노인 돌봄 AI 시장 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the aI in elderly care market size is projected to be USD 39.20 billion in 2025, USD 44.61 billion in 2026, and reach USD 90.35 billion by 2031, growing at a CAGR of 15.16% from 2026 to 2031.

This report is Segmented by Service (Hardware, Software, Services), Deployment Mode (Cloud, On-Premise), Technology (ML & Analytics, NLP, Vision, Robotics, Other), Application (Fall Detection, Remote Monitoring, and More), End User (Home Care, Assisted Living, Nursing Homes, Others), and Geography (North America, Europe, and More). The Market Forecasts are Provided in Terms of Value (USD).

Global AI In Elderly Care Market Trends and Insights

Aging Population and Longer Life Expectancy

The demographic foundation of the AI in elderly care market is expanding most quickly in the age groups that consume the highest amount of care. Global life expectancy at birth reached 73.3 years in 2024, and the added years are increasingly associated with chronic disease, functional decline, and recurring support needs rather than long periods of full health. The global population aged 60 and above reached 1.22 billion in 2025, and that group is projected to rise toward 1.4 billion by 2030, which keeps the AI in elderly care market tied to an expanding care base for the full forecast period. The population aged 80 and above already stood at 104 million and is projected to reach 263 million by 2050, which matters because this group has the highest intensity of supervision, medication support, and mobility assistance needs. In the United States, the population aged 80 and above stood at 14.75 million in 2025 and is projected to reach 18.79 million by 2030, which points to rising pressure on staffing models and facility operations. China and India together are expected to hold a very large share of the global older population by 2050, which means product design in AI in elderly care market is increasingly being adapted for lower cost and resource-constrained care settings in Asia.

Caregiver Shortages Across Senior Care Settings

The AI in elderly care market is also being shaped by a labor shortage that is deeper than open vacancy numbers alone suggest. PHI projects that the United States direct care workforce must fill 9.7 million openings between 2024 and 2034, with 772,000 of those representing net new job growth rather than simple replacement demand. This means providers are not only struggling to hire, but they are also trying to maintain service quality while training large numbers of new workers into physically and emotionally demanding roles. In Japan, the required caregiver count reached 2.40 million by FY2026, while aging is projected to keep rising toward 2040, which widens the gap between labor supply and required care capacity. In this setting, the AI in elderly care market is benefiting from demand for tools that reduce charting time, simplify staff coordination, and identify resident risk earlier so fewer workers can manage more residents safely. The operational appeal is straightforward, because technologies that reduce documentation load and improve alert quality can support both productivity and worker retention in settings where every avoided hour of manual work matters.

Privacy, Cybersecurity, and Compliance Burden

The compliance barrier in the AI in elderly care market is rising at the same time that clinical and operational demand is accelerating. The EU AI Act entered into force in August 2024, and many healthcare related monitoring systems will face high risk obligations from August 2026, which increases the need for risk management, data governance, documentation, and human oversight before deployment. In the United States, protected health information rules under HIPAA add a separate layer of security and process requirements for any vendor working with resident level health data. Tokyo Metropolitan University research found that 82% of elderly subjects preferred privacy-protected monitoring systems over camera-based alternatives, which shows that privacy is not only a legal issue but also a user acceptance issue inside the AI in elderly care market. The challenge is that privacy preserving systems often require more advanced sensing, encryption, and data handling architecture, which raises development cost and slows smaller vendors. This gives larger and better funded suppliers a structural advantage because they can absorb compliance work more easily and spread those costs across a wider installed base.

Other drivers and restraints analyzed in the detailed report include:

1. Aging-In-Place and Proactive Remote Monitoring Adoption
2. Better AI Accuracy Across NLP, Vision, and Robotics
3. Integration Cost and Legacy-System Complexity

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Software captured 65.02% of the AI in elderly care market share in 2025, which shows that platform subscriptions still form the spending core of the current adoption cycle. This lead reflects the strong role of software in resident monitoring, clinical workflow support, analytics, and alert management across both home and facility environments. Hardware remains necessary for sensing and robotics, but it does not capture the same margin profile because device competition is broader and pricing pressure is more visible. Services are projected to expand at 16.17% CAGR from 2026 to 2031, which means the AI in the elderly care industry is moving from simple product purchase toward full deployment support. That pattern usually appears when buyers no longer ask whether to adopt a tool and instead focus on how to make it work across staff, workflows, and reporting structures.

