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智慧商业建筑人工智慧 (AI) 的全球市场：2024 年
市场调查报告书
商品编码
1448836

智慧商业建筑人工智慧 (AI) 的全球市场：2024 年

The Market for AI in Smart Commercial Buildings 2024
出版日期: | 出版商: Memoori | 英文 Spreadsheet, 164 Pages, 18 Charts, Presentation Slides | 商品交期: 最快1-2个工作天内

价格

本报告是截至 2024 年的最新分析，探讨了人工智慧 (AI) 在商业建筑市场中日益增长的作用。

这项最新分析以Memoori 的2021 年人工智慧(AI) 市场分析为基础，探讨了AI 的整体功能和专业应用，以实现更智慧、更永续、反应更灵敏的建筑环境。重点关注和分析两者所发生的变化。

该报告是由两部分组成的系列报告中的第一篇，关于人工智慧市场前景的第二部分预计将于今年稍后发布。 这两份报告均包含在我们的 2024 年Premium Subscription Service中，其中还包括对我们的ChatbotAIM的访问，该服务利用大规模语言模型 (LLM) 来搜寻我们的所有分析报告。

本报告回答的关键问题

  • 我们在 "真正的认知建设" 之路上处于什么位置？ 当今的商业建筑技术正在从基于规则的分析过渡到人工智慧预测机器学习模型，但采用率仍然有限。实际实施的范围仍然很窄，并且仍在能源优化、空间利用和安全等易于理解的领域内。
  • 是什么阻碍了人工智慧的广泛采用？阻碍人工智慧广泛采用的课题包括与遗留系统的技术整合、缺乏必要的专业技能和广泛的教育，以及有效利用人工智慧解决方案所必需的新流程的缓慢采用。 。
  • 商业建筑人工智慧市场中期内将如何成长？智慧建筑人工智慧市场预计到 2028 年将以 25.5% 的复合年增长率成长，达到 64.8 亿美元。

这份报告以 164 页的文字和 18 个图表，提炼了所有重要事实并得出结论，让您能够准确地瞭解人工智慧技术如何以及为何应用于商业建筑；

  • 透过广泛的分析，Memoori 将 66 个不同的人工智慧用例分为 12 个主要领域。在每个领域，智慧建筑的解决方案都在积极开发和商业化。这些涵盖了广泛的潜在好处，从促进永续性和能源效率到增强安全性和客製化居住者体验。
  • 大型科技公司正在投入数亿美元进行 "人工智慧军备竞赛" 。这正在推动快速创新，使每个行业受益。例如， ARK Invest 的研究表明，训练深度学习模型的成本下降速度比摩尔定律快 50 倍。
  • 由于人工智慧具有提高营运效率、改善住宅体验和实现永续发展目标的潜力，商业建筑业正处于重大进步的风口浪尖。随着人工智慧技术变得越来越容易取得，商业房地产利害关係人必须策略性地驾驭这些发展，以充分利用人工智慧的潜力。

该报告提供了有价值的信息，可帮助公司改进战略规划，并考虑透过实施人工智慧技术来发展业务的潜力。

目录

前言

调查范围/方法

执行摘要

第 1 章人工智慧和机器学习的基础知识

  • 机器学习的基本方法
    • 强化学习
    • 监督学习
    • 无监督学习
    • 深度学习（深度学习）
  • AI专业领域
    • 计算机视觉
    • 自然语言处理（NLP）
    • 人工智慧世代
    • 机器人技术

第 2 章 技术推动因素

  • 物联网（IoT）
  • 大数据
  • 人工智慧硬体和运算
    • 人工智慧晶片
    • AI边缘设备
    • 资料处理基础设施

第3章 AI模型训练与资料要求

  • 培训费用持续下降
  • AI训练目标与学习目标
  • 数据要求
  • 智慧建筑中的迁移学习

第四章 市场现状

  • 炒作还是现实？
  • 一般人工智慧介绍/投资趋势
    • 依行业划分的采用率
    • 地区差异
    • 投资趋势
    • 投资报酬率（ROI）
    • 企业情绪
    • 主要用例和应用领域
  • 智慧建筑采用和投资的趋势
    • 招募指标
    • 调查指标
  • 智慧建筑人工智慧解决方案成熟度评估

第五章：评估市场机会的规模

  • 人工智慧市场预测的统合分析
  • 评估人工智慧的好处
  • 依地区划分的市场分析
  • 智慧建筑市场预测

第六章 使用领域和用例

  • 智慧建筑用例：概述
    • 将用例映射到资料输入和人工智慧技术
  • 智慧建筑用例：市场评估
  • 能源管理和效率
    • 域概述
    • 主要用例
  • 水和污水管理
  • 预测性维护/资产优化
  • 安全和存取控制
  • 空间/空缺/人员流动
  • 室内环境/居住舒适度
  • 居民参与度与体验
  • 永续性和法规遵从性
  • 紧急/安全系统
  • 网路安全/网路管理
  • 数位孪生/建筑模拟
  • 数据整合/分析

