Summary

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).

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Table of Contents

1 INTRODUCTION 33
1.1 STUDY OBJECTIVES 33
1.2 MARKET DEFINITION 33
1.3 STUDY SCOPE 34
1.3.1 MARKET SEGMENTATION 34
1.3.2 INCLUSIONS AND EXCLUSIONS 35
1.4 YEARS CONSIDERED 35
1.5 CURRENCY CONSIDERED 36
1.6 STAKEHOLDERS 36
2 RESEARCH METHODOLOGY 37
2.1 RESEARCH DATA 37
2.1.1 SECONDARY DATA 38
2.1.2 PRIMARY DATA 38
2.1.2.1 Primary interviews with experts 38
2.1.2.2 Breakdown of primary profiles 39
2.1.2.3 Key insights from industry experts 39
2.2 MARKET SIZE ESTIMATION 40
2.2.1 TOP-DOWN APPROACH 41
2.2.2 BOTTOM-UP APPROACH 42
2.2.3 AI IN ENERGY MARKET ESTIMATION: DEMAND-SIDE ANALYSIS 43
2.3 DATA TRIANGULATION 44
2.4 LIMITATIONS AND RISK ASSESSMENT 45
2.5 RESEARCH ASSUMPTIONS 45
2.6 RESEARCH LIMITATIONS 45
3 EXECUTIVE SUMMARY 46
4 PREMIUM INSIGHTS 48
4.1 OPPORTUNITIES FOR KEY PLAYERS IN AI IN ENERGY MARKET 48
4.2 AI IN ENERGY MARKET, BY OFFERING 48
4.3 AI IN ENERGY MARKET, BY SERVICE 49
4.4 AI IN ENERGY MARKET, BY PROFESSIONAL SERVICE 49
4.5 AI IN ENERGY MARKET, BY APPLICATION 50
4.6 AI IN ENERGY MARKET, BY ENERGY TYPE 50
4.7 AI IN ENERGY MARKET, BY END USE 51
4.8 AI IN ENERGY MARKET, BY TYPE 51
4.9 NORTH AMERICA: AI IN ENERGY MARKET, BY OFFERING AND END USE 52
5 MARKET OVERVIEW AND INDUSTRY TRENDS 53
5.1 INTRODUCTION 53
5.2 MARKET DYNAMICS 53
5.2.1 DRIVERS 54
5.2.1.1 Energy market volatility and risk management 54
5.2.1.2 Rising consumer demand for smart energy solutions 54
5.2.1.3 AI-powered robots increasing energy sector worker safety 54
5.2.2 RESTRAINTS 54
5.2.2.1 Data privacy and security 54
5.2.2.2 High implementation costs 55
5.2.3 OPPORTUNITIES 55
5.2.3.1 Increasing shift toward carbon emission reduction and sustainability 55
5.2.3.2 Renewable energy integration 55
5.2.4 CHALLENGES 56
5.2.4.1 Insufficient real-time energy data limiting training and deployment of AI models 56
5.2.4.2 Lack of skilled professionals in AI and energy analytics 56
5.3 BRIEF HISTORY OF AI IN ENERGY MARKET 56
5.4 ECOSYSTEM ANALYSIS 57
5.5 CASE STUDY ANALYSIS 59
5.5.1 OPTIMIZING ENERGY EFFICIENCY ACROSS PORTFOLIOS: BLACKSTONE'S STRATEGIC PARTNERSHIP WITH SCHNEIDER ELECTRIC 59
5.5.2 C3 AI ENERGY MANAGEMENT PLATFORM HELPED LEADING PETROCHEMICAL COMPANY BOOST ENERGY EFFICIENCY AND ENVIRONMENTAL PERFORMANCE 60
5.5.3 ENVERUS INSTANT ANALYST ENABLED ENERGY COMPANIES IMPROVE DECISION-MAKING AND OPERATIONAL EFFICIENCY 61
5.5.4 AI-POWERED MICROGRIDS FACILITATED ENERGY RESILIENCE AND EQUITY IN REGIONAL COMMUNITIES 61
5.5.5 C3 AI ENERGY MANAGEMENT PLATFORM HELPED LEADING STEEL MANUFACTURER GAIN SUBSTANTIAL COST SAVINGS AND
OPERATIONAL IMPROVEMENTS 62
5.6 SUPPLY CHAIN ANALYSIS 63
5.7 TARIFF AND REGULATORY LANDSCAPE 64
5.7.1 TARIFF RELATED TO PROCESSORS AND CONTROLLERS (HSN: 854231) 64
5.7.2 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 65
5.7.3 KEY REGULATIONS: AI IN ENERGY 68
5.7.3.1 North America 68
5.7.3.1.1 SCR 17: Artificial Intelligence Bill (California) 68
5.7.3.1.2 S1103: Artificial Intelligence Automated Decision Bill (Connecticut) 68
5.7.3.1.3 National Artificial Intelligence Initiative Act (NAIIA) 69
5.7.3.1.4 The Artificial Intelligence and Data Act (AIDA) - Canada 69
5.7.3.2 Europe 70
5.7.3.2.1 European Union (EU) - Artificial Intelligence Act (AIA) 70
5.7.3.2.2 General Data Protection Regulation (Europe) 70
5.7.3.3 Asia Pacific 71
5.7.3.3.1 Interim Administrative Measures for Generative Artificial Intelligence Services (China) 71
5.7.3.3.2 National AI Strategy (Singapore) 71
5.7.3.3.3 Hiroshima AI Process Comprehensive Policy Framework (Japan) 72
5.7.3.4 Middle East & Africa 72
5.7.3.4.1 National Strategy for Artificial Intelligence (UAE) 72
5.7.3.4.2 National Artificial Intelligence Strategy (Qatar) 73
5.7.3.4.3 AI Ethics Principles and Guidelines (Dubai) 73
5.7.3.5 Latin America 73
5.7.3.5.1 Santiago Declaration (Chile) 73
5.7.3.5.2 Brazilian Artificial Intelligence Strategy (EBIA) 74
5.8 PRICING ANALYSIS 74
5.8.1 AVERAGE SELLING PRICE, BY RENEWABLE ENERGY TYPE 74
5.8.2 INDICATIVE PRICING ANALYSIS, BY OFFERING, 2024 75
5.9 TECHNOLOGY ANALYSIS 75
5.9.1 KEY TECHNOLOGIES 75
5.9.1.1 Conversational AI 75
5.9.1.2 Energy modeling and simulation tools 76
5.9.1.3 AutoML 76
5.9.1.4 MLOps 76
5.9.2 COMPLEMENTARY TECHNOLOGIES 77
5.9.2.1 Blockchain 77
5.9.2.2 Edge computing 77
5.9.2.3 Sensors and robotics 77
5.9.2.4 Cybersecurity 78
5.9.2.5 Big data 78
5.9.2.6 IoT 78
5.9.3 ADJACENT TECHNOLOGIES 79
5.9.3.1 Smart grids 79
5.9.3.2 Robotics 79
5.9.3.3 Geospatial technologies 79
5.10 PATENT ANALYSIS 80
5.10.1 LIST OF MAJOR PATENTS 81
5.11 PORTER’S FIVE FORCES ANALYSIS 83
5.11.1 THREAT OF NEW ENTRANTS 84
5.11.2 THREAT OF SUBSTITUTES 84
5.11.3 BARGAINING POWER OF BUYERS 85
5.11.4 BARGAINING POWER OF SUPPLIERS 85
5.11.5 INTENSITY OF COMPETITIVE RIVALRY 85
5.12 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS 85
5.13 KEY STAKEHOLDERS AND BUYING CRITERIA 86
5.13.1 KEY STAKEHOLDERS IN BUYING PROCESS 86
5.13.2 BUYING CRITERIA 87
5.14 KEY CONFERENCES AND EVENTS, 2024–2025 88
5.15 TECHNOLOGY ROADMAP FOR AI IN ENERGY MARKET 89
5.15.1 SHORT-TERM ROADMAP (2023–2025) 89
5.15.2 MID-TERM ROADMAP (2026–2028) 89
5.15.3 LONG-TERM ROADMAP (2029–2030) 89
