The global Artificial Intelligence in Smart Buildings and Infrastructure Market size was valued at USD 27.4 billion in 2022 and is projected to expand at a compound annual growth rate (CAGR) of 24.3% during the forecast period, reaching a value of USD 170.88 billion by 2030.
Artificial Intelligence in Smart Buildings and Infrastructure Market research report by Future Data Stats, offers a comprehensive view of the market's historical data from 2017 to 2021, capturing trends, growth patterns, and key drivers. It establishes 2021 as the base year, analyzing the market landscape, consumer behavior, competition, and regulations. Additionally, the report presents a well-researched forecast period from 2022 to 2030, leveraging data analysis techniques to project the market's growth trajectory, emerging opportunities, and anticipated challenges.
MARKET OVERVIEW:
Artificial Intelligence in Smart Buildings and Infrastructure refers to the integration of advanced technologies and algorithms to create intelligent, automated, and energy-efficient systems within buildings and infrastructure. By harnessing AI capabilities like machine learning, natural language processing, and computer vision, these smart systems can autonomously optimize various aspects, such as energy consumption, HVAC control, security, and maintenance. AI-powered solutions enable real-time data analysis and predictive insights, allowing building managers and occupants to make informed decisions, enhance operational efficiency, and create more comfortable and sustainable environments.
In essence, Artificial Intelligence in Smart Buildings and Infrastructure represents a revolutionary approach to modernizing traditional construction and facility management practices. By embracing AI technologies, buildings can become more adaptive and responsive, catering to the needs of occupants and adapting to changing environmental conditions. The integration of AI not only enhances energy efficiency and cost savings but also improves overall safety, comfort, and convenience, making it a pivotal component of the future of sustainable and intelligent infrastructure.
MARKET DYNAMICS:
One of the primary drivers is the growing need for energy efficiency and sustainability in buildings. AI-powered systems can optimize energy consumption by intelligently adjusting lighting, heating, and cooling based on occupancy patterns and external factors, leading to significant cost savings and reduced environmental impact. Additionally, the rising demand for smart and connected buildings that offer enhanced comfort and convenience to occupants is fueling the adoption of AI technologies. Smart buildings equipped with AI-driven applications can provide personalized experiences, such as intelligent climate control and seamless automation of various tasks, elevating the overall living and working experience.
Despite the numerous drivers, the market for AI in Smart Buildings and Infrastructure also faces certain challenges and restraints. One of the major restraints is the high initial cost of implementing AI solutions and the need for significant infrastructure upgrades. Integrating AI systems requires substantial investments in hardware, software, and skilled professionals, which can deter some building owners and operators from adoption. Moreover, concerns about data privacy and security pose another challenge to widespread AI implementation. With the vast amounts of data collected by AI systems, there is a need to ensure robust cybersecurity measures and compliance with data protection regulations to gain trust and confidence from users. However, amidst these challenges lie significant opportunities. The increasing focus on smart city initiatives and sustainable urban development presents a promising opportunity for AI in Smart Buildings and Infrastructure. As governments and industries prioritize smart and green solutions, the demand for AI-driven smart buildings is likely to surge. Furthermore, advancements in AI technologies, coupled with the growing ecosystem of IoT devices, present opportunities to create more sophisticated and interconnected smart building systems, further enhancing their efficiency and effectiveness.
AI IN SMART BUILDINGS AND INFRASTRUCTURE MARKET SEGMENTAL ANALYSIS
BY TYPE:
Firstly, AI-powered software plays a pivotal role in managing and optimizing various building functions, including energy management, occupancy tracking, and predictive maintenance. Secondly, AI hardware, such as smart sensors and IoT devices, enables the collection of real-time data, essential for informed decision-making and system automation. Finally, AI services, provided by skilled professionals and technology vendors, offer consultation, implementation, and maintenance support to ensure seamless integration and operation of AI systems.
BY APPLICATION:
Building Energy Management is a key area where AI optimizes energy usage and reduces costs by intelligently controlling systems based on occupancy patterns and environmental conditions. HVAC Control and Optimization leverage AI algorithms to regulate heating and cooling, ensuring optimal comfort and energy efficiency. Security and Surveillance benefit from AI-driven analytics, enhancing threat detection and response through smart monitoring and video analysis. Predictive Maintenance employs AI to forecast equipment failures, enabling proactive maintenance and minimizing downtime. Occupancy and Space Management optimize space utilization, enabling flexible and efficient workspace arrangements. Lighting Control and Optimization utilize AI to adjust lighting levels based on natural light and occupancy, saving energy while ensuring comfort. AI aids in Emergency Management and Response by automating emergency protocols and facilitating faster, more effective responses.
BY END-USER:
In Commercial Buildings, AI-powered systems offer cost-effective energy management solutions, enhancing operational efficiency and occupant comfort. Residential Buildings benefit from AI technologies that automate tasks, enhance security, and provide personalized experiences for homeowners. In Industrial Buildings, AI enables predictive maintenance, minimizing downtime and optimizing production processes. Government Buildings leverage AI for smart energy management and resource optimization, contributing to sustainability goals. Healthcare Facilities benefit from AI-powered systems that enhance patient safety, optimize resource allocation, and improve overall healthcare delivery. Educational Institutions utilize AI to create smart and interactive learning environments, enhancing student experiences. Retail Spaces employ AI-driven analytics for personalized customer experiences and efficient inventory management.
BY AI TECHNOLOGY:
Machine Learning, a key AI technology, enables smart systems to learn from data and adapt autonomously, optimizing energy consumption and predictive maintenance. Natural Language Processing enhances human-machine communication, facilitating voice-controlled smart building systems and intelligent virtual assistants. Computer Vision enables AI-powered surveillance and analytics, enhancing security and safety within buildings. Deep Learning, a subset of Machine Learning, empowers AI systems to process complex data and make sophisticated decisions, improving overall building performance. Neural Networks play a critical role in pattern recognition and data processing, enhancing the efficiency and accuracy of AI-driven solutions.
REGIONAL ANALYSIS:
In North America, the market is driven by early adoption of advanced technologies and strong investments in smart building initiatives. Europe is experiencing significant growth due to strict energy efficiency regulations and a focus on sustainable infrastructure. Asia Pacific is witnessing rapid expansion fueled by the increasing urbanization, rising demand for smart cities, and government initiatives supporting smart building projects.
Latin America is also embracing AI in smart buildings, driven by efforts to enhance energy efficiency and create modern, connected spaces. In the Middle East and Africa, smart building projects are gaining traction, supported by investments in infrastructure and a growing awareness of the benefits of AI integration.
COVID-19 IMPACT:
The COVID-19 pandemic had a significant impact on the Artificial Intelligence in Smart Buildings and Infrastructure market. During the pandemic, there was an increased focus on health and safety, leading to a heightened demand for AI-driven systems to monitor and manage building occupancy, ventilation, and hygiene. As many people shifted to remote work and businesses implemented social distancing measures, the need for AI-powered solutions for remote building management and touchless controls became evident. On the other hand, the economic challenges posed by the pandemic resulted in budget constraints for some building projects, affecting the rate of AI adoption. However, the crisis also accelerated digital transformation efforts, prompting industries to seek more efficient and automated solutions.
INDUSTRY ANALYSIS:
Mergers & Acquisitions:
- In January 2022, Johnson Controls completed the acquisition of FogHorn, the leading developer of Edge AI software for industrial and commercial Internet of Things (IoT) solutions.
- In February 2022, Legrand partnered with URC, a leading provider of smart home automation and control systems, to expand Smart Home Control.
Product Launches:
- In March 2022, IBM launched Watson for Buildings, a suite of AI-powered solutions that help buildings become more efficient, sustainable, and secure.
- In April 2022, Siemens launched MindSphere for Buildings, a cloud-based platform that enables buildings to collect, analyze, and act on data in real time.
- In May 2022, Honeywell launched its new suite of smart building solutions, which include AI-powered features for energy management, security, and occupant comfort.
KEY MARKET PLAYERS:
- Siemens AG
- Honeywell International Inc.
- Johnson Controls International plc
- Schneider Electric SE
- IBM Corporation
- Cisco Systems, Inc.
- Microsoft Corporation
- Intel Corporation
- Google LLC
- Amazon Web Services, Inc.
- General Electric Company
- ABB Ltd.
- Oracle Corporation
- Huawei Technologies Co., Ltd.
