세계의 에너지 분야 인공지능(AI) 시장 : 제공 제품별, 에너지 유형별, 유형별, 용도별, 최종 용도별, 지역별 - 예측(-2030년)

The AI in energy market is estimated at USD 8.91 billion in 2024 to USD 58.66 billion by 2030, at a Compound Annual Growth Rate (CAGR) of 36.9%. AI-based methods and ML techniques are expected to help buildings run more efficiently and provide greater comfort levels to occupants. Buildings and HVAC systems have been designed, constructed, and commissioned as fixed systems and with static environmental assumptions. This can lead to inefficiencies because building use, occupancy, and environmental factors change over time. AI can be applied to parse data collected by building systems and integrate with controls to continuously adjust setpoints to optimize HVAC performance while maintaining or improving occupant comfort. AI-based methods can provide additional. Controls to the operators, enabling increased load flexibility of buildings for participation in Virtual Power Plants (VPPs).

"By energy type, conventional energy segment to hold the largest market size during the forecast period."

Artificial intelligence is increasingly being integrated into the more traditional energy sectors such as coal, oil, natural gas, and nuclear energy to make it much more efficient, safe, and sustainable. In fossil fuel-based energy generation, AI optimizes resource extraction, improves plant performance, and enables predictive maintenance that reduces downtime and operational costs. Using coal, oil, and natural gas, AI systems can forecast demand fluctuations, adjust supply levels, and monitor emissions, helping operators comply with environmental regulations. With nuclear energy, AI ensures safety by monitoring reactor conditions and predicting anomalies while automating response mechanisms, hence increasing the overall plant reliability. In addition, AI use supports the development of better extracting processes and fewer operational risks in other conventional energy sources, such as peat, oil shale, and tar sands, toward sustainability in energy production. In doing so, AI is redefining the conventional energy landscape, ensuring it is more efficient, safe, and environmentally friendly.

"The services segment to register the fastest growth rate during the forecast period."

In the AI-driven energy sector, services such as training, consulting, deploying, integrating systems, supporting, and maintenance are critical for operation optimization in generation, distribution, and consumption across an entire power system. Professional services aid energy companies in identifying specific needs using AI solutions, with potential expertise in grid optimization, energy forecasting, and smart grid management. Deployment and integration services guarantee the smooth integration of AI systems with existing energy infrastructures. Support and maintenance ensure that the AI-powered solutions stay up and running with swift troubleshooting and updates, ensuring maximum uptime. Managed services allow energy companies to step back from AI solutions, as external providers handle them to improve efficiency and minimize operational costs. Together, these services empower energy organizations to use AI technologies holistically to drive operational excellence and innovation across the value chain.

"Asia Pacific to hold the highest market growth rate during the forecast period."

In October 2023, BluWave-ai expanded its business in the Japanese market using AI-driven energy optimization technology. BluWave-ai introduced its technology from global AI deployments to enable the energy transition in Japan by optimizing energy at industrial grid-attached plants with solar generation and battery storage. It partnered with Japanese engineering companies and completed a project at an industrial R&D center. The work included optimization of rooftop solar, battery storage, and biomass generation systems. The Smart Grid Optimizer did some incredible feats such as 20% peak demand reduction, 100% utilization of renewable energy without reverse power flow and significant savings in energy costs. By November 2024, ZTE Corporation and China Mobile developed an AI-driven Green Telco Cloud that dynamically adjusts computing resources using load-based network adjustments toward making energy use in telecommunications networks optimal. In China in November 2024, ZTE Corporation and China Mobile developed an AI-driven Green Telco Cloud that makes energy use in telecommunications networks optimal with load-based network adjustments dynamically adjusting computing resources.

In-depth interviews have been conducted with chief executive officers (CEOs), Directors, and other executives from various key organizations operating in the AI in energy market.

• By Company Type: Tier 1 - 40%, Tier 2 - 35%, and Tier 3 - 25%
• By Designation: Directors -25%, Managers - 35%, and Others - 40%
• By Region: North America - 37%, Europe - 42%, Asia Pacific - 21

The major players in the AI in energy market include Schneider Electric SE (France), GE Vernova (US), ABB Ltd (Switzerland), Honeywell International (US), Siemens AG (Germany), AWS (US), IBM (US), Microsoft (US), Oracle (US), Vestas Wind Systems A/S (Denmark), Atos zData (US), C3.ai (US), Tesla (US), Alpiq (Switzerland), Enel group (Italy), Origami Energy (UK), Innowatts (US), Irasus technologies (India), Grid4C (US), Uplight (US), GridBeyond (Ireland), eSmart Systems (Norway), Ndustrial (US), Datategy (France), Omdena (US). These players have adopted various growth strategies, such as partnerships, agreements and collaborations, new product launches, enhancements, and acquisitions to expand their AI in energy market footprint.

Research Coverage

The market study covers the AI in energy market size across different segments. It aims at estimating the market size and the growth potential across various segments, including by offering (solutions and services (professional services, managed services) by energy type (conventional energy (fossil fuels, nuclear energy, other conventional energy types) renewable energy (solar, wind, hydropower, biomass, other renewable energy types) by type (Generative AI, other AI), by application (energy demand forecasting, grid optimization & management, energy storage optimization , renewables integration , energy trading & market forecasting, energy sustainability management, disaster resilience and recovery, other applications (energy theft detection and customer management)) by end use (generation, transmission , distribution, consumption(commercial, industrial)) and Region (North America, Europe, Asia Pacific, Middle East & Africa, and Latin America). The study includes an in-depth competitive analysis of the leading market players, their company profiles, key observations related to product and business offerings, recent developments, and market strategies.

Key Benefits of Buying the Report

The report will help the market leaders/new entrants with information on the closest approximations of the global AI in energy market's revenue numbers and subsegments. This report will help stakeholders understand the competitive landscape and gain more insights to position their businesses better and plan suitable go-to-market strategies. Moreover, the report will provide insights for stakeholders to understand the market's pulse and provide them with information on key market drivers, restraints, challenges, and opportunities.

The report provides insights on the following pointers:

Analysis of key drivers (energy market volatility and risk management, rising consumer demand for smart energy solutions, AI-Powered robots increasing energy sector worker safety), restraints (data privacy and security, high implementation cost) opportunities (increasing shift towards carbon emission reduction and sustainability, renewable energy integration), and challenges (insufficient real-time energy data limiting the training and deployment of AI models, lack of skilled professionals in AI and energy analytics.) influencing the growth of the AI in energy market.

Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the AI in energy market.

Market Development: The report provides comprehensive information about lucrative markets and analyses the AI in energy market across various regions.

Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in the AI in energy market.

Competitive Assessment: In-depth assessment of market shares, growth strategies and service offerings of leading include include Schneider Electric SE (France), GE Vernova (US), ABB Ltd (Switzerland), Honeywell International (US), Siemens AG (Germany), AWS (US), IBM (US), Microsoft (US), Oracle (US), Vestas Wind Systems A/S (Denmark), Atos zData (US), C3.ai (US), Tesla (US), Alpiq (Switzerland), Enel group (Italy), Origami Energy (UK), Innowatts (US), Irasus technologies (India), Grid4C (US), Uplight (US), GridBeyond (Ireland), eSmart Systems (Norway), Ndustrial (US), Datategy (France), Omdena (US).

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 STUDY OBJECTIVES
• 1.2 MARKET DEFINITION
• 1.3 STUDY SCOPE
  • 1.3.1 MARKET SEGMENTATION
  • 1.3.2 INCLUSIONS AND EXCLUSIONS
• 1.4 YEARS CONSIDERED
• 1.5 CURRENCY CONSIDERED
• 1.6 STAKEHOLDERS

2 RESEARCH METHODOLOGY

• 2.1 RESEARCH DATA
  • 2.1.1 SECONDARY DATA
  • 2.1.2 PRIMARY DATA
    • 2.1.2.1 Primary interviews with experts
    • 2.1.2.2 Breakdown of primary profiles
    • 2.1.2.3 Key insights from industry experts
• 2.2 MARKET SIZE ESTIMATION
  • 2.2.1 TOP-DOWN APPROACH
  • 2.2.2 BOTTOM-UP APPROACH
  • 2.2.3 AI IN ENERGY MARKET ESTIMATION: DEMAND-SIDE ANALYSIS
• 2.3 DATA TRIANGULATION
• 2.4 LIMITATIONS AND RISK ASSESSMENT
• 2.5 RESEARCH ASSUMPTIONS
• 2.6 RESEARCH LIMITATIONS