5.16 BEST PRACTICES IN AI IN ENERGY MARKET 90
5.16.1 ENSURE DATA QUALITY AND INTEGRATION 90
5.16.2 ADOPT AI-POWERED PREDICTIVE MAINTENANCE 90
5.16.3 FOSTER COLLABORATION AMONG STAKEHOLDERS 90
5.16.4 PRIORITIZE SCALABILITY AND FLEXIBILITY 90
5.16.5 FOCUS ON ETHICAL AI IMPLEMENTATION 90
5.16.6 INVEST IN AI-DRIVEN ENERGY TRADING PLATFORMS 90
5.16.7 IMPLEMENT AI FOR ENERGY FORECASTING AND LOAD MANAGEMENT 90
5.16.8 ENHANCE CUSTOMER ENGAGEMENT WITH AI SOLUTIONS 90
5.17 CURRENT AND EMERGING BUSINESS MODELS 91
5.17.1 ENERGY-AS-A-SERVICE (EAAS) 91
5.17.2 PREDICTIVE MAINTENANCE CONTRACTS 91
5.17.3 AI-DRIVEN TRADING PLATFORMS 91
5.17.4 GRID FLEXIBILITY SOLUTIONS 91
5.17.5 SUSTAINABILITY-AS-A-SERVICE 91
5.17.6 REMOTE ENERGY MONITORING AND MANAGEMENT 91
5.17.7 GREEN FINANCE AND AI-POWERED CREDIT SCORING 91
5.17.8 AI-BASED ENERGY EFFICIENCY AUDITS AND RETROFITTING SERVICES 91
5.18 AI IN ENERGY MARKET: TOOLS, FRAMEWORKS, AND TECHNIQUES 92
5.19 TRADE ANALYSIS (8542) 92
5.19.1 EXPORT SCENARIO OF PROCESSORS AND CONTROLLERS 92
5.19.2 IMPORT SCENARIO OF PROCESSORS AND CONTROLLERS 94
5.20 INVESTMENT AND FUNDING SCENARIO 95
5.21 IMPACT OF AI/GEN AI ON AI IN ENERGY MARKET 96
5.21.1 IMPACT OF AI/GEN AI ON ENERGY SECTOR 96
5.21.2 USE CASES OF GEN AI IN ENERGY SECTOR 96
6 AI IN ENERGY MARKET, BY OFFERING 98
6.1 INTRODUCTION 99
6.1.1 OFFERING: AI IN ENERGY MARKET DRIVERS 99
 
6.2 SOLUTIONS 100
6.2.1 AI IN ENERGY SOLUTIONS TO DRIVE EFFICIENCY, SUSTAINABILITY, AND INNOVATION 100
6.3 SERVICES 101
6.3.1 FOCUS ON CONTINUOUS MONITORING, MAINTENANCE, AND PERFORMANCE OPTIMIZATION TO BOOST MARKET 101
6.3.2 PROFESSIONAL SERVICES 103
6.3.2.1 Training & consulting 105
6.3.2.2 System integration & implementation 106
6.3.2.3 Support & maintenance 107
6.3.3 MANAGED SERVICES 108
7 AI IN ENERGY MARKET, BY ENERGY TYPE 109
7.1 INTRODUCTION 110
7.1.1 ENERGY TYPE: AI IN ENERGY MARKET DRIVERS 110
7.2 CONVENTIONAL ENERGY 111
7.2.1 ENHANCED MONITORING AND OPERATIONAL OPTIMIZATION TO PROPEL MARKET GROWTH 111
7.2.2 FOSSIL FUELS 112
7.2.2.1 Coal 113
7.2.2.2 Oil 113
7.2.2.3 Natural gas 113
7.2.3 NUCLEAR ENERGY 114
7.2.4 OTHER CONVENTIONAL ENERGY TYPES 115
7.3 RENEWABLE ENERGY 116
7.3.1 BETTER MAINTENANCE PRACTICES, RESOURCE ALLOCATION, AND INTEGRATION OF INNOVATIVE SOLUTIONS TO SUPPORT MARKET GROWTH 116
7.3.2 SOLAR 117
7.3.3 WIND 118
7.3.4 HYDROPOWER 119
7.3.5 BIOMASS 120
7.3.6 OTHER RENEWABLE ENERGY TYPES 121
8 AI IN ENERGY MARKET, BY TYPE 122
8.1 INTRODUCTION 123
8.1.1 TYPE: AI IN ENERGY MARKET DRIVERS 123
8.2 GENERATIVE AI 124
8.2.1 GENERATION OF SYNTHETIC DATA THAT MIMICS REAL-WORLD CONDITIONS TO DRIVE MARKET 124