- Hitachi, Ltd.
- Bosch Building Technologies
- Johnson & Johnson Services, Inc.
- Carrier Global Corporation
- SAP SE
- Honeywell Building Solutions
- Philips Lighting (Signify)
- Emerson Electric Co.
- NEC Corporation
- Siemens Building Technologies
- Lutron Electronics Co., Inc.
- others
Table of Contents
Introduction
1.1. Overview of Artificial Intelligence in Smart Buildings and Infrastructure
1.2. Market Definition and Scope
1.3. Research Methodology
Executive Summary
Market Dynamics
3.1. Market Drivers
3.2. Market Restraints
3.3. Market Opportunities
3.4. Industry Challenges
Key AI Technologies for Smart Buildings and Infrastructure
Market Segmentation
5.1. By Type
5.1.1. Software
5.1.2. Hardware
5.1.3. Services
5.2. By Application
5.2.1. Building Energy Management
5.2.2. HVAC Control and Optimization
5.2.3. Security and Surveillance
5.2.4. Predictive Maintenance
5.2.5. Occupancy and Space Management
5.2.6. Lighting Control and Optimization
5.2.7. Emergency Management and Response
5.2.8. Others
5.3. By End-User
5.3.1. Commercial Buildings
5.3.2. Residential Buildings
5.3.3. Industrial Buildings
5.3.4. Government Buildings
5.3.5. Healthcare Facilities
5.3.6. Educational Institutions
5.3.7. Retail Spaces
5.3.8. Others
5.4. By Region
5.4.1. North America
5.4.2. Europe
5.4.3. Asia-Pacific
5.4.4. Latin America
5.4.5. Middle East and Africa
Industry Trends and Innovations
Competitive Landscape
7.1. Market Share Analysis
7.2. Key Players' Profiles
Recent Mergers, Acquisitions, and Partnerships
Regional Analysis
Future Outlook and Growth Opportunities
Conclusion
Artificial Intelligence in Smart Buildings And Infrastructure Market Segmentation
By Type:
- Software
- Hardware
- Services
By Application:
- Building Energy Management
- HVAC Control and Optimization
- Security and Surveillance
- Predictive Maintenance
- Occupancy and Space Management
- Lighting Control and Optimization
- Emergency Management and Response
- Others
By End-User:
- Commercial Buildings
- Residential Buildings
- Industrial Buildings
- Government Buildings
- Healthcare Facilities
- Educational Institutions
- Retail Spaces
- Others
By AI Technology:
- Machine Learning
- Natural Language Processing
- Computer Vision
- Deep Learning
- Neural Networks
- Others
By Geography:
- North America (USA, Canada, Mexico)
- Europe (Germany, UK, France, Russia, Italy, Rest of Europe)
- Asia-Pacific (China, Japan, South Korea, India, Southeast Asia, Rest of Asia-Pacific)
- South America (Brazil, Argentina, Columbia, Rest of South America)
- Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria, South Africa, Rest of MEA)
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RESEARCH METHODOLOGY
With a collective industry experience of about 70 years of analysts and experts, Future Data Stats encompasses the most infallible research methodology for its market intelligence and industry analysis. Not only does the company dig deep into the innermost levels of the market, but also examines the minutest details for its market estimates and forecasts.
This approach helps build a greater market-specific view of size, shape, and industry trends within each industry segment. Various industry trends and real-time developments are factored into identifying key growth factors and the future course of the market. The research proceeds are the results of high-quality data, expert views & analysis, and valuable independent opinions. The research process is designed to deliver a balanced view of the global markets and allows stakeholders to make informed decisions, to attain their highest growth objectives.
Future Data Stats offers its clients exhaustive research and analysis, based on a wide variety of factual inputs, which largely include interviews with industry participants, reliable statistics, and regional intelligence. The in-house industry experts play an instrumental role in designing analytic tools and models, tailored to the requirements of a particular industry segment. These analytical tools and models distill the data & statistics and enhance the accuracy of our recommendations and advice.
With Future Data Stats calibrated research process and 360° data-evaluation methodology, the clients receive:
- Consistent, valuable, robust, and actionable data & analysis that can easily be referenced for strategic business planning
- Technologically sophisticated and reliable insights through a well-audited and veracious research methodology
- Sovereign research proceeds that present a tangible depiction of the marketplace
With this strong methodology, Future Data Stats ensures that its research and analysis is most reliable and guarantees sound business planning.