3 EXECUTIVE SUMMARY

4 PREMIUM INSIGHTS

• 4.1 OPPORTUNITIES FOR KEY PLAYERS IN AI IN ENERGY MARKET
• 4.2 AI IN ENERGY MARKET, BY OFFERING
• 4.3 AI IN ENERGY MARKET, BY SERVICE
• 4.4 AI IN ENERGY MARKET, BY PROFESSIONAL SERVICE
• 4.5 AI IN ENERGY MARKET, BY APPLICATION
• 4.6 AI IN ENERGY MARKET, BY ENERGY TYPE
• 4.7 AI IN ENERGY MARKET, BY END USE
• 4.8 AI IN ENERGY MARKET, BY TYPE
• 4.9 NORTH AMERICA: AI IN ENERGY MARKET, BY OFFERING AND END USE

5 MARKET OVERVIEW AND INDUSTRY TRENDS

• 5.1 INTRODUCTION
• 5.2 MARKET DYNAMICS
  • 5.2.1 DRIVERS
    • 5.2.1.1 Energy market volatility and risk management
    • 5.2.1.2 Rising consumer demand for smart energy solutions
    • 5.2.1.3 AI-powered robots increasing energy sector worker safety
  • 5.2.2 RESTRAINTS
    • 5.2.2.1 Data privacy and security
    • 5.2.2.2 High implementation costs
  • 5.2.3 OPPORTUNITIES
    • 5.2.3.1 Increasing shift toward carbon emission reduction and sustainability
    • 5.2.3.2 Renewable energy integration
  • 5.2.4 CHALLENGES
    • 5.2.4.1 Insufficient real-time energy data limiting training and deployment of AI models
    • 5.2.4.2 Lack of skilled professionals in AI and energy analytics
• 5.3 BRIEF HISTORY OF AI IN ENERGY MARKET
• 5.4 ECOSYSTEM ANALYSIS
• 5.5 CASE STUDY ANALYSIS
  • 5.5.1 OPTIMIZING ENERGY EFFICIENCY ACROSS PORTFOLIOS: BLACKSTONE'S STRATEGIC PARTNERSHIP WITH SCHNEIDER ELECTRIC
  • 5.5.2 C3 AI ENERGY MANAGEMENT PLATFORM HELPED LEADING PETROCHEMICAL COMPANY BOOST ENERGY EFFICIENCY AND ENVIRONMENTAL PERFORMANCE
  • 5.5.3 ENVERUS INSTANT ANALYST ENABLED ENERGY COMPANIES IMPROVE DECISION-MAKING AND OPERATIONAL EFFICIENCY
  • 5.5.4 AI-POWERED MICROGRIDS FACILITATED ENERGY RESILIENCE AND EQUITY IN REGIONAL COMMUNITIES
  • 5.5.5 C3 AI ENERGY MANAGEMENT PLATFORM HELPED LEADING STEEL MANUFACTURER GAIN SUBSTANTIAL COST SAVINGS AND OPERATIONAL IMPROVEMENTS
• 5.6 SUPPLY CHAIN ANALYSIS
• 5.7 TARIFF AND REGULATORY LANDSCAPE
  • 5.7.1 TARIFF RELATED TO PROCESSORS AND CONTROLLERS (HSN: 854231)
  • 5.7.2 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
  • 5.7.3 KEY REGULATIONS: AI IN ENERGY
    • 5.7.3.1 North America
      • 5.7.3.1.1 SCR 17: Artificial Intelligence Bill (California)
      • 5.7.3.1.2 S1103: Artificial Intelligence Automated Decision Bill (Connecticut)
      • 5.7.3.1.3 National Artificial Intelligence Initiative Act (NAIIA)
      • 5.7.3.1.4 The Artificial Intelligence and Data Act (AIDA) - Canada
    • 5.7.3.2 Europe
      • 5.7.3.2.1 European Union (EU) - Artificial Intelligence Act (AIA)
      • 5.7.3.2.2 General Data Protection Regulation (Europe)
    • 5.7.3.3 Asia Pacific
      • 5.7.3.3.1 Interim Administrative Measures for Generative Artificial Intelligence Services (China)
      • 5.7.3.3.2 National AI Strategy (Singapore)
      • 5.7.3.3.3 Hiroshima AI Process Comprehensive Policy Framework (Japan)
    • 5.7.3.4 Middle East & Africa