第七章 市场课题与限制因素

  • 基础设施老化
  • 用户信任与信心
    • 人工智慧高估与行销
    • 可解释性
    • 准确性和错觉
  • 数据相关的课题
    • 整合、互通性和开放标准
    • 资料所有权和控制权
    • 数据品质
    • 资料隐私
  • 技能差距与人力资源开发
  • 搬迁
  • 人工智慧能耗

第 8 章 道德与监管考虑

  • 人工智慧安全、道德和诚信
  • 目前的人工智慧法规及其对智慧建筑的影响
    • 人工智慧法规与标准：世界各地的差异
    • 资料隐私
  • AI监理格局将如何演变？

第 9 章未来情境及其影响

  • 通往AGI（通用人工智慧）之路：现状
  • 生成式人工智慧和互动式人工智慧的新应用领域
  • 人工智慧代理
  • AIaaS（ "人工智慧即服务" ）
  • 可访问性、民主化和开源人工智慧

This Report is a New 2024 Study that Explores the Growing Role of Artificial Intelligence within the Commercial Buildings Market.

This new research builds on Memoori's 2021 Artificial Intelligence (AI) market analysis and looks at the progress that has occurred both in the capabilities of AI broadly and its specialized applications enabling smarter, more sustainable, and more responsive built environments.

It includes, at no extra cost, a spreadsheet containing the data from the report and high-resolution presentation charts showing the key findings. It is the first in a 2-part series of reports, with the second report on the AI market landscape being published later this year. Both these reports are included in our 2024 Premium Subscription Service, which also gives access to our chatbot AIM, where you can query all our research using the power of Large Language Models (LLMs) .

KEY QUESTIONS ADDRESSED:

  • Where are we on the journey towards "truly cognitive buildings"? Today's commercial buildings technology is transitioning away from rules-based analytics towards AI predictive machine learning models but adoption remains at modest levels. Real-world deployments remain narrow in scope driven by the more well-understood use cases around energy optimization, space utilization, and security.
  • What is holding back more widespread adoption of AI? Challenges inhibiting widespread AI adoption span technical integration with legacy systems, a general lack of the necessary specialist skills and wider education, and a culture within commercial real estate that is slow to embrace the new processes essential to leveraging AI solutions effectively.
  • How will AI in the Commercial Buildings Market Grow over the Medium Term? We estimate that the smart building AI market will grow at a 25.5% CAGR through 2028 to $6.48 billion, as this sector begins to embrace the emerging technology and closes the gap with more AI-centric industries.

WITHIN ITS 164 PAGES AND 18 CHARTS AND TABLES, THE REPORT FILTERS OUT ALL THE KEY FACTS AND DRAWS CONCLUSIONS, SO YOU CAN UNDERSTAND EXACTLY HOW AI TECHNOLOGY WILL BE APPLIED TO COMMERCIAL BUILDINGS AND WHY;

  • Through extensive analysis, Memoori has mapped out 66 distinct AI use cases spanning 12 key domains where solutions are actively being developed and commercialized for smart buildings. These encompass a diverse range of potential benefits from driving sustainability and energy efficiency to security enhancements and more tailored occupant experiences.
  • Billions of dollars are being invested by Big Tech in an "AI Arms Race". This is driving rapid innovation which will be of benefit to all industries. For example, Research from ARK Invest reveals that the cost of training deep learning models is decreasing at a rate 50 times faster than Moore's Law.
  • The commercial buildings industry stands on the cusp of significant advancements, driven by AI's potential to enhance operational efficiencies, improve occupant experiences, and contribute to sustainability goals. As AI technologies become more accessible, commercial real estate stakeholders must navigate these developments strategically to harness AI's full potential.

This report provides valuable information to companies so they can improve their strategic planning exercises AND look at the potential for developing their business through implementing AI technology.

WHO SHOULD BUY THIS REPORT?

The information contained in this report will be of value to all those engaged in managing, operating and investing in Commercial Buildings (and their Advisers) around the world. In particular, those wishing to understand exactly how AI & Machine Learning Technologies are impacting Commercial Real Estate will find it particularly useful.