8.3 OTHER AI 125
8.3.1 AI TECHNOLOGIES TO TRANSFORM ENERGY PROCESSES WITH SMARTER, FASTER, AND MORE ADAPTIVE SOLUTIONS 125
8.3.2 MACHINE LEARNING 126
8.3.3 NATURAL LANGUAGE PROCESSING 127
8.3.4 PREDICTIVE ANALYTICS 127
8.3.5 COMPUTER VISION 127
9 AI IN ENERGY MARKET, BY APPLICATION 128
9.1 INTRODUCTION 129
9.1.1 APPLICATION: AI IN ENERGY MARKET DRIVERS 129
9.2 ENERGY DEMAND FORECASTING 131
9.2.1 ALIGNING SUPPLY WITH ANTICIPATED DEMAND AND REAL-TIME DEMAND PREDICTIONS TO PROPEL MARKET GROWTH 131
9.3 GRID OPTIMIZATION & MANAGEMENT 132
9.3.1 REAL-TIME MONITORING, ANALYSIS, AND CONTROL TO HELP TRANSFORM ENERGY NETWORKS INTO INTELLIGENT SYSTEMS 132
9.4 ENERGY STORAGE OPTIMIZATION 133
9.4.1 PREDICTION OF ENERGY NEEDS AND IDENTIFICATION OF PERFORMANCE ANOMALIES IN STORAGE SYSTEMS TO AID MARKET GROWTH 133
9.5 RENEWABLES INTEGRATION 134
9.5.1 SEAMLESS INCORPORATION OF VARIABLE ENERGY SOURCES INTO POWER GRIDS TO ENSURE EFFICIENCY AND RELIABILITY 134
9.6 ENERGY TRADING & MARKET FORECASTING 135
9.6.1 CRUCIAL ROLE IN STREAMLINING OPERATIONS AND FOSTERING SUSTAINABLE ENERGY ECONOMIES TO SUPPORT MARKET GROWTH 135
9.7 ENERGY SUSTAINABILITY MANAGEMENT 136
9.7.1 REAL-TIME MONITORING OF ENERGY CONSUMPTION TO DRIVE MARKET 136
9.8 DISASTER RESILIENCE & RECOVERY 137
9.8.1 RISING DEMAND FOR MINIMIZING DOWNTIME AND ENSURING RELIABLE POWER DURING CRISES TO HELP MARKET GROWTH 137
9.9 OTHER APPLICATIONS 138
10 AI IN ENERGY MARKET, BY END USE 139
10.1 INTRODUCTION 140
10.1.1 END USE: AI IN ENERGY MARKET DRIVERS 140
10.2 GENERATION 142
10.2.1 REDUCED COSTS, ENHANCED SUSTAINABILITY, AND IMPROVED OPERATIONAL EFFICIENCY TO FOSTER MARKET GROWTH 142
10.3 TRANSMISSION 143
10.3.1 RESILIENT, SUSTAINABLE, AND SECURE ENERGY INFRASTRUCTURE TO DRIVE MARKET 143
10.4 DISTRIBUTION 144
10.4.1 OPTIMIZATION OF ENERGY DISTRIBUTION BY BALANCING LOAD DEMAND AND DETECTING FAULTS IN REAL TIME TO BOOST MARKET 144
10.5 CONSUMPTION 145
10.5.1 OPTIMIZED ENERGY USAGE, REDUCED COSTS, AND ENHANCED SUSTAINABILITY TO FUEL MARKET GROWTH 145
10.5.2 COMMERCIAL 146
10.5.3 INDUSTRIAL 147
11 AI IN ENERGY MARKET, BY REGION 149
11.1 INTRODUCTION 150
11.2 NORTH AMERICA 151
11.2.1 NORTH AMERICA: MACROECONOMIC OUTLOOK 151
11.2.2 US 159
11.2.2.1 Government initiatives and funding to boost market growth 159
11.2.3 CANADA 164
11.2.3.1 Increased focus on reducing energy consumption to fuel market growth 164
11.3 EUROPE 171
11.3.1 EUROPE: MACROECONOMIC OUTLOOK 171
11.3.2 GERMANY 178
11.3.2.1 Significant investments and collaborative projects to drive market growth 178
11.3.3 UK 184