      • 5.7.3.4.1 National Strategy for Artificial Intelligence (UAE)
      • 5.7.3.4.2 National Artificial Intelligence Strategy (Qatar)
      • 5.7.3.4.3 AI Ethics Principles and Guidelines (Dubai)
    • 5.7.3.5 Latin America
      • 5.7.3.5.1 Santiago Declaration (Chile)
      • 5.7.3.5.2 Brazilian Artificial Intelligence Strategy (EBIA)
• 5.8 PRICING ANALYSIS
  • 5.8.1 AVERAGE SELLING PRICE, BY RENEWABLE ENERGY TYPE
  • 5.8.2 INDICATIVE PRICING ANALYSIS, BY OFFERING, 2024
• 5.9 TECHNOLOGY ANALYSIS
  • 5.9.1 KEY TECHNOLOGIES
    • 5.9.1.1 Conversational AI
    • 5.9.1.2 Energy modeling and simulation tools
    • 5.9.1.3 AutoML
    • 5.9.1.4 MLOps
  • 5.9.2 COMPLEMENTARY TECHNOLOGIES
    • 5.9.2.1 Blockchain
    • 5.9.2.2 Edge computing
    • 5.9.2.3 Sensors and robotics
    • 5.9.2.4 Cybersecurity
    • 5.9.2.5 Big data
    • 5.9.2.6 IoT
  • 5.9.3 ADJACENT TECHNOLOGIES
    • 5.9.3.1 Smart grids
    • 5.9.3.2 Robotics
    • 5.9.3.3 Geospatial technologies
• 5.10 PATENT ANALYSIS
  • 5.10.1 LIST OF MAJOR PATENTS
• 5.11 PORTER'S FIVE FORCES ANALYSIS
  • 5.11.1 THREAT OF NEW ENTRANTS
  • 5.11.2 THREAT OF SUBSTITUTES
  • 5.11.3 BARGAINING POWER OF BUYERS
  • 5.11.4 BARGAINING POWER OF SUPPLIERS
  • 5.11.5 INTENSITY OF COMPETITIVE RIVALRY
• 5.12 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
• 5.13 KEY STAKEHOLDERS AND BUYING CRITERIA
  • 5.13.1 KEY STAKEHOLDERS IN BUYING PROCESS
  • 5.13.2 BUYING CRITERIA
• 5.14 KEY CONFERENCES AND EVENTS, 2024-2025
• 5.15 TECHNOLOGY ROADMAP FOR AI IN ENERGY MARKET
  • 5.15.1 SHORT-TERM ROADMAP (2023-2025)
  • 5.15.2 MID-TERM ROADMAP (2026-2028)
  • 5.15.3 LONG-TERM ROADMAP (2029-2030)
• 5.16 BEST PRACTICES IN AI IN ENERGY MARKET
  • 5.16.1 ENSURE DATA QUALITY AND INTEGRATION
  • 5.16.2 ADOPT AI-POWERED PREDICTIVE MAINTENANCE
  • 5.16.3 FOSTER COLLABORATION AMONG STAKEHOLDERS
  • 5.16.4 PRIORITIZE SCALABILITY AND FLEXIBILITY
  • 5.16.5 FOCUS ON ETHICAL AI IMPLEMENTATION
  • 5.16.6 INVEST IN AI-DRIVEN ENERGY TRADING PLATFORMS
  • 5.16.7 IMPLEMENT AI FOR ENERGY FORECASTING AND LOAD MANAGEMENT
  • 5.16.8 ENHANCE CUSTOMER ENGAGEMENT WITH AI SOLUTIONS
• 5.17 CURRENT AND EMERGING BUSINESS MODELS
  • 5.17.1 ENERGY-AS-A-SERVICE (EAAS)
  • 5.17.2 PREDICTIVE MAINTENANCE CONTRACTS
  • 5.17.3 AI-DRIVEN TRADING PLATFORMS
  • 5.17.4 GRID FLEXIBILITY SOLUTIONS
  • 5.17.5 SUSTAINABILITY-AS-A-SERVICE
  • 5.17.6 REMOTE ENERGY MONITORING AND MANAGEMENT
  • 5.17.7 GREEN FINANCE AND AI-POWERED CREDIT SCORING
  • 5.17.8 AI-BASED ENERGY EFFICIENCY AUDITS AND RETROFITTING SERVICES
• 5.18 AI IN ENERGY MARKET: TOOLS, FRAMEWORKS, AND TECHNIQUES
• 5.19 TRADE ANALYSIS (8542)
  • 5.19.1 EXPORT SCENARIO OF PROCESSORS AND CONTROLLERS
  • 5.19.2 IMPORT SCENARIO OF PROCESSORS AND CONTROLLERS
• 5.20 INVESTMENT AND FUNDING SCENARIO
• 5.21 IMPACT OF AI/GEN AI ON AI IN ENERGY MARKET
  • 5.21.1 IMPACT OF AI/GEN AI ON ENERGY SECTOR
  • 5.21.2 USE CASES OF GEN AI IN ENERGY SECTOR

6 AI IN ENERGY MARKET, BY OFFERING

• 6.1 INTRODUCTION
  • 6.1.1 OFFERING: AI IN ENERGY MARKET DRIVERS
• 6.2 SOLUTIONS
  • 6.2.1 AI IN ENERGY SOLUTIONS TO DRIVE EFFICIENCY, SUSTAINABILITY, AND INNOVATION
• 6.3 SERVICES
  • 6.3.1 FOCUS ON CONTINUOUS MONITORING, MAINTENANCE, AND PERFORMANCE OPTIMIZATION TO BOOST MARKET
  • 6.3.2 PROFESSIONAL SERVICES
    • 6.3.2.1 Training & consulting
    • 6.3.2.2 System integration & implementation
    • 6.3.2.3 Support & maintenance
  • 6.3.3 MANAGED SERVICES

7 AI IN ENERGY MARKET, BY ENERGY TYPE

• 7.1 INTRODUCTION
  • 7.1.1 ENERGY TYPE: AI IN ENERGY MARKET DRIVERS
• 7.2 CONVENTIONAL ENERGY
  • 7.2.1 ENHANCED MONITORING AND OPERATIONAL OPTIMIZATION TO PROPEL MARKET GROWTH
  • 7.2.2 FOSSIL FUELS
    • 7.2.2.1 Coal
    • 7.2.2.2 Oil
    • 7.2.2.3 Natural gas
  • 7.2.3 NUCLEAR ENERGY
  • 7.2.4 OTHER CONVENTIONAL ENERGY TYPES
• 7.3 RENEWABLE ENERGY
  • 7.3.1 BETTER MAINTENANCE PRACTICES, RESOURCE ALLOCATION, AND INTEGRATION OF INNOVATIVE SOLUTIONS TO SUPPORT MARKET GROWTH
  • 7.3.2 SOLAR
  • 7.3.3 WIND
  • 7.3.4 HYDROPOWER
  • 7.3.5 BIOMASS
  • 7.3.6 OTHER RENEWABLE ENERGY TYPES

8 AI IN ENERGY MARKET, BY TYPE

• 8.1 INTRODUCTION
  • 8.1.1 TYPE: AI IN ENERGY MARKET DRIVERS
• 8.2 GENERATIVE AI
  • 8.2.1 GENERATION OF SYNTHETIC DATA THAT MIMICS REAL-WORLD CONDITIONS TO DRIVE MARKET
• 8.3 OTHER AI
  • 8.3.1 AI TECHNOLOGIES TO TRANSFORM ENERGY PROCESSES WITH SMARTER, FASTER, AND MORE ADAPTIVE SOLUTIONS
  • 8.3.2 MACHINE LEARNING
  • 8.3.3 NATURAL LANGUAGE PROCESSING
  • 8.3.4 PREDICTIVE ANALYTICS
  • 8.3.5 COMPUTER VISION

9 AI IN ENERGY MARKET, BY APPLICATION

• 9.1 INTRODUCTION
  • 9.1.1 APPLICATION: AI IN ENERGY MARKET DRIVERS
• 9.2 ENERGY DEMAND FORECASTING
  • 9.2.1 ALIGNING SUPPLY WITH ANTICIPATED DEMAND AND REAL-TIME DEMAND PREDICTIONS TO PROPEL MARKET GROWTH
• 9.3 GRID OPTIMIZATION & MANAGEMENT
  • 9.3.1 REAL-TIME MONITORING, ANALYSIS, AND CONTROL TO HELP TRANSFORM ENERGY NETWORKS INTO INTELLIGENT SYSTEMS
• 9.4 ENERGY STORAGE OPTIMIZATION
  • 9.4.1 PREDICTION OF ENERGY NEEDS AND IDENTIFICATION OF PERFORMANCE ANOMALIES IN STORAGE SYSTEMS TO AID MARKET GROWTH
• 9.5 RENEWABLES INTEGRATION
  • 9.5.1 SEAMLESS INCORPORATION OF VARIABLE ENERGY SOURCES INTO POWER GRIDS TO ENSURE EFFICIENCY AND RELIABILITY
• 9.6 ENERGY TRADING & MARKET FORECASTING
  • 9.6.1 CRUCIAL ROLE IN STREAMLINING OPERATIONS AND FOSTERING SUSTAINABLE ENERGY ECONOMIES TO SUPPORT MARKET GROWTH
• 9.7 ENERGY SUSTAINABILITY MANAGEMENT
  • 9.7.1 REAL-TIME MONITORING OF ENERGY CONSUMPTION TO DRIVE MARKET
• 9.8 DISASTER RESILIENCE & RECOVERY
  • 9.8.1 RISING DEMAND FOR MINIMIZING DOWNTIME AND ENSURING RELIABLE POWER DURING CRISES TO HELP MARKET GROWTH
• 9.9 OTHER APPLICATIONS