Table of Contents

Preface

Research Scope & Methodology

Executive Summary

1. The Fundamentals of AI & Machine Learning

  • 1.1. Foundational Machine Learning Approaches
    • Reinforcement Learning
    • Supervised Learning
    • Unsupervised Learning
    • Deep Learning
  • 1.2. Specialized AI Domains
    • Computer Vision
    • Natural Language Processing (NLP)
    • Generative AI
    • Robotics

2. Technology Enablers

  • 2.1. The Internet of Things
  • 2.2. Big Data
  • 2.3. AI Hardware & Compute
    • AI Chips
    • AI Edge Devices
    • Data Processing Infrastructure

3. AI Model Training & Data Requirements

  • 3.1. Training Costs Continue to Fall
  • 3.2. AI Training Goals and Learning Objectives
  • 3.3. Data Requirements
  • 3.4. Transfer Learning in Smart Buildings

4. The State of the Market

  • 4.1. Hype or Reality?
  • 4.2. General AI Adoption & Investment Trends
    • Adoption Rates Across Industries
    • Regional Variations
    • Investment Trends
    • Returns on Investment
    • Corporate Sentiment
    • Leading Use Cases and Applications
  • 4.3. Smart Building Specific Adoption & Investment Trends
    • Adoption Indicators
    • Research Indicators
  • 4.4. Assessing Smart Building AI Solution Maturity

5. Sizing the Opportunity

  • 5.1. A Meta Analysis of AI Market Forecasts
  • 5.2. Assessing the Gains Attributable to AI
  • 5.3. Geographic Market Analysis
  • 5.4. Smart Building Market Estimates

6. Applications & Use Cases

  • 6.1. An Overview of Smart Building Use Cases
    • Mapping Use Cases to Data Inputs & AI Techniques
  • 6.2. Evaluating Smart Building Use Case Markets
  • 6.3. Energy Management and Efficiency
    • Domain Overview
    • Key Use Cases
  • 6.4. Water and Waste Management
    • Domain Overview
    • Key Use Cases
  • 6.5. Predictive Maintenance and Asset Optimization
    • Domain Overview
    • Key Use Cases
  • 6.6. Security and Access Control
    • Domain Overview
    • Key Use Cases
  • 6.7. Space, Occupancy & People Movement
    • Domain Overview
    • Key Use Cases
  • 6.8. Indoor Environment and Occupant Comfort
    • Domain Overview
    • Key Use Cases
  • 6.9. Occupant Engagement and Experience
    • Domain Overview
    • Key Use Cases
  • 6.10. Sustainability & Regulatory Compliance
    • Domain Overview
    • Key Use Cases
  • 6.11. Emergency and Safety Systems
    • Domain Overview
    • Key Use Cases
  • 6.12. Cybersecurity and Network Management
    • Domain Overview
    • Key Use Cases
  • 6.13. Digital Twin and Building Simulation
    • Domain Overview
    • Key Use Cases
  • 6.14. Data Integration and Analytics
    • Domain Overview
    • Key Use Cases

7. Challenges & Market Barriers

  • 7.1. Legacy Infrastructure
  • 7.2. User Confidence & Trust
    • AI Overhype and Marketing
    • Interpretability
    • Accuracy & Hallucination
  • 7.3. Data Related Challenges
    • Integration, Interoperability & Open Standards
    • Data Ownership & Control
    • Data Quality
    • Data Privacy
  • 7.4. Skills Gaps and Workforce Development
  • 7.5. Job Displacement
  • 7.6. The Energy Consumption of AI

8. Ethical & Regulatory Considerations

  • 8.1. AI Safety, Ethics & Alignment
  • 8.2. The Current State AI Regulations & Implications for Smart Buildings
    • Global Variations in AI Regulations & Standards
    • Data Privacy
  • 8.3. How the AI Regulatory Landscape Might Evolve

9. Future Scenarios & Their Implications

  • 9.1. Where Are We on the Road to AGI?
  • 9.2. Emerging Applications for Generative AI & Interactive AI
  • 9.3. AI Agents
  • 9.4. AI as a Service
  • 9.5. Accessibility, Democratization & Open-Source AI

List of Charts and Figures

  • Fig 1.0 - Research Scope
  • Fig 1.1 - AI & Machine Learning Techniques and their Smart Building Applications
  • Fig 2.1 - Installed Base of IoT Devices in Commercial Smart Buildings 2020 to 2028
  • Fig 4.1 - Global Corporate Investment in AI by Type 2013 to 2022
  • Fig 4.2 - AI Mentions in S&P 500 Earnings Calls Q3 2018 to Q3 2023
  • Fig 4.3 - The Leading Applications for AI Deployments, % of Respondents
  • Fig 4.4 - Real Estate Firm Engagement with AI Solutions in 2023
  • Fig 4.4 - The Gap between Expected Impacts & Knowledge Levels for AI Real Estate
  • Fig 4.5 - Trends in Smart Buildings & AI Research 2015 to 2023
  • Fig 4.6 - Prescriptive Data's Model of AI Solution Maturity
  • Fig 4.7 - Smart Building Solution Maturity
  • Fig 5.1 - Global AI Market Forecasts, A Meta-Analysis 2019 to 2030
  • Fig 5.2 - The Global Market for AI in Smart Commercial Buildings 2020 to 2028, $ Billions
  • Fig 6.1 - AI & Machine Learning Use Cases in Smart Commercial Buildings
  • Fig 6.2 - AI's Energy-saving Potential by Application Area, Energy Saving Effect %
  • Fig 7.1 - Leading Barriers Hindering Enterprise AI Adoption %