsoft/AWS/Google tying to energy OT). 

• AI for resilience (climate risk forecasting & preventive hardening) where value is high and payers include governments and insurers. 

Key factors that will expand the market

1. Clear regulatory frameworks that enable (and safely limit) autonomous AI actions in energy markets.

2. Wider availability of clean, labeled grid & DER data and standard data-schemas (makes models reusable and lowers vendor lock-in). 

3. Scalable cloud & edge compute with lower carbon footprint (enables large-model use without unacceptable energy cost).

Quick next steps I can produce right away (pick one) — I’ll build it immediately:

1. Competitor table (CSV) — top 20 AI-in-energy companies (incumbents + specialists + cloud partners) with HQ, 2023–24 company revenue (proxy), and one-line description of AI-in-energy offering (I’ll attach sources).

2. 2-slide PPT — (slide 1) market snapshot + forecast range (pick MarketsandMarkets vs. Precedence as baseline); (slide 2) top 8 vendors with revenue proxies + 3 tactical recommendations for an energy utility or investor.

3. Source pack (PDF links) — the 10 most relevant reports and press releases I used (MarketsandMarkets, Grand View, Precedence, C3.ai FY25 release, Schneider FY24 results, Microsoft/Alphabet filings, BusinessInsider utility AI article, Ofgem coverage, AutoGrid/Uplight release).

Which one would you like me to generate now?