AI in Energy and Utilities Market Size and Revenue Impact Study 2026 to 2035

Report Id: 2747 Pages: 170 Last Updated: 30 January 2026 Format: PDF / PPT / Excel / Power BI
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Segmentation of AI in Energy and Utilities Market :

AI in Energy and Utilities Market By Type-

  • Machine Learning
  • Natural Language Processing
  • Computer Vision
  • Predictive Analytics
  • Deep Learning
  • Others

AI in Energy and Utilities Market

AI in Energy and Utilities Market By Application-

  • Energy Management and Optimization
  • Grid Management
  • Demand Forecasting
  • Equipment Maintenance and Monitoring
  • Smart Metering
  • Renewable Energy Integration
  • Customer Service and Engagement
  • Fraud Detection
  • Energy Trading and Pricing
  • Others

AI in Energy and Utilities Market By, End-User-

  • Power Generation Companies
  • Utility Companies
  • Oil and Gas Companies
  • Renewable Energy Providers
  • Energy Service Providers
  • Government and Regulatory Bodies
  • Others

AI in Energy and Utilities Market, By Region-

  • North America-
    • The US
    • Canada
  • Europe-
    • Germany
    • The UK
    • France
    • Italy
    • Spain
    • Rest of Europe
  • Asia-Pacific-
    • China
    • Japan
    • India
    • South Korea
    • South East Asia
    • Rest of Asia Pacific
  • Latin America-
    • Brazil
    • Argentina
    • Mexico
    • Rest of Latin America
  •  Middle East & Africa-
    • GCC Countries
    • South Africa
    • Rest of Middle East and Africa

Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions

Chapter 2. Executive Summary

Chapter 3. Global AI in Energy and Utilities Market Snapshot

Chapter 4. Global AI in Energy and Utilities Market Variables, Trends & Scope
4.1. Market Segmentation & Scope
4.2. Drivers
4.3. Challenges
4.4. Trends
4.5. Investment and Funding Analysis 
4.6. Industry Analysis – Porter’s Five Forces Analysis
4.7. Competitive Landscape & Market Share Analysis
4.8. Impact of Covid-19 Analysis

Chapter 5. Market Segmentation 1: by Type Estimates & Trend Analysis
5.1. by Type & Market Share, 2025 & 2035
5.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2022 to 2035 for the following by Type:

5.2.1. Machine Learning
5.2.2. Natural Language Processing
5.2.3. Computer Vision
5.2.4. Predictive Analytics
5.2.5. Deep Learning
5.2.6. Others

Chapter 6. Market Segmentation 2: by Application Estimates & Trend Analysis
6.1. by Application & Market Share, 2025 & 2035
6.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2022 to 2035 for the following by Application:

6.2.1. Energy Management and Optimization
6.2.2. Grid Management
6.2.3. Demand Forecasting
6.2.4. Equipment Maintenance and Monitoring
6.2.5. Smart Metering
6.2.6. Renewable Energy Integration
6.2.7. Customer Service and Engagement
6.2.8. Fraud Detection
6.2.9. Energy Trading and Pricing
6.2.10. Others

Chapter 7. Market Segmentation 3: by End-User Estimates & Trend Analysis
7.1. by End-User & Market Share, 2025 & 2035
7.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2022 to 2035 for the following by End-User:

7.2.1. Power Generation Companies
7.2.2. Utility Companies
7.2.3. Oil and Gas Companies
7.2.4. Renewable Energy Providers
7.2.5. Energy Service Providers
7.2.6. Government and Regulatory Bodies
7.2.7. Others

Chapter 8. AI in Energy and Utilities Market Segmentation 4: Regional Estimates & Trend Analysis
8.1. North America

8.1.1. North America AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by Type, 2022-2035
8.1.2. North America AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by Application, 2022-2035
8.1.3. North America AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by End-User, 2022-2035
8.1.4. North America AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by country, 2022-2035

8.2. Europe

8.2.1. Europe AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by Type, 2022-2035
8.2.2. Europe AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by Application, 2022-2035
8.2.3. Europe AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by End-User, 2022-2035
8.2.4. Europe AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by country, 2022-2035

8.3. Asia Pacific

8.3.1. Asia Pacific AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by Type, 2022-2035
8.3.2. Asia Pacific AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by Application, 2022-2035
8.3.3. Asia-Pacific AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by End-User, 2022-2035
8.3.4. Asia Pacific AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by country, 2022-2035

8.4. Latin America

8.4.1. Latin America AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by Type, 2022-2035
8.4.2. Latin America AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by Application, 2022-2035
8.4.3. Latin America AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by End-User, 2022-2035
8.4.4. Latin America AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by country, 2022-2035

8.5. Middle East & Africa

8.5.1. Middle East & Africa AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by Type, 2022-2035
8.5.2. Middle East & Africa AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by Application, 2022-2035
8.5.3. Middle East & Africa AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by End-User, 2022-2035
8.5.4. Middle East & Africa AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by country, 2022-2035

Chapter 9. Competitive Landscape
9.1. Major Mergers and Acquisitions/Strategic Alliances
9.2. Company Profiles

9.2.1. Siemens AG
9.2.2. General Electric Company
9.2.3. International Business Machines Corporation (IBM)
9.2.4. Schneider Electric SE
9.2.5. ABB Ltd
9.2.6. Microsoft Corporation
9.2.7. Oracle Corporation
9.2.8. Honeywell International Inc.
9.2.9. Cisco Systems, Inc.
9.2.10. SAS Institute Inc.
9.2.11. Intel Corporation
9.2.12. Siemens Energy AG
9.2.13. Enel X S.r.l.
9.2.14. C3.ai, Inc.
9.2.15. Tesla, Inc.
9.2.16. Google LLC (subsidiary of Alphabet Inc.)
9.2.17. Engie SA
9.2.18. Accenture plc
9.2.19. Hitachi, Ltd.
9.2.20. Vestas Wind Systems A/S
9.2.21. Wärtsilä Oyj Abp
9.2.22. Électricité de France (EDF)
9.2.23. Shell plc
9.2.24. Nvidia Corporation
9.2.25. Eaton Corporation plc
9.2.26. Other Market Players

Research Design and Approach

This study employed a multi-step, mixed-method research approach that integrates:

  • Secondary research
  • Primary research
  • Data triangulation
  • Hybrid top-down and bottom-up modelling
  • Forecasting and scenario analysis

This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.

Secondary Research

Secondary research for this study involved the collection, review, and analysis of publicly available and paid data sources to build the initial fact base, understand historical market behaviour, identify data gaps, and refine the hypotheses for primary research.

Sources Consulted

Secondary data for the market study was gathered from multiple credible sources, including:

  • Government databases, regulatory bodies, and public institutions
  • International organizations (WHO, OECD, IMF, World Bank, etc.)
  • Commercial and paid databases
  • Industry associations, trade publications, and technical journals
  • Company annual reports, investor presentations, press releases, and SEC filings
  • Academic research papers, patents, and scientific literature
  • Previous market research publications and syndicated reports

These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.

Secondary Research

Primary Research

Primary research was conducted to validate secondary data, understand real-time market dynamics, capture price points and adoption trends, and verify the assumptions used in the market modelling.

Stakeholders Interviewed

Primary interviews for this study involved:

  • Manufacturers and suppliers in the market value chain
  • Distributors, channel partners, and integrators
  • End-users / customers (e.g., hospitals, labs, enterprises, consumers, etc., depending on the market)
  • Industry experts, technology specialists, consultants, and regulatory professionals
  • Senior executives (CEOs, CTOs, VPs, Directors) and product managers

Interview Process

Interviews were conducted via:

  • Structured and semi-structured questionnaires
  • Telephonic and video interactions
  • Email correspondences
  • Expert consultation sessions

Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.

Data Processing, Normalization, and Validation

All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.

The data validation process included:

  • Standardization of units (currency conversions, volume units, inflation adjustments)
  • Cross-verification of data points across multiple secondary sources
  • Normalization of inconsistent datasets
  • Identification and resolution of data gaps
  • Outlier detection and removal through algorithmic and manual checks
  • Plausibility and coherence checks across segments and geographies

This ensured that the dataset used for modelling was clean, robust, and reliable.

Market Size Estimation and Data Triangulation

Bottom-Up Approach

The bottom-up approach involved aggregating segment-level data, such as:

  • Company revenues
  • Product-level sales
  • Installed base/usage volumes
  • Adoption and penetration rates
  • Pricing analysis

This method was primarily used when detailed micro-level market data were available.

Bottom Up Approach

Top-Down Approach

The top-down approach used macro-level indicators:

  • Parent market benchmarks
  • Global/regional industry trends
  • Economic indicators (GDP, demographics, spending patterns)
  • Penetration and usage ratios

This approach was used for segments where granular data were limited or inconsistent.

Hybrid Triangulation Approach

To ensure accuracy, a triangulated hybrid model was used. This included:

  • Reconciling top-down and bottom-up estimates
  • Cross-checking revenues, volumes, and pricing assumptions
  • Incorporating expert insights to validate segment splits and adoption rates

This multi-angle validation yielded the final market size.

Forecasting Framework and Scenario Modelling

Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.

Forecasting Methods

  • Time-series modelling
  • S-curve and diffusion models (for emerging technologies)
  • Driver-based forecasting (GDP, disposable income, adoption rates, regulatory changes)
  • Price elasticity models
  • Market maturity and lifecycle-based projections

Scenario Analysis

Given inherent uncertainties, three scenarios were constructed:

  • Base-Case Scenario: Expected trajectory under current conditions
  • Optimistic Scenario: High adoption, favourable regulation, strong economic tailwinds
  • Conservative Scenario: Slow adoption, regulatory delays, economic constraints

Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.

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Frequently Asked Questions

AI in Energy and Utilities Market Size is valued at USD 15.23 Billion in 2025 and is predicted to reach USD 93.29 Billion by the year 2035

AI in Energy and Utilities Market is expected to grow at a 20.0% CAGR during the forecast period for 2026 to 2035.

Siemens AG, General Electric Company, International Business Machines Corporation (IBM), Schneider Electric SE, ABB Ltd, Microsoft Corporation, Oracle Corporation, Honeywell International Inc., Cisco Systems, Inc., SAS Institute Inc., Intel Corporation, Siemens Energy AG, Enel X S.r.l., C3.ai, Inc., Tesla, Inc., Google LLC (subsidiary of Alphabet Inc.), Engie SA, Accenture plc, Hitachi, Ltd., Vestas Wind Systems A/S, Wärtsilä Oyj Abp, Électricité de France (EDF), Shell plc, and Nvidia Corporation, and Eaton Corporation plc. and Others.

AI in Energy and Utilities Market is segmented in Type (Machine Learning, Natural Language Processing, Computer Vision, Predictive Analytics, Deep Learning, Others), Application, End-User and Other.

North America region is leading the AI in Energy and Utilities Market

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