세계의 스마트 상업 빌딩용 인공지능(AI) 시장(2024년)

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