The research methodology of the global market involves extensive primary and secondary research. Primary research includes about 24 hours of interviews and discussions with a wide range of stakeholders that include upstream and downstream participants. Primary research typically is a bulk of our research efforts, coherently supported by extensive secondary research. Over 3000 product literature, industry releases, annual reports, and other such documents of key industry participants have been reviewed to obtain a better market understanding and gain enhanced competitive intelligence. In addition, authentic industry journals, trade associations’ releases, and government websites have also been reviewed to generate high-value industry insights.
Primary Research:
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Primary Research
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Desk Research
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Company Analysis
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• Identify key opinion leaders • Questionnaire design • In-depth Interviews • Coverage across the value chain
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• Company Website • Company Annual Reports • Paid Databases • Financial Reports
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• Market Participants • Key Strengths • Product Portfolio • Mapping as per Value Chain • Key focus segment
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Primary research efforts include reaching out to participants through emails, telephonic conversations, referrals, and professional corporate relations with various companies that make way for greater flexibility in reaching out to industry participants and commentators for interviews and discussions.
The aforementioned helps to:
- Validate and improve data quality and strengthen the research proceeds
- Develop a market understanding and expertise
- Supply authentic information about the market size, share, growth, and forecasts
The primary research interview and discussion panels comprise experienced industry personnel.
These participants include, but are not limited to:
- Chief executives and VPs of leading corporations specific to an industry
- Product and sales managers or country heads; channel partners & top-level distributors; banking, investments, and valuation experts
- Key opinion leaders (KOLs)
Secondary Research:
A broad array of industry sources for the secondary research typically includes, but is not limited to:
- Company SEC filings, annual reports, company websites, broker & financial reports, and investor presentations for a competitive scenario and shape of the industry
- Patent and regulatory databases to understand technical & legal developments
- Scientific and technical writings for product information and related preemptions
- Regional government and statistical databases for macro analysis
- Authentic news articles, web-casts, and other related releases to evaluate the market
- Internal and external proprietary databases, key market indicators, and relevant press releases for market estimates and forecasts
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PRIMARY SOURCES |
DATA SOURCES |
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• Top executives of end-use industries • C-level executives of the leading Parenteral Nutrition companies • Sales manager and regional sales manager of the Parenteral Nutrition companies • Industry Consultants • Distributors/Suppliers
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• Annual Reports • Presentations • Company Websites • Press Releases • News Articles • Government Agencies’ Publications • Industry Publications • Paid Databases
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Analyst Tools and Models:
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BOTTOM-UP APPROACH |
TOP-DOWN APPROACH |
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· Arriving at · Arriving at · Market Share · Key Market Players |
· Key Market Players · Market Share · Arriving at · Arriving at |
Artificial Intelligence in Smart Buildings and Infrastructure Market Dynamic Factors
Drivers:
- Growing demand for energy-efficient and sustainable buildings.
- Increasing adoption of IoT and smart technologies in the construction industry.
- Need for optimized building operations and resource management.
- Rising focus on enhancing occupant comfort and experience.
- Government initiatives promoting smart city and green building projects.
Restraints:
- High initial implementation cost and infrastructure requirements.
- Lack of skilled workforce and expertise in AI integration.
- Data privacy and security concerns with AI-powered systems.
- Resistance to change and traditional building management practices.
- Interoperability challenges among different AI technologies and devices.
Opportunities:
- Advancements in AI technologies, leading to more sophisticated applications.
- Growing demand for AI-driven predictive maintenance and analytics.
- Expansion of smart building projects in emerging economies.
- Integration of AI with renewable energy sources for sustainable buildings.
- Potential for AI-powered autonomous and self-learning buildings.
Challenges:
- Ensuring seamless integration of AI with existing building systems.
- Addressing ethical and regulatory implications of AI in buildings.
- Balancing energy efficiency with occupant preferences and comfort.
- Overcoming interoperability issues among AI vendors and platforms.
- Navigating uncertainties and market fluctuations caused by external factors like economic conditions or global events.
Frequently Asked Questions