10 AI IN ENERGY MARKET, BY END USE

• 10.1 INTRODUCTION
  • 10.1.1 END USE: AI IN ENERGY MARKET DRIVERS
• 10.2 GENERATION
  • 10.2.1 REDUCED COSTS, ENHANCED SUSTAINABILITY, AND IMPROVED OPERATIONAL EFFICIENCY TO FOSTER MARKET GROWTH
• 10.3 TRANSMISSION
  • 10.3.1 RESILIENT, SUSTAINABLE, AND SECURE ENERGY INFRASTRUCTURE TO DRIVE MARKET
• 10.4 DISTRIBUTION
  • 10.4.1 OPTIMIZATION OF ENERGY DISTRIBUTION BY BALANCING LOAD DEMAND AND DETECTING FAULTS IN REAL TIME TO BOOST MARKET
• 10.5 CONSUMPTION
  • 10.5.1 OPTIMIZED ENERGY USAGE, REDUCED COSTS, AND ENHANCED SUSTAINABILITY TO FUEL MARKET GROWTH
  • 10.5.2 COMMERCIAL
  • 10.5.3 INDUSTRIAL

11 AI IN ENERGY MARKET, BY REGION

• 11.1 INTRODUCTION
• 11.2 NORTH AMERICA
  • 11.2.1 NORTH AMERICA: MACROECONOMIC OUTLOOK
  • 11.2.2 US
    • 11.2.2.1 Government initiatives and funding to boost market growth
  • 11.2.3 CANADA
    • 11.2.3.1 Increased focus on reducing energy consumption to fuel market growth
• 11.3 EUROPE
  • 11.3.1 EUROPE: MACROECONOMIC OUTLOOK
  • 11.3.2 GERMANY
    • 11.3.2.1 Significant investments and collaborative projects to drive market growth
  • 11.3.3 UK
    • 11.3.3.1 Key investments focused on cutting emissions in energy and transportation to drive market
  • 11.3.4 FRANCE
    • 11.3.4.1 Increased focus on reducing environmental impact of fossil fuels to accelerate market growth
  • 11.3.5 ITALY
    • 11.3.5.1 Public investments and collaboration between private players to drive market
  • 11.3.6 SPAIN
    • 11.3.6.1 Green energy initiatives and investments to aid market growth
  • 11.3.7 NORDICS
    • 11.3.7.1 Innovative AI-based projects to reduce energy consumption and government initiatives driving market growth
  • 11.3.8 REST OF EUROPE
• 11.4 ASIA PACIFIC
  • 11.4.1 ASIA PACIFIC: MACROECONOMIC OUTLOOK
  • 11.4.2 CHINA
    • 11.4.2.1 Rising demand for energy efficiency and sustainability to fuel market growth
  • 11.4.3 JAPAN
    • 11.4.3.1 Initiatives for reducing fossil fuel reliance to drive sustainable market growth
  • 11.4.4 INDIA
    • 11.4.4.1 Government initiatives for sustainable development and efficient resource management to foster market growth
  • 11.4.5 AUSTRALIA & NEW ZEALAND
    • 11.4.5.1 Increasing demand for smart home energy to drive market
  • 11.4.6 SOUTH KOREA
    • 11.4.6.1 Transformative shift driven by AI initiatives to bolster market growth
  • 11.4.7 ASEAN
    • 11.4.7.1 Growing integration of AI into energy systems to drive sustainability and efficiency
  • 11.4.8 REST OF ASIA PACIFIC
• 11.5 MIDDLE EAST & AFRICA
  • 11.5.1 MIDDLE EAST & AFRICA: MACROECONOMIC OUTLOOK
    • 11.5.1.1 KSA
      • 11.5.1.1.1 Increasing focus on reducing transmission losses and enhancing energy efficiency goals to aid market growth
    • 11.5.1.2 UAE
      • 11.5.1.2.1 Increasing energy demands and focus on reducing environmental footprints to foster market growth
    • 11.5.1.3 Kuwait
      • 11.5.1.3.1 Rising applications of AI for enhancing asset management, operational excellence, and technical capabilities to assist market growth
    • 11.5.1.4 Bahrain
      • 11.5.1.4.1 Digitalization in energy sector to drive growth
    • 11.5.1.5 South Africa
      • 11.5.1.5.1 Increasing awareness of sustainability and government commitments to create significant growth opportunities
    • 11.5.1.6 Rest of Middle East & Africa
• 11.6 LATIN AMERICA
  • 11.6.1 LATIN AMERICA: MACROECONOMIC OUTLOOK
  • 11.6.2 BRAZIL
    • 11.6.2.1 Government support, technological advancements, and skilled workforce to drive market
  • 11.6.3 ARGENTINA
    • 11.6.3.1 Government initiatives for optimizing energy consumption and integrating renewable sources to accelerate market growth
  • 11.6.4 MEXICO
    • 11.6.4.1 National AI strategy and increasing demand for energy forecasting to drive market
  • 11.6.5 REST OF LATIN AMERICA