The services expansion matters because implementation, training, workflow redesign, and managed support now influence contract value more directly than they did in the early phase of adoption. Facilities that deploy AI tools across multiple buildings or care programs often need help mapping alerts, staff roles, and escalation protocols into existing care processes. This makes professional services more central to retention because poor onboarding can weaken outcomes even when the software itself is strong. Sage raised USD 65 million in March 2026, bringing its total capital raised to USD 124 million, which points to investor confidence in integrated models that combine predictive software with operational support for senior living and skilled nursing providers. As the AI in elderly care market matures, vendors that pair software with recurring service capacity are likely to defend pricing more effectively than companies that rely on standalone products.

Cloud deployment accounted for 58.46% share of the AI in elderly care market size in 2026, and it is also projected to grow at 15.59% CAGR through 2031. This combination of current scale and future growth shows that cloud has become the preferred operating model for multi-site providers that need centralized visibility and rapid software updates. In the AI in elderly care market, cloud architectures are especially attractive when operators manage distributed home care, assisted living, and nursing services under one administrative structure. Shared access to alerts, documentation tools, and model improvements is easier when platforms are centrally managed rather than installed separately inside each building. This advantage becomes more important as AI tools expand from simple monitoring into workflow support, reporting, and predictive decision assistance.

On-premises systems still hold a place in settings with strict data residency demands or highly controlled public sector procurement rules. Some European and Asia-Pacific operators also prefer on-premises or private cloud structures when they handle sensitive neurological or long-term clinical data. Even so, local systems usually update more slowly, and slower model updates can weaken performance when vendors are improving detection or language features quickly. Enzo Health raised USD 26 million in May 2026 to scale cloud-based AI across home health, linking intake automation, clinical documentation, and quality assurance in one workflow, which reflects where capital is moving inside the AI in elderly care market. The broader direction remains clear, because buyers increasingly want platforms that can be rolled out quickly, managed centrally, and extended across care settings without major local infrastructure work.

Geography Analysis

North America accounted for 37.83% share of the AI in elderly care market size in 2025, which kept it as the largest regional contributor. The region benefits from high care costs, strong digital infrastructure, and a provider base that is actively seeking labor saving tools across home, community, and institutional care. In the United States, the population aged 80 and above stood at 14.75 million in 2025 and is projected to reach 18.79 million by 2030, which continues to strengthen the long term need for AI supported elderly care delivery. CMS finalized a minimum staffing standard of 3.48 nursing hours per resident day in April 2024, and that policy has increased pressure on facilities to use documentation and workflow tools that support compliance where hiring remains difficult. Capital also remains available for deployment in the region, as shown by Sage's March 2026 funding round and Enzo Health's May 2026 raise, which both focused on scaling AI tools across senior care and home health settings. South America remains earlier in adoption, with smartphone led monitoring and voice companion use appearing before broader institutional deployments in many areas.

Europe is a large but uneven regional opportunity inside the AI in elderly care market. The population aged 65 and above in the European Union reached 21% in 2023 and is projected to reach 29% by 2050, which keeps demand anchored in long duration demographic change rather than short term technology cycles. The EU AI Act is reshaping product requirements because healthcare-related monitoring systems will face stricter obligations from August 2026, which raises entry barriers and favors vendors with stronger compliance resources. The United Kingdom is moving on a separate path, but AI documentation trials in community care settings still show that workflow support remains one of the most immediate use cases for elderly care providers in the region.

Asia-Pacific is the fastest-growing region in the AI in elderly care market, with an expected CAGR of 18.03% from 2026 to 2031. Japan remains a key driver because its aging rate reached 28.6% in 2020 and is projected to move toward 35% by 2040, while caregiver demand reached 2.40 million by FY2026. Japan's government also committed JPY 1.9 billion, or USD 12.6 million, in its FY2024 supplementary budget for Care DX adoption packages, and a model project in Kitakyushu City reported a 35% reduction in overall care work time from coordinated technology use. China is also increasing its focus on AI robotics and elderly care infrastructure, while South Korea and Australia add support through digital readiness and aged care reform. The Middle East and Africa remain a smaller regional base, but premium facility deployments in GCC countries and community-oriented monitoring initiatives in parts of Africa suggest that adoption is beginning to widen beyond the most mature elderly care systems.

