Global AI in Smart Buildings and Infrastructure Market Size is valued at USD 40.1 Bn 2024 and is predicted to reach USD 338.5 Bn by the year 2034 at a 23.9% CAGR during the forecast period for 2025-2034.
Artificial Intelligence (AI) in Smart Buildings and Infrastructure use advanced algorithms to efficiently control energy consumption, boost security measures, and enhance the overall comfort of occupants. Notable uses encompass energy management, intelligent lighting, anticipatory maintenance, and surveillance monitoring. Advantages encompass decreased energy expenses, heightened safety, and higher tenant satisfaction. Nevertheless, in order to achieve wider acceptance, it is imperative to tackle obstacles such as the interface with current systems, data privacy concerns, and significant upfront expenses.
Moreover, AI is driving innovation in urban infrastructure planning and management. By analyzing diverse data sources such as traffic patterns, air quality levels, and social media sentiment, AI algorithms can provide valuable insights to urban planners and policymakers. These insights enable informed decision-making regarding infrastructure development, transportation systems, and resource allocation, leading to more sustainable and resilient cities.
The AI in Smart Buildings and Infrastructure market is categorized into product and application. The type segment is categorized into software, hardware, and services. As per the application segment, the market is divided into Building Energy Management, HVAC Control and Optimization, Security and Surveillance, Predictive Maintenance, Occupancy and Space Management, Lighting Control and Optimization, Emergency Management and Response, and Others. The end users segment comprises commercial buildings, residential buildings, industrial buildings, government buildings, healthcare facilities, educational institutions, retail spaces, and others. Based on AI Technology, the market is segmented into Machine Learning, Computer Vision, Natural Language Processing, Deep Learning, Neural Networks, and Others.
The software category is expected to hold a large share in the global AI in Smart Buildings and Infrastructure market. This segment includes solutions for energy management, security, building automation, and facility management. AI-driven software optimizes building operations by analyzing data from various sensors and systems, leading to enhanced efficiency, reduced operational costs, as well as improved occupant comfort. With advancements in machine learning and IoT integration, the demand for AI-based software in smart buildings is on the rise. The software's ability to predict maintenance needs, optimize energy usage, and enhance security systems makes it a critical component of smart infrastructure. This segment is expected to see significant investment and innovation, driving the overall growth of AI in the smart Buildings and Infrastructure Market.
The Building Energy Management segment is predicted to grow at a rapid rate in the global AI in Smart Buildings and Infrastructure market owing to its pivotal role in enhancing energy efficiency and sustainability. AI-driven BEM systems optimize energy consumption by analyzing data from sensors and smart meters to adjust lighting, cooling, heating, and other systems in real-time. These systems reduce operational costs and environmental impact by predicting energy needs, identifying inefficiencies, and enabling proactive maintenance. With the integration of renewable energy sources, AI can further balance energy loads and storage. The adoption of AI in BEM is driven by the growing demand for green buildings, regulatory requirements for energy efficiency, and the need for cost-effective energy management solutions, making it a critical component of the smart building ecosystem.
The North American AI in Smart Buildings and Infrastructure market is estimated to report the highest market revenue share in terms of revenue in the near future. It can be attributed to rapid technological advancements and a strong emphasis on sustainable development. The region's mature technological landscape and high adoption rates of AI-driven solutions contribute to its leading market position. The increasing focus on energy efficiency, smart city initiatives, and enhanced building management systems drive the demand for AI in this sector. Furthermore, government regulations and incentives for smart infrastructure development bolster market growth. North America's well-established infrastructure, coupled with significant investments in research and development, positions it as a dominant player in the AI in Smart Buildings and Infrastructure market, with projections indicating continued high growth in the coming years.
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Report Attribute |
Specifications |
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Market Size Value In 2024 |
USD 40.1 Bn |
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Revenue Forecast In 2034 |
USD 338.5 Bn |
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Growth Rate CAGR |
CAGR of 23.9% from 2025 to 2034 |
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Quantitative Units |
Representation of revenue in US$ Bn and CAGR from 2025 to 2034 |
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Historic Year |
2021 to 2024 |
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Forecast Year |
2025-2034 |
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Report Coverage |
The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
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Segments Covered |
By Type, Application |
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Regional Scope |
North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
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Country Scope |
U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea |
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Competitive Landscape |
IBM Corporation, Siemens AG, Honeywell International Inc., Johnson Controls International plc, Schneider Electric SE, ABB Ltd., Intel Corporation, Microsoft Corporation, Google LLC, Cisco Systems, Inc., Huawei Technologies Co., Ltd., and Amazon Web Services, Inc. |
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Customization Scope |
Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
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Pricing And Available Payment Methods |
Explore pricing alternatives that are customized to your particular study requirements. |
AI in Smart Buildings and Infrastructure Market By Type-
AI in Smart Buildings and Infrastructure Market By Application-
AI in Smart Buildings and Infrastructure Market By End-User
AI in Smart Buildings and Infrastructure Market By AI Technology
AI in Smart Buildings and Infrastructure Market By Region-
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
InsightAce Analytic follows a standard and comprehensive market research methodology focused on offering the most accurate and precise market insights. The methods followed for all our market research studies include three significant steps – primary research, secondary research, and data modeling and analysis - to derive the current market size and forecast it over the forecast period. In this study, these three steps were used iteratively to generate valid data points (minimum deviation), which were cross-validated through multiple approaches mentioned below in the data modeling section.
Through secondary research methods, information on the market under study, its peer, and the parent market was collected. This information was then entered into data models. The resulted data points and insights were then validated by primary participants.
Based on additional insights from these primary participants, more directional efforts were put into doing secondary research and optimize data models. This process was repeated till all data models used in the study produced similar results (with minimum deviation). This way, this iterative process was able to generate the most accurate market numbers and qualitative insights.
Secondary research
The secondary research sources that are typically mentioned to include, but are not limited to:
The paid sources for secondary research like Factiva, OneSource, Hoovers, and Statista
Primary Research:
Primary research involves telephonic interviews, e-mail interactions, as well as face-to-face interviews for each market, category, segment, and subsegment across geographies
The contributors who typically take part in such a course include, but are not limited to:
Data Modeling and Analysis:
In the iterative process (mentioned above), data models received inputs from primary as well as secondary sources. But analysts working on these models were the key. They used their extensive knowledge and experience about industry and topic to make changes and fine-tuning these models as per the product/service under study.
The standard data models used while studying this market were the top-down and bottom-up approaches and the company shares analysis model. However, other methods were also used along with these – which were specific to the industry and product/service under study.