12 COMPETITIVE LANDSCAPE

• 12.1 INTRODUCTION
• 12.2 KEY PLAYER STRATEGIES/RIGHT TO WIN, 2021-2024
• 12.3 MARKET SHARE ANALYSIS, 2024
  • 12.3.1 MARKET RANKING ANALYSIS
• 12.4 REVENUE ANALYSIS, 2019-2023
• 12.5 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2024
  • 12.5.1 STARS
  • 12.5.2 EMERGING LEADERS
  • 12.5.3 PERVASIVE PLAYERS
  • 12.5.4 PARTICIPANTS
  • 12.5.5 COMPANY FOOTPRINT: KEY PLAYERS, 2024
    • 12.5.5.1 Company footprint
    • 12.5.5.2 Region footprint
    • 12.5.5.3 Offering footprint
    • 12.5.5.4 Energy type footprint
    • 12.5.5.5 Type footprint
    • 12.5.5.6 Application footprint
    • 12.5.5.7 End-use footprint
• 12.6 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2024
  • 12.6.1 PROGRESSIVE COMPANIES
  • 12.6.2 RESPONSIVE COMPANIES
  • 12.6.3 DYNAMIC COMPANIES
  • 12.6.4 STARTING BLOCKS
  • 12.6.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2024
    • 12.6.5.1 Detailed list of key startups/SMEs
    • 12.6.5.2 Competitive benchmarking of key startups/SMEs
• 12.7 COMPETITIVE SCENARIO
  • 12.7.1 PRODUCT LAUNCHES AND ENHANCEMENTS
  • 12.7.2 DEALS
• 12.8 BRAND/PRODUCT COMPARISON
• 12.9 COMPANY VALUATION AND FINANCIAL METRICS