1. Aiva Health
2. Best Buy Health
3. CarePredict
4. Cera Care
5. GrandCare Systems
6. GrandPad
7. IBM
8. Intuition Robotics
9. K4Connect
10. Kami Vision
11. Nobi
12. Oracle
13. Koninklijke Philips
14. Reemo Health
15. Resideo Technologies
16. Samsung Electronics
17. Sensi.AI
18. SoftBank Robotics
19. Tunstall Healthcare
20. UBTECH Robotics
21. Vayyar Care
22. WellSky

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 Introduction

• 1.1 Study Assumptions & Market Definition
• 1.2 Scope of the Study

2 Research Methodology

3 Executive Summary

4 Market Landscape

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 Aging Population and Longer Life Expectancy
  • 4.2.2 Caregiver Shortages Across Senior Care Settings
  • 4.2.3 Aging-In-Place and Proactive Remote Monitoring Adoption
  • 4.2.4 Better AI Accuracy Across NLP, Vision, and Robotics
  • 4.2.5 Privacy-Preserving Ambient Sensing Replacing Cameras and Wearables
  • 4.2.6 Staffing-Rule and Documentation ROI Driving Workflow AI Uptake
• 4.3 Market Restraints
  • 4.3.1 Privacy, Cybersecurity, And Compliance Burden
  • 4.3.2 Integration Cost and Legacy-System Complexity
  • 4.3.3 Surveillance Trust Gap Among Oldest-Old Users
  • 4.3.4 Tariff And Component Volatility in Sensors and Robotics
• 4.4 Value / Supply-Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Porter's Five Forces Analysis
  • 4.7.1 Threat of New Entrants
  • 4.7.2 Bargaining Power of Suppliers
  • 4.7.3 Bargaining Power of Buyers
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Industry Rivalry

5 Market Size & Growth Forecasts

• 5.1 By Offering
  • 5.1.1 Hardware
  • 5.1.2 Software
  • 5.1.3 Services
• 5.2 By Deployment Mode
  • 5.2.1 Cloud
  • 5.2.2 On-Premise
• 5.3 By Technology
  • 5.3.1 Machine Learning and Predictive Analytics
  • 5.3.2 Natural Language Processing
  • 5.3.3 Computer Vision
  • 5.3.4 Robotics and Robotic Assistance
  • 5.3.5 Other Technology (Smart Home Devices and IoT Solutions, Generative AI Care Assistants, etc.)
• 5.4 By Application
  • 5.4.1 Fall Detection and Prevention
  • 5.4.2 Remote Monitoring and Predictive Alerts
  • 5.4.3 Medication Management
  • 5.4.4 Cognitive Support and Dementia Care
  • 5.4.5 Social Interaction and Companionship
  • 5.4.6 Rehabilitation and Daily Living Assistance
• 5.5 By End User
  • 5.5.1 Home Care Settings
  • 5.5.2 Assisted Living Facilities
  • 5.5.3 Nursing Homes
  • 5.5.4 Other End Users (Hospitals and Clinics, etc.)
• 5.6 By Geography
  • 5.6.1 North America
    • 5.6.1.1 United States
    • 5.6.1.2 Canada
    • 5.6.1.3 Mexico
  • 5.6.2 Europe
    • 5.6.2.1 Germany
    • 5.6.2.2 United Kingdom
    • 5.6.2.3 France
    • 5.6.2.4 Italy
    • 5.6.2.5 Spain
    • 5.6.2.6 Rest of Europe
  • 5.6.3 Asia-Pacific
    • 5.6.3.1 China
    • 5.6.3.2 Japan
    • 5.6.3.3 India
    • 5.6.3.4 Australia
    • 5.6.3.5 South Korea
    • 5.6.3.6 Rest of Asia-Pacific
  • 5.6.4 Middle East & Africa
    • 5.6.4.1 GCC
    • 5.6.4.2 South Africa
    • 5.6.4.3 Rest of Middle East & Africa
  • 5.6.5 South America
    • 5.6.5.1 Brazil
    • 5.6.5.2 Argentina
    • 5.6.5.3 Rest of South America

6 Competitive Landscape

• 6.1 Market Concentration
• 6.2 Market Share Analysis
• 6.3 Company Profiles (includes Global level Overview, Market-level Overview, Core Segments, Financials, Strategic Information, Market Rank/Share, Products & Services, Recent Developments)
  • 6.3.1 Aiva Health
  • 6.3.2 Best Buy Health
  • 6.3.3 CarePredict
  • 6.3.4 Cera Care
  • 6.3.5 GrandCare Systems
  • 6.3.6 GrandPad
  • 6.3.7 IBM
  • 6.3.8 Intuition Robotics
  • 6.3.9 K4Connect
  • 6.3.10 Kami Vision
  • 6.3.11 Nobi
  • 6.3.12 Oracle Corporation
  • 6.3.13 Philips
  • 6.3.14 Reemo Health
  • 6.3.15 Resideo Technologies
  • 6.3.16 Samsung Electronics
  • 6.3.17 Sensi.AI
  • 6.3.18 SoftBank Robotics
  • 6.3.19 Tunstall Healthcare
  • 6.3.20 UBTECH Robotics
  • 6.3.21 Vayyar Care
  • 6.3.22 WellSky

7 Market Opportunities & Future Outlook

• 7.1 White-space & Unmet-need Assessment