11.3.3.1 Key investments focused on cutting emissions in energy and transportation to drive market 184
11.3.4 FRANCE 185
11.3.4.1 Increased focus on reducing environmental impact of fossil fuels to accelerate market growth 185
11.3.5 ITALY 185
11.3.5.1 Public investments and collaboration between private players to drive market 185
11.3.6 SPAIN 185
11.3.6.1 Green energy initiatives and investments to aid market growth 185
11.3.7 NORDICS 186
11.3.7.1 Innovative AI-based projects to reduce energy consumption and government initiatives driving market growth 186
11.3.8 REST OF EUROPE 186
11.4 ASIA PACIFIC 187
11.4.1 ASIA PACIFIC: MACROECONOMIC OUTLOOK 187
11.4.2 CHINA 195
11.4.2.1 Rising demand for energy efficiency and sustainability to fuel market growth 195
11.4.3 JAPAN 201
11.4.3.1 Initiatives for reducing fossil fuel reliance to drive sustainable market growth 201
11.4.4 INDIA 201
11.4.4.1 Government initiatives for sustainable development and efficient resource management to foster market growth 201
11.4.5 AUSTRALIA & NEW ZEALAND 202
11.4.5.1 Increasing demand for smart home energy to drive market 202
11.4.6 SOUTH KOREA 202
11.4.6.1 Transformative shift driven by AI initiatives to bolster market growth 202
11.4.7 ASEAN 203
11.4.7.1 Growing integration of AI into energy systems to drive sustainability and efficiency 203
11.4.8 REST OF ASIA PACIFIC 203
11.5 MIDDLE EAST & AFRICA 203
11.5.1 MIDDLE EAST & AFRICA: MACROECONOMIC OUTLOOK 203
11.5.1.1 KSA 210
11.5.1.1.1 Increasing focus on reducing transmission losses and enhancing energy efficiency goals to aid market growth 210
11.5.1.2 UAE 216
11.5.1.2.1 Increasing energy demands and focus on reducing environmental footprints to foster market growth 216
11.5.1.3 Kuwait 216
11.5.1.3.1 Rising applications of AI for enhancing asset management, operational excellence, and technical capabilities to assist market growth 216
11.5.1.4 Bahrain 217
11.5.1.4.1 Digitalization in energy sector to drive growth 217
11.5.1.5 South Africa 217
11.5.1.5.1 Increasing awareness of sustainability and government commitments to create significant growth opportunities 217
11.5.1.6 Rest of Middle East & Africa 217
11.6 LATIN AMERICA 218
11.6.1 LATIN AMERICA: MACROECONOMIC OUTLOOK 218
11.6.2 BRAZIL 225
11.6.2.1 Government support, technological advancements, and skilled workforce to drive market 225
11.6.3 ARGENTINA 230
11.6.3.1 Government initiatives for optimizing energy consumption and integrating renewable sources to accelerate market growth 230
11.6.4 MEXICO 231
11.6.4.1 National AI strategy and increasing demand for energy forecasting to drive market 231
11.6.5 REST OF LATIN AMERICA 231
12 COMPETITIVE LANDSCAPE 232
12.1 INTRODUCTION 232
12.2 KEY PLAYER STRATEGIES/RIGHT TO WIN, 2021–2024 232
12.3 MARKET SHARE ANALYSIS, 2024 234
12.3.1 MARKET RANKING ANALYSIS 236
12.4 REVENUE ANALYSIS, 2019–2023 237
12.5 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2024 237
12.5.1 STARS 237
12.5.2 EMERGING LEADERS 237
12.5.3 PERVASIVE PLAYERS 238
12.5.4 PARTICIPANTS 238
12.5.5 COMPANY FOOTPRINT: KEY PLAYERS, 2024 239
12.5.5.1 Company footprint 239
12.5.5.2 Region footprint 240
12.5.5.3 Offering footprint 241
12.5.5.4 Energy type footprint 242
12.5.5.5 Type footprint 243
12.5.5.6 Application footprint 244
12.5.5.7 End-use footprint 245
12.6 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2024 246
12.6.1 PROGRESSIVE COMPANIES 246
12.6.2 RESPONSIVE COMPANIES 246
12.6.3 DYNAMIC COMPANIES 246
12.6.4 STARTING BLOCKS 246
12.6.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2024 248
12.6.5.1 Detailed list of key startups/SMEs 248
12.6.5.2 Competitive benchmarking of key startups/SMEs 249
12.7 COMPETITIVE SCENARIO 250
12.7.1 PRODUCT LAUNCHES AND ENHANCEMENTS 250
12.7.2 DEALS 251
12.8 BRAND/PRODUCT COMPARISON 253
12.9 COMPANY VALUATION AND FINANCIAL METRICS 254
13 COMPANY PROFILES 255
13.1 KEY PLAYERS 255
13.1.1 SCHNEIDER ELECTRIC SE 255
13.1.1.1 Business overview 255
13.1.1.2 Products/Solutions/Services offered 257
13.1.1.3 Recent developments 257
13.1.1.3.1 Product launches and enhancements 257
13.1.1.3.2 Deals 258
13.1.1.4 MnM view 258
13.1.1.4.1 Key strengths 258
13.1.1.4.2 Strategic choices 258
13.1.1.4.3 Weaknesses and competitive threats 258
13.1.2 GE VERNOVA 259
13.1.2.1 Business overview 259
13.1.2.2 Products/Solutions/Services offered 259
13.1.2.3 Recent developments 260
13.1.2.3.1 Product launches and enhancements 260
13.1.2.3.2 Deals 261
 
13.1.2.4 MnM view 261
13.1.2.4.1 Key strengths 261
13.1.2.4.2 Strategic choices 261
13.1.2.4.3 Weaknesses and competitive threats 261
13.1.3 ABB LTD. 262
13.1.3.1 Business overview 262
13.1.3.2 Products/Solutions/Services offered 263
13.1.3.3 Recent developments 264
13.1.3.3.1 Deals 264
13.1.3.4 MnM view 264
13.1.3.4.1 Key strengths 264
13.1.3.4.2 Strategic choices 264
13.1.3.4.3 Weaknesses and competitive threats 264
13.1.4 HONEYWELL INTERNATIONAL, INC. 265
13.1.4.1 Business overview 265
13.1.4.2 Products/Solutions/Services offered 267
13.1.4.3 Recent developments 268
13.1.4.3.1 Product launches and enhancements 268
13.1.4.3.2 Deals 268
13.1.4.4 MnM view 269
13.1.4.4.1 Key strengths 269
13.1.4.4.2 Strategic choices 269
13.1.4.4.3 Weaknesses and competitive threats 269
13.1.5 SIEMENS AG 270
13.1.5.1 Business overview 270
