세계의 AI 맞춤형 영양 플랫폼 시장 예측 : 구성요소별, 기술별, 용도별, 최종사용자별, 비즈니스 모델별, 지역별 분석(-2032년)

According to Stratistics MRC, the Global AI-Personalized Nutrition Platforms Market is accounted for $1.4 billion in 2025 and is expected to reach $7.6 billion by 2032 growing at a CAGR of 27.5% during the forecast period. AI-personalized nutrition platforms are digital platforms using AI to deliver customized dietary and supplement advice. By analyzing individual data like DNA, gut microbiome, and lifestyle, they create tailored nutrition plans. This moves beyond one-size-fits-all diets to hyper-personalized wellness. The market is growing as consumers seek scientifically-backed, individualized health solutions. Companies leverage this technology to offer subscription services, personalized meal kits, and targeted supplement recommendations, driving engagement and better health outcomes.

According to the American Society for Nutrition, AI-powered nutrition platforms generated personalized diet recommendations for more than 12 million users in 2023, improving health outcomes in clinical studies.

Market Dynamics:

Driver:

Rising prevalence of chronic diseases and preventive healthcare focus

The increasing incidence of lifestyle-related chronic diseases such as obesity, diabetes, and cardiovascular disorders is fueling demand for AI-personalized nutrition platforms. Consumers and healthcare providers are focusing on preventive strategies, leveraging AI-driven insights to optimize diet plans based on individual health metrics, genetics, and lifestyle patterns. Moreover, wearable devices and health apps generate real-time data, enabling personalized nutrition recommendations. This shift toward proactive wellness and precision nutrition is creating sustained growth opportunities for platform developers and healthcare integrators globally.

Restraint:

Limited scientific validation for some AI recommendations

Despite growing adoption, certain AI-personalized nutrition solutions face skepticism due to limited clinical validation and lack of universally accepted dietary standards. Inaccurate or non-evidence-based recommendations can reduce user trust, hinder adoption, and potentially lead to adverse health outcomes. Additionally, discrepancies in algorithms across platforms and lack of longitudinal studies may constrain integration with healthcare systems. Vendors must invest in research collaborations, clinical trials, and regulatory compliance to strengthen credibility and encourage wider market penetration.

Opportunity:

Expansion into corporate wellness and insurance programs

Companies are increasingly incorporating AI-driven nutrition platforms into employee wellness initiatives and health insurance programs to improve workforce health, reduce absenteeism, and lower healthcare costs. Integration with wearable devices and personalized health monitoring enables scalable preventive interventions, enhancing employee engagement. Moreover, insurers are exploring AI insights to design tailored plans and incentives, driving B2B adoption. This corporate expansion offers recurring revenue opportunities for platform providers while reinforcing long-term market growth, especially as organizations emphasize holistic well-being and data-driven health solutions.

Threat:

Data security risks and potential algorithm biases

AI-personalized nutrition platforms collect sensitive health and lifestyle data, exposing users and providers to privacy breaches and regulatory scrutiny. Inadequate encryption, poor data governance, and third-party vulnerabilities can erode trust and lead to financial penalties. Additionally, algorithmic biases due to limited or skewed datasets may deliver inaccurate recommendations, reducing efficacy and credibility. To mitigate these threats, developers must implement robust cybersecurity measures, transparent AI models, and continuous auditing to ensure data integrity, ethical use, and equitable outcomes.

Covid-19 Impact:

The pandemic accelerated adoption of AI-personalized nutrition platforms as consumers sought remote, tailored health guidance while accessing gyms and clinics was restricted. Lockdowns highlighted the importance of preventive health and immunity, driving engagement with digital tools and telehealth integration. Platforms experienced rapid user growth, expansion in app features, and increased investment from healthcare providers and insurers. This period reinforced the long-term relevance of AI-driven nutrition solutions, prompting sustained adoption and innovation while fostering consumer trust in digital health technologies.

The software/platforms segment is expected to be the largest during the forecast period

The software/platforms segment is expected to account for the largest market share during the forecast period. These platforms dominate due to their ability to combine AI algorithms, user-friendly interfaces, and comprehensive dietary guidance in a single solution. Partnerships with healthcare providers and integration with medical records further strengthen adoption. Additionally, continuous software updates and modular add-ons for meal tracking, nutrition scoring, and personalized recommendations enhance user retention. Their versatility across individual consumers, corporates, and insurers solidifies long-term market share, making the software/platforms segment the preferred choice for personalized nutrition solutions worldwide.