13 COMPANY PROFILES

• 13.1 KEY PLAYERS
  • 13.1.1 SCHNEIDER ELECTRIC SE
    • 13.1.1.1 Business overview
    • 13.1.1.2 Products/Solutions/Services offered
    • 13.1.1.3 Recent developments
      • 13.1.1.3.1 Product launches and enhancements
      • 13.1.1.3.2 Deals
    • 13.1.1.4 MnM view
      • 13.1.1.4.1 Key strengths
      • 13.1.1.4.2 Strategic choices
      • 13.1.1.4.3 Weaknesses and competitive threats
  • 13.1.2 GE VERNOVA
    • 13.1.2.1 Business overview
    • 13.1.2.2 Products/Solutions/Services offered
    • 13.1.2.3 Recent developments
      • 13.1.2.3.1 Product launches and enhancements
      • 13.1.2.3.2 Deals
    • 13.1.2.4 MnM view
      • 13.1.2.4.1 Key strengths
      • 13.1.2.4.2 Strategic choices
      • 13.1.2.4.3 Weaknesses and competitive threats
  • 13.1.3 ABB LTD.
    • 13.1.3.1 Business overview
    • 13.1.3.2 Products/Solutions/Services offered
    • 13.1.3.3 Recent developments
      • 13.1.3.3.1 Deals
    • 13.1.3.4 MnM view
      • 13.1.3.4.1 Key strengths
      • 13.1.3.4.2 Strategic choices
      • 13.1.3.4.3 Weaknesses and competitive threats
  • 13.1.4 HONEYWELL INTERNATIONAL, INC.
    • 13.1.4.1 Business overview
    • 13.1.4.2 Products/Solutions/Services offered
    • 13.1.4.3 Recent developments
      • 13.1.4.3.1 Product launches and enhancements
      • 13.1.4.3.2 Deals
    • 13.1.4.4 MnM view
      • 13.1.4.4.1 Key strengths
      • 13.1.4.4.2 Strategic choices
      • 13.1.4.4.3 Weaknesses and competitive threats
  • 13.1.5 SIEMENS AG
    • 13.1.5.1 Business overview
    • 13.1.5.2 Products/Solutions/Services offered
    • 13.1.5.3 Recent developments
      • 13.1.5.3.1 Deals
    • 13.1.5.4 MnM view
      • 13.1.5.4.1 Key strengths
      • 13.1.5.4.2 Strategic choices
      • 13.1.5.4.3 Weaknesses and competitive threats
  • 13.1.6 ORACLE CORPORATION
    • 13.1.6.1 Business overview
    • 13.1.6.2 Products/Solutions/Services offered
    • 13.1.6.3 Recent developments
      • 13.1.6.3.1 Deals
  • 13.1.7 VESTAS WIND SYSTEMS A/S
    • 13.1.7.1 Business overview
    • 13.1.7.2 Products/Solutions/Services offered
    • 13.1.7.3 Recent developments
      • 13.1.7.3.1 Deals
  • 13.1.8 IBM CORPORATION
    • 13.1.8.1 Business overview
    • 13.1.8.2 Products/Solutions/Services offered
    • 13.1.8.3 Recent developments
      • 13.1.8.3.1 Deals
  • 13.1.9 MICROSOFT CORPORATION, INC.
    • 13.1.9.1 Business overview
    • 13.1.9.2 Products/Solutions/Services offered
    • 13.1.9.3 Recent developments
      • 13.1.9.3.1 Deals
  • 13.1.10 AMAZON WEB SERVICES, INC
    • 13.1.10.1 Business overview
    • 13.1.10.2 Products/Solutions/Services offered
    • 13.1.10.3 Recent developments
      • 13.1.10.3.1 Deals
  • 13.1.11 ATOS SE
    • 13.1.11.1 Business overview
    • 13.1.11.2 Products/Solutions/Services offered
    • 13.1.11.3 Recent developments
      • 13.1.11.3.1 Product launches and enhancements
      • 13.1.11.3.2 Deals
  • 13.1.12 TESLA, INC.
  • 13.1.13 C3.AI, INC.
  • 13.1.14 ALPIQ
  • 13.1.15 ENEL S.P.A.
• 13.2 STARTUPS/SMES
  • 13.2.1 ORIGAMI ENERGY
  • 13.2.2 INNOWATTS
  • 13.2.3 IRASUS TECHNOLOGIES
  • 13.2.4 GRID4C
  • 13.2.5 UPLIGHT
  • 13.2.6 GRIDBEYOND
  • 13.2.7 ESMART SYSTEMS
  • 13.2.8 NDUSTRIAL
  • 13.2.9 DATATEGY
  • 13.2.10 OMDENA
  • 13.2.11 BIDGELY
  • 13.2.12 AVATHON

14 ADJACENT/RELATED MARKETS

• 14.1 INTRODUCTION
• 14.2 CONVERSATIONAL AI MARKET
  • 14.2.1 MARKET OVERVIEW
  • 14.2.2 CONVERSATIONAL AI MARKET, BY OFFERING
• 14.3 SERVICES
  • 14.3.1 CONVERSATIONAL AI MARKET, BY SERVICE
  • 14.3.2 CONVERSATIONAL AI MARKET, BY BUSINESS FUNCTION
  • 14.3.3 CONVERSATIONAL AI MARKET, BY INTEGRATION MODE
  • 14.3.4 CONVERSATIONAL AI MARKET, BY VERTICAL
• 14.4 CUSTOMER EXPERIENCE MANAGEMENT MARKET
  • 14.4.1 MARKET DEFINITION
  • 14.4.2 MARKET OVERVIEW
  • 14.4.3 CUSTOMER EXPERIENCE MANAGEMENT MARKET, BY OFFERING
  • 14.4.4 CUSTOMER EXPERIENCE MANAGEMENT MARKET, BY DEPLOYMENT TYPE
  • 14.4.5 CUSTOMER EXPERIENCE MANAGEMENT MARKET, BY ORGANIZATION SIZE
  • 14.4.6 CUSTOMER EXPERIENCE MANAGEMENT MARKET, BY VERTICAL

15 APPENDIX

• 15.1 DISCUSSION GUIDE
• 15.2 KNOWLEDGESTORE: MARKETSANDMARKETS' SUBSCRIPTION PORTAL
• 15.3 CUSTOMIZATION OPTIONS
• 15.4 RELATED REPORTS
• 15.5 AUTHOR DETAILS