13.1.5.2 Products/Solutions/Services offered 271
13.1.5.3 Recent developments 272
13.1.5.3.1 Deals 272
13.1.5.4 MnM view 272
13.1.5.4.1 Key strengths 272
13.1.5.4.2 Strategic choices 272
13.1.5.4.3 Weaknesses and competitive threats 272
13.1.6 ORACLE CORPORATION 273
13.1.6.1 Business overview 273
13.1.6.2 Products/Solutions/Services offered 274
13.1.6.3 Recent developments 275
13.1.6.3.1 Deals 275
13.1.7 VESTAS WIND SYSTEMS A/S 276
13.1.7.1 Business overview 276
13.1.7.2 Products/Solutions/Services offered 277
13.1.7.3 Recent developments 278
13.1.7.3.1 Deals 278
13.1.8 IBM CORPORATION 279
13.1.8.1 Business overview 279
13.1.8.2 Products/Solutions/Services offered 281
13.1.8.3 Recent developments 282
13.1.8.3.1 Deals 282
13.1.9 MICROSOFT CORPORATION, INC. 283
13.1.9.1 Business overview 283
13.1.9.2 Products/Solutions/Services offered 284
13.1.9.3 Recent developments 285
13.1.9.3.1 Deals 285
13.1.10 AMAZON WEB SERVICES, INC 286
13.1.10.1 Business overview 286
13.1.10.2 Products/Solutions/Services offered 287
13.1.10.3 Recent developments 287
13.1.10.3.1 Deals 287
13.1.11 ATOS SE 288
13.1.11.1 Business overview 288
13.1.11.2 Products/Solutions/Services offered 289
13.1.11.3 Recent developments 291
13.1.11.3.1 Product launches and enhancements 291
13.1.11.3.2 Deals 291
13.1.12 TESLA, INC. 292
13.1.13 C3.AI, INC. 293
13.1.14 ALPIQ 294
13.1.15 ENEL S.P.A. 295
13.2 STARTUPS/SMES 296
13.2.1 ORIGAMI ENERGY 296
13.2.2 INNOWATTS 297
13.2.3 IRASUS TECHNOLOGIES 298
13.2.4 GRID4C 299
13.2.5 UPLIGHT 300
13.2.6 GRIDBEYOND 301
13.2.7 ESMART SYSTEMS 302
13.2.8 NDUSTRIAL 303
13.2.9 DATATEGY 304
13.2.10 OMDENA 304
13.2.11 BIDGELY 305
13.2.12 AVATHON 306
 
14 ADJACENT/RELATED MARKETS 307
14.1 INTRODUCTION 307
14.2 CONVERSATIONAL AI MARKET 307
14.2.1 MARKET OVERVIEW 307
14.2.2 CONVERSATIONAL AI MARKET, BY OFFERING 308
14.3 SERVICES 308
14.3.1 CONVERSATIONAL AI MARKET, BY SERVICE 308
14.3.2 CONVERSATIONAL AI MARKET, BY BUSINESS FUNCTION 309
14.3.3 CONVERSATIONAL AI MARKET, BY INTEGRATION MODE 310
14.3.4 CONVERSATIONAL AI MARKET, BY VERTICAL 311
14.4 CUSTOMER EXPERIENCE MANAGEMENT MARKET 312
14.4.1 MARKET DEFINITION 312
14.4.2 MARKET OVERVIEW 312
14.4.3 CUSTOMER EXPERIENCE MANAGEMENT MARKET, BY OFFERING 312
14.4.4 CUSTOMER EXPERIENCE MANAGEMENT MARKET, BY DEPLOYMENT TYPE 313
14.4.5 CUSTOMER EXPERIENCE MANAGEMENT MARKET, BY ORGANIZATION SIZE 314
14.4.6 CUSTOMER EXPERIENCE MANAGEMENT MARKET, BY VERTICAL 315
15 APPENDIX 316
15.1 DISCUSSION GUIDE 316
15.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 320
15.3 CUSTOMIZATION OPTIONS 322
15.4 RELATED REPORTS 322
15.5 AUTHOR DETAILS 323