The computer vision segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the computer vision segment is predicted to witness the highest growth rate. Adoption of computer vision is fueled by the convenience of instant dietary analysis and integration with mobile apps and health platforms. Innovations in AI image recognition, augmented reality features, and database expansion for food items accelerate adoption across consumer, clinical, and corporate wellness applications. Additionally, growing smartphone penetration and wearable device usage enable widespread deployment. These factors collectively contribute to rapid market growth, positioning computer vision as the fastest-expanding technology segment in AI-personalized nutrition platforms globally.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share due to high health awareness, widespread adoption of digital health technologies, and strong investment in AI healthcare solutions. Regulatory support, well-established telehealth infrastructure, and partnerships between platform providers, insurers, and wellness programs drive adoption. Moreover, high disposable incomes and advanced consumer tech penetration allow for early uptake of personalized nutrition tools. These factors collectively contribute to North America maintaining the largest market share, solidifying its position as a key revenue hub for AI-personalized nutrition platforms.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR. Rapid urbanization, increasing health consciousness, and rising disposable incomes fuel demand for AI-personalized nutrition solutions in Asia Pacific. Governments and private stakeholders are investing in digital healthcare infrastructure, while smartphone and wearable device adoption support scalable platform deployment. Furthermore, localized content, affordable pricing models, and emerging startups catering to regional dietary preferences accelerate market growth. Collectively, these factors drive high adoption rates, positioning Asia Pacific as the fastest-growing region for AI-personalized nutrition platforms during the forecast period.

Key players in the market

Some of the key players in AI-Personalized Nutrition Platforms Market include Viome, ZOE, InsideTracker, NutriSense, Levels Health, EatLove, Suggestic, Foodsmart, Baze, Habit, DNAFit, Nutrigenomix, Fay, GenoPalate, Noom, Inc., and Medtronic plc.

Key Developments:

In July 2025, Viome, a life sciences startup founded by veteran tech entrepreneur Naveen Jain, announced collaboration with Microsoft to scale its molecular analysis platform - part of what Viome describes as a new era of AI-powered preventive health and wellness. Viome says Microsoft's cloud and AI infrastructure specially tuned for its purposes in conjunction with the tech giant will allow it to process biological data more efficiently. The idea is to expand access, reduce costs, and accelerate data processing and diagnostics.

In April 2025, InsideTracker, a leader in data-driven health technology, is pleased to introduce Terra, a first-of-its-kind virtual coach that enables its members to dive deep into their own body. Terra builds on the success of Ask InsideTracker, a native AI tool released last year and now one of the platform's most popular features. With this major version update, Terra becomes a personalized health coach with the ability to access information and offer recommendations typically limited to high-end concierge medicine.

Components Covered:

• Software/Platforms
• Services

Technologies Covered:

• Machine Learning & Deep Learning
• Natural Language Processing (NLP)
• Computer Vision
• Data Analytics

Applications Covered:

• Disease Management
• Weight Management
• Sports Nutrition & Active Lifestyle
• General Health & Wellness

End Users Covered:

• Healthcare Providers & Professionals
• Wellness & Fitness Centers
• Corporate Organizations
• Research Institutions

Business Models Covered:

• Business-to-Consumer (B2C)
• Business-to-Business (B2B)

Regions Covered:

• North America
  • US
  • Canada
  • Mexico
• Europe
  • Germany
  • UK
  • Italy
  • France
  • Spain
  • Rest of Europe
• Asia Pacific
  • Japan
  • China
  • India
  • Australia
  • New Zealand
  • South Korea
  • Rest of Asia Pacific
• South America
  • Argentina
  • Brazil
  • Chile
  • Rest of South America
• Middle East & Africa
  • Saudi Arabia
  • UAE
  • Qatar
  • South Africa
  • Rest of Middle East & Africa

What our report offers:

• Market share assessments for the regional and country-level segments
• Strategic recommendations for the new entrants
• Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
• Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
• Strategic recommendations in key business segments based on the market estimations
• Competitive landscaping mapping the key common trends
• Company profiling with detailed strategies, financials, and recent developments
• Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

• Company Profiling
  • Comprehensive profiling of additional market players (up to 3)
  • SWOT Analysis of key players (up to 3)
• Regional Segmentation
  • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
  • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

• 2.1 Abstract
• 2.2 Stake Holders
• 2.3 Research Scope
• 2.4 Research Methodology
  • 2.4.1 Data Mining
  • 2.4.2 Data Analysis
  • 2.4.3 Data Validation
  • 2.4.4 Research Approach
• 2.5 Research Sources
  • 2.5.1 Primary Research Sources
  • 2.5.2 Secondary Research Sources
  • 2.5.3 Assumptions

3 Market Trend Analysis

• 3.1 Introduction
• 3.2 Drivers
• 3.3 Restraints
• 3.4 Opportunities
• 3.5 Threats
• 3.6 Technology Analysis
• 3.7 Application Analysis
• 3.8 End User Analysis
• 3.9 Emerging Markets
• 3.10 Impact of Covid-19

4 Porters Five Force Analysis

• 4.1 Bargaining power of suppliers
• 4.2 Bargaining power of buyers
• 4.3 Threat of substitutes
• 4.4 Threat of new entrants
• 4.5 Competitive rivalry

5 Global AI-Personalized Nutrition Platforms Market, By Component

• 5.1 Introduction
• 5.2 Software/Platforms
• 5.3 Services

6 Global AI-Personalