Artificial Intelligence in Building

Artificial Intelligence in Building Market Outlook

The global Artificial Intelligence in Building market is projected to reach approximately USD 50 billion by 2035, growing at a remarkable compound annual growth rate (CAGR) of around 25% from 2025 to 2035. This surge in market size can be attributed to the increasing adoption of AI technologies in the construction industry, as stakeholders seek innovative solutions to enhance operational efficiency, reduce costs, and improve safety measures. The integration of AI in building systems is revolutionizing traditional processes by offering predictive analytics, intelligent automation, and advanced data processing capabilities. Moreover, the growing demand for smart city initiatives and sustainable building practices is driving investments in AI-driven solutions that facilitate energy efficiency and resource management. The emphasis on enhancing user experiences and operational performance further propels the adoption of AI technologies, making it a pivotal element in the construction and management of residential, commercial, and industrial structures.

Growth Factor of the Market

The growth of the Artificial Intelligence in Building market is significantly influenced by a variety of factors that are reshaping the construction landscape. The demand for smart buildings equipped with advanced technologies is on the rise, with stakeholders increasingly emphasizing sustainable and energy-efficient designs. As urbanization continues to accelerate, there is a pressing need for solutions that can optimize resource utilization and enhance building management. The integration of AI technologies facilitates real-time data analysis, enabling predictive maintenance and reducing the risk of equipment failures. Furthermore, the increasing focus on safety and security in buildings is propelling the market growth, as intelligent security systems powered by AI can significantly enhance surveillance and threat detection. The advent of Industry 4.0 has also played a crucial role in transforming traditional building practices into more automated and data-driven processes, further driving the demand for AI solutions in the building sector.

Key Highlights of the Market

• Rapid growth of the AI in building market with a projected CAGR of 25% from 2025 to 2035.
• Increased investment in smart city initiatives and sustainable building practices.
• Growing demand for predictive analytics in facility management and maintenance.
• Significant advancements in intelligent security systems enhancing building safety.
• Accelerated adoption of AI technologies due to urbanization and resource optimization needs.

By Product Type

Smart Building Systems:

Smart building systems represent a significant segment in the artificial intelligence in building market, enabling the automation of various building functions to optimize performance and enhance user experience. These systems integrate various technologies such as sensors, IoT devices, and AI algorithms to create responsive environments that adapt to the needs of occupants. The use of smart building systems not only improves operational efficiency but also contributes to energy savings and cost reductions, thereby making them an attractive option for a variety of building types. With the increasing emphasis on sustainability and energy efficiency, the adoption of smart building systems is expected to rise rapidly, as they offer solutions to monitor and manage energy consumption in real-time, leading to more efficient building operations.

AI-Based Energy Management Systems:

AI-based energy management systems are crucial for optimizing energy usage in buildings, which is an increasingly important consideration for both operational cost and environmental impact. These systems leverage machine learning algorithms to analyze energy consumption patterns, forecast energy demands, and suggest energy-saving measures. By utilizing predictive analytics, organizations can significantly reduce their energy costs and carbon footprint while ensuring compliance with regulatory standards. The ability to seamlessly integrate with existing building management systems enhances the attractiveness of AI-based energy management solutions, making them an essential component in the quest for sustainable and efficient building practices.

Intelligent Security Systems:

Intelligent security systems powered by AI technology are transforming the way buildings approach safety and surveillance. These systems utilize computer vision and machine learning algorithms to enhance threat detection, manage access controls, and monitor premises in real-time. By analyzing data from various sources, such as cameras and sensor networks, intelligent security systems can identify unusual patterns or behaviors, alerting security personnel to potential threats. The growing emphasis on safety in both commercial and residential buildings is driving the demand for advanced security solutions, making this segment a critical area for investment and innovation.

Predictive Maintenance Systems:

Predictive maintenance systems play a pivotal role in ensuring the longevity and reliability of building infrastructure by utilizing AI to predict equipment failures before they occur. By analyzing historical data and real-time performance metrics, these systems can identify warning signs of potential issues, allowing maintenance teams to intervene proactively. This not only reduces downtime and maintenance costs but also enhances the overall efficiency of building operations. As building managers increasingly adopt data-driven strategies for maintenance, the demand for predictive maintenance systems is expected to grow, driven by their ability to significantly improve asset management and operational performance.

Building Automation Systems:

Building automation systems are becoming an integral part of modern building management, allowing for the centralized control of various systems such as HVAC, lighting, and security. These systems utilize AI algorithms to optimize energy consumption and enhance occupant comfort by automatically adjusting settings based on occupancy and environmental conditions. By providing real-time data analysis, building automation systems facilitate informed decision-making, leading to improved operational efficiency and reduced operational costs. As the trend towards smart buildings accelerates, the adoption of building automation systems is expected to see substantial growth, as they provide essential tools for effective building management.

By Application

Residential Buildings:

The application of artificial intelligence in residential buildings is gaining traction as homeowners seek to enhance comfort, security, and energy efficiency. AI-driven solutions enable homeowners to control various aspects of their living environment, including lighting, climate control, and security systems, through smart devices and applications. As the demand for smart homes grows, the integration of AI technologies into residential buildings is expected to increase, allowing homeowners to enjoy convenience and energy savings. Furthermore, advanced AI algorithms can analyze user behavior to provide personalized recommendations for optimizing energy usage, thereby promoting sustainable living practices.

Commercial Buildings:

In commercial buildings, the incorporation of artificial intelligence is transforming operational efficiency and tenant experiences. AI technologies are utilized to monitor and manage building systems, analyze usage patterns, and optimize energy consumption in real-time. This helps property managers reduce operational costs and enhance tenant satisfaction by creating more comfortable and responsive work environments. Additionally, AI-powered analytics tools provide valuable insights into building performance, enabling proactive decision-making and effective resource management. As businesses increasingly recognize the importance of efficient building operations, the adoption of AI in commercial properties is anticipated to grow significantly.

Industrial Buildings:

The adoption of artificial intelligence in industrial buildings is primarily focused on enhancing operational efficiency and safety. AI technologies are applied to monitor machinery and equipment, predict maintenance needs, and optimize production processes. By leveraging data analytics, industrial operators can make informed decisions that reduce downtime and improve productivity. Furthermore, the integration of AI in industrial settings enhances workplace safety by identifying potential hazards and ensuring compliance with safety regulations. As industries continue to embrace automation and data-driven strategies, the role of AI in industrial buildings is expected to expand, driving further advancements in operational efficiency and safety protocols.

Government Buildings:

Government buildings are increasingly adopting artificial intelligence to improve service delivery and operational efficiency. AI technologies enable the automation of various administrative processes, data analysis for better decision-making, and enhanced security through intelligent surveillance systems. By leveraging AI, government agencies can streamline public services, optimize resource allocation, and enhance transparency. The integration of AI in government buildings not only improves operational capabilities but also contributes to the development of smart city initiatives, fostering innovation and promoting citizen engagement. As governments seek to modernize their infrastructure, the adoption of AI in public buildings is set to increase significantly.

Educational Buildings:

The application of artificial intelligence in educational buildings is focused on enhancing learning environments and administrative efficiencies. AI-powered tools are utilized to personalize learning experiences, analyze student performance, and automate administrative tasks. By leveraging data analytics, educational institutions can identify trends and areas for improvement, enabling tailored educational strategies that cater to individual student needs. Additionally, AI-enhanced security systems contribute to the safety of students and staff within educational buildings, providing real-time monitoring and threat detection capabilities. As education continues to evolve, the integration of AI in building management and operations is expected to grow, creating more conducive learning environments.

By Distribution Channel

Direct Sales:

Direct sales remain a prominent distribution channel for artificial intelligence solutions in the building market, allowing manufacturers and service providers to engage with customers and tailor solutions to meet their specific needs. This approach fosters strong relationships between the vendor and the client, facilitating personalized service and support. Direct sales enable companies to demonstrate the capabilities of AI technologies firsthand, ensuring that stakeholders fully understand the benefits and applications of the solutions being offered. As the demand for customized AI solutions continues to rise, direct sales channels are likely to remain a crucial component in the market.

Indirect Sales:

Indirect sales channels, including distributors, resellers, and value-added partners, play a vital role in expanding the reach of artificial intelligence solutions in the building industry. These intermediaries leverage their established networks to provide a wider array of products and services to end-users. By partnering with various stakeholders in the construction and building management industry, indirect sales channels facilitate market penetration and enhance the distribution of AI technologies. The collaboration between manufacturers and indirect sales partners fosters innovation, allowing for the development of tailored solutions that meet the specific demands of diverse client segments.

Online Sales:

Online sales channels have emerged as an increasingly significant distribution method for artificial intelligence solutions in the building market, driven by the convenience and accessibility they offer. The digital marketplace allows customers to explore a wide range of AI products and services, compare features, and make informed purchasing decisions from the comfort of their homes or offices. Additionally, online sales platforms often provide educational resources and customer support, enabling potential buyers to better understand the value of AI technologies in building management. As online shopping continues to gain popularity, the role of e-commerce in distributing AI solutions is expected to expand further.

By Ingredient Type

Machine Learning:

Machine learning is a foundational ingredient in the artificial intelligence in building market, driving the development of intelligent systems capable of learning from data and improving their performance over time. By analyzing vast amounts of data, machine learning algorithms can identify patterns, make predictions, and provide valuable insights to building managers and operators. The ability to adapt to changing conditions and user behavior makes machine learning an indispensable component of various building applications, from energy management to predictive maintenance. As buildings increasingly rely on data-driven decision-making, the significance of machine learning continues to grow within the market.

Natural Language Processing:

Natural language processing (NLP) is an essential ingredient that enhances user interaction with AI-powered building systems. By enabling machines to understand and interpret human language, NLP facilitates seamless communication between occupants and building management systems. This capability allows for voice-activated controls, automated customer support, and more intuitive user interfaces. The integration of NLP in building systems not only improves user experience but also enhances operational efficiencies by simplifying complex tasks. As the demand for user-friendly solutions continues to rise, the role of natural language processing in the artificial intelligence in building market is expected to expand significantly.

Computer Vision:

Computer vision is a critical ingredient in the artificial intelligence in building market, enabling systems to interpret and analyze visual information from the environment. This technology is extensively used in security and surveillance applications, where it can identify and track individuals, detect unauthorized access, and monitor building conditions. Additionally, computer vision can be applied to enhance energy management by analyzing occupancy patterns and optimizing lighting and HVAC systems accordingly. As the capabilities of computer vision continue to evolve, its integration into building management solutions will become increasingly vital, driving further innovation in the market.

Robotics:

Robotics plays a transformative role in the artificial intelligence in building market, enabling automation of various tasks to improve operational efficiency and safety. Robotic systems are increasingly utilized for inspections, maintenance, and monitoring of building infrastructure, allowing for proactive identification of issues and reducing the need for manual labor. The integration of robotics with AI technologies facilitates real-time data collection and analysis, further enhancing building management processes. As industries continue to embrace automation, the demand for robotic solutions in building management is expected to grow, driving advancements in operational capabilities and resource optimization.

Deep Learning:

Deep learning, a subset of machine learning, is gaining traction in the artificial intelligence in building market due to its ability to process complex data sets and improve predictive capabilities. By utilizing neural networks, deep learning algorithms can analyze vast amounts of data to identify intricate patterns and relationships that may not be apparent through traditional analytical methods. In the building sector, deep learning is applied to enhance energy management, optimize security systems, and improve predictive maintenance strategies. As organizations increasingly seek to leverage big data for informed decision-making, the significance of deep learning in building management solutions is expected to expand considerably.

By Region

In North America, the artificial intelligence in building market is poised to witness substantial growth, driven by the region's commitment to technological innovation and the increasing implementation of smart building initiatives. The market in North America is projected to reach approximately USD 20 billion by 2035, owing to the growing demand for energy-efficient solutions and the integration of AI technologies in building management systems. The presence of major technology companies and startups in the region fosters a competitive environment that encourages the development of cutting-edge AI solutions tailored for various building applications. As the region continues to prioritize sustainability and operational efficiency, the adoption of AI in building management is expected to accelerate significantly over the forecast period.

Europe is also experiencing a notable expansion in the artificial intelligence in building market, with a projected market size of around USD 15 billion by 2035. The European market is characterized by stringent regulations regarding energy efficiency and sustainability, prompting building owners and operators to adopt AI-driven solutions that enhance compliance and operational performance. The increasing focus on smart cities and digital transformation initiatives across European nations is further fueling the demand for AI technologies in building management. With a CAGR of 23% during the forecast period, Europe is anticipated to be a key player in advancing artificial intelligence solutions in the building sector, as stakeholders seek innovative strategies to optimize resource usage and enhance occupant experiences.

Opportunities

The artificial intelligence in building market presents numerous opportunities for growth and innovation, particularly in light of the ongoing digital transformation across various industries. One significant opportunity lies in the increasing adoption of smart city initiatives, where AI technologies play a crucial role in enhancing infrastructure efficiency and sustainability. As urbanization continues to rise, municipalities are seeking advanced solutions to manage resources more effectively and improve the quality of life for residents. This opens the door for AI solutions that streamline energy management, optimize traffic flow, and enhance public safety within urban environments. Moreover, partnerships between technology providers and building owners represent a lucrative opportunity, allowing for the co-creation of tailored solutions that address specific building management challenges, ultimately driving further market growth.

Additionally, the proliferation of IoT devices and sensors in building management creates exciting opportunities for AI integration. As more buildings become interconnected through IoT technologies, the volume of data generated increases exponentially, providing a rich source of insights for AI algorithms to analyze. This data-driven approach enables more accurate predictive analytics for maintenance, energy consumption, and occupancy patterns, further enhancing operational efficiencies. Furthermore, advancements in cloud computing and edge computing technologies facilitate the deployment of AI solutions at scale, making them more accessible to a wider range of building types and sizes. As these trends continue to evolve, the artificial intelligence in building market is poised for substantial growth, driven by innovation and collaboration among key stakeholders.

Threats

Despite the promising outlook for the artificial intelligence in building market, several threats could impede its growth and adoption. One of the primary challenges is the concern regarding data privacy and security, as the integration of AI technologies often involves the collection and analysis of sensitive information. Building owners and operators may hesitate to adopt AI solutions if they perceive risks associated with data breaches or unauthorized access to confidential data. Furthermore, regulatory compliance regarding data protection laws can pose significant obstacles, requiring companies to invest in robust security measures and protocols to mitigate potential risks. As cybersecurity threats continue to evolve, stakeholders in the building industry must prioritize data security to build trust and encourage the widespread adoption of AI technologies.

Additionally, the rapid pace of technological advancements presents a potential threat to the artificial intelligence in building market. As new technologies emerge, the market landscape may become increasingly competitive, forcing companies to continuously innovate to remain relevant. This can lead to challenges in keeping up with evolving customer expectations and demands for AI solutions that offer greater efficiency and performance. The need for ongoing investment in research and development, as well as workforce training, can also strain resources for companies operating in this space. To navigate these challenges, stakeholders must remain agile and responsive to technological changes while prioritizing customer-centric solutions that address the evolving needs of the market.

Competitor Outlook

• IBM Corporation
• Microsoft Corporation
• Siemens AG
• Honeywell International Inc.
• Schneider Electric SE
• Johnson Controls International plc
• Oracle Corporation
• Amazon Web Services, Inc.
• Hitachi, Ltd.
• Cisco Systems, Inc.
• ABB Ltd.
• BuildingIQ Inc.
• Verdantix
• C3.ai
• Ubisense Group plc

The competitive landscape of the artificial intelligence in building market is characterized by a diverse array of companies, ranging from established technology giants to innovative startups. Leading players such as IBM, Microsoft, and Siemens are driving advancements in AI technologies, investing heavily in research and development to enhance their offerings. These companies leverage extensive resources and expertise to develop comprehensive AI solutions that cater to various building applications, including energy management, predictive maintenance, and security systems. Furthermore, strategic partnerships and collaborations among industry players are becoming increasingly common, as companies seek to combine their strengths and deliver integrated solutions that address the complexities of modern building management.

Additionally, the market landscape includes a growing number of startups and niche players that focus on specific aspects of AI in building management. Companies like BuildingIQ and C3.ai are innovating with unique solutions that address specific market needs, such as energy efficiency and data analytics. These emerging players are often agile and responsive to market changes, allowing them to capitalize on new opportunities and quickly adapt to evolving customer demands. As competition intensifies, established companies are increasingly looking to acquire or partner with startups to enhance their technological capabilities and expand their market reach.

In summary, the artificial intelligence in building market is poised for significant growth, fueled by the increasing demand for smart and sustainable building solutions. As companies continue to innovate and adapt to emerging trends, the competitive landscape will evolve, resulting in dynamic partnerships and collaborations that drive advancements in technology. Major players will need to focus on delivering value-added solutions that meet the diverse needs of building owners and operators while addressing the challenges associated with data privacy and security. With continued investment in R&D and a commitment to customer-centric approaches, stakeholders in the market are well-positioned to thrive in this rapidly changing environment.

• 1 Appendix

  • 1.1 List of Tables
  • 1.2 List of Figures

• 2 Introduction

  • 2.1 Market Definition
  • 2.2 Scope of the Report
  • 2.3 Study Assumptions
  • 2.4 Base Currency & Forecast Periods

• 3 Market Dynamics

  • 3.1 Market Growth Factors
  • 3.2 Economic & Global Events
  • 3.3 Innovation Trends
  • 3.4 Supply Chain Analysis

• 4 Consumer Behavior

  • 4.1 Market Trends
  • 4.2 Pricing Analysis
  • 4.3 Buyer Insights

• 5 Key Player Profiles

  • 5.1 C3.ai
    • 5.1.1 Business Overview
    • 5.1.2 Products & Services
    • 5.1.3 Financials
    • 5.1.4 Recent Developments
    • 5.1.5 SWOT Analysis
  • 5.2 ABB Ltd.
    • 5.2.1 Business Overview
    • 5.2.2 Products & Services
    • 5.2.3 Financials
    • 5.2.4 Recent Developments
    • 5.2.5 SWOT Analysis
  • 5.3 Verdantix
    • 5.3.1 Business Overview
    • 5.3.2 Products & Services
    • 5.3.3 Financials
    • 5.3.4 Recent Developments
    • 5.3.5 SWOT Analysis
  • 5.4 Siemens AG
    • 5.4.1 Business Overview
    • 5.4.2 Products & Services
    • 5.4.3 Financials
    • 5.4.4 Recent Developments
    • 5.4.5 SWOT Analysis
  • 5.5 Hitachi, Ltd.
    • 5.5.1 Business Overview
    • 5.5.2 Products & Services
    • 5.5.3 Financials
    • 5.5.4 Recent Developments
    • 5.5.5 SWOT Analysis
  • 5.6 BuildingIQ Inc.
    • 5.6.1 Business Overview
    • 5.6.2 Products & Services
    • 5.6.3 Financials
    • 5.6.4 Recent Developments
    • 5.6.5 SWOT Analysis
  • 5.7 IBM Corporation
    • 5.7.1 Business Overview
    • 5.7.2 Products & Services
    • 5.7.3 Financials
    • 5.7.4 Recent Developments
    • 5.7.5 SWOT Analysis
  • 5.8 Oracle Corporation
    • 5.8.1 Business Overview
    • 5.8.2 Products & Services
    • 5.8.3 Financials
    • 5.8.4 Recent Developments
    • 5.8.5 SWOT Analysis
  • 5.9 Ubisense Group plc
    • 5.9.1 Business Overview
    • 5.9.2 Products & Services
    • 5.9.3 Financials
    • 5.9.4 Recent Developments
    • 5.9.5 SWOT Analysis
  • 5.10 Cisco Systems, Inc.
    • 5.10.1 Business Overview
    • 5.10.2 Products & Services
    • 5.10.3 Financials
    • 5.10.4 Recent Developments
    • 5.10.5 SWOT Analysis
  • 5.11 Microsoft Corporation
    • 5.11.1 Business Overview
    • 5.11.2 Products & Services
    • 5.11.3 Financials
    • 5.11.4 Recent Developments
    • 5.11.5 SWOT Analysis
  • 5.12 Schneider Electric SE
    • 5.12.1 Business Overview
    • 5.12.2 Products & Services
    • 5.12.3 Financials
    • 5.12.4 Recent Developments
    • 5.12.5 SWOT Analysis
  • 5.13 Amazon Web Services, Inc.
    • 5.13.1 Business Overview
    • 5.13.2 Products & Services
    • 5.13.3 Financials
    • 5.13.4 Recent Developments
    • 5.13.5 SWOT Analysis
  • 5.14 Honeywell International Inc.
    • 5.14.1 Business Overview
    • 5.14.2 Products & Services
    • 5.14.3 Financials
    • 5.14.4 Recent Developments
    • 5.14.5 SWOT Analysis
  • 5.15 Johnson Controls International plc
    • 5.15.1 Business Overview
    • 5.15.2 Products & Services
    • 5.15.3 Financials
    • 5.15.4 Recent Developments
    • 5.15.5 SWOT Analysis

• 6 Market Segmentation

  • 6.1 Artificial Intelligence in Building Market, By Application
    • 6.1.1 Residential Buildings
    • 6.1.2 Commercial Buildings
    • 6.1.3 Industrial Buildings
    • 6.1.4 Government Buildings
    • 6.1.5 Educational Buildings
  • 6.2 Artificial Intelligence in Building Market, By Product Type
    • 6.2.1 Smart Building Systems
    • 6.2.2 AI-Based Energy Management Systems
    • 6.2.3 Intelligent Security Systems
    • 6.2.4 Predictive Maintenance Systems
    • 6.2.5 Building Automation Systems
  • 6.3 Artificial Intelligence in Building Market, By Ingredient Type
    • 6.3.1 Machine Learning
    • 6.3.2 Natural Language Processing
    • 6.3.3 Computer Vision
    • 6.3.4 Robotics
    • 6.3.5 Deep Learning
  • 6.4 Artificial Intelligence in Building Market, By Distribution Channel
    • 6.4.1 Direct Sales
    • 6.4.2 Indirect Sales
    • 6.4.3 Online Sales

• 7 Competitive Analysis

  • 7.1 Key Player Comparison
  • 7.2 Market Share Analysis
  • 7.3 Investment Trends
  • 7.4 SWOT Analysis

• 8 Research Methodology

  • 8.1 Analysis Design
  • 8.2 Research Phases
  • 8.3 Study Timeline

• 9 Future Market Outlook

  • 9.1 Growth Forecast
  • 9.2 Market Evolution

• 10 Geographical Overview

  • 10.1 Europe - Market Analysis
    • 10.1.1 By Country
      • 10.1.1.1 UK
      • 10.1.1.2 France
      • 10.1.1.3 Germany
      • 10.1.1.4 Spain
      • 10.1.1.5 Italy
  • 10.2 Asia Pacific - Market Analysis
    • 10.2.1 By Country
      • 10.2.1.1 India
      • 10.2.1.2 China
      • 10.2.1.3 Japan
      • 10.2.1.4 South Korea
  • 10.3 Latin America - Market Analysis
    • 10.3.1 By Country
      • 10.3.1.1 Brazil
      • 10.3.1.2 Argentina
      • 10.3.1.3 Mexico
  • 10.4 North America - Market Analysis
    • 10.4.1 By Country
      • 10.4.1.1 USA
      • 10.4.1.2 Canada
  • 10.5 Middle East & Africa - Market Analysis
    • 10.5.1 By Country
      • 10.5.1.1 Middle East
      • 10.5.1.2 Africa
  • 10.6 Artificial Intelligence in Building Market by Region

• 11 Global Economic Factors

  • 11.1 Inflation Impact
  • 11.2 Trade Policies

• 12 Technology & Innovation

  • 12.1 Emerging Technologies
  • 12.2 AI & Digital Trends
  • 12.3 Patent Research

• 13 Investment & Market Growth

  • 13.1 Funding Trends
  • 13.2 Future Market Projections

• 14 Market Overview & Key Insights

  • 14.1 Executive Summary
  • 14.2 Key Trends
  • 14.3 Market Challenges
  • 14.4 Regulatory Landscape

Segments Analyzed in the Report

The global Artificial Intelligence in Building market is categorized based on

By Product Type

• Smart Building Systems
• AI-Based Energy Management Systems
• Intelligent Security Systems
• Predictive Maintenance Systems
• Building Automation Systems

By Application

• Residential Buildings
• Commercial Buildings
• Industrial Buildings
• Government Buildings
• Educational Buildings

By Distribution Channel

• Direct Sales
• Indirect Sales
• Online Sales

By Ingredient Type

• Machine Learning
• Natural Language Processing
• Computer Vision
• Robotics
• Deep Learning

By Region

• North America
• Europe
• Asia Pacific
• Latin America
• Middle East & Africa

Key Players

• IBM Corporation
• Microsoft Corporation
• Siemens AG
• Honeywell International Inc.
• Schneider Electric SE
• Johnson Controls International plc
• Oracle Corporation
• Amazon Web Services, Inc.
• Hitachi, Ltd.
• Cisco Systems, Inc.
• ABB Ltd.
• BuildingIQ Inc.
• Verdantix
• C3.ai
• Ubisense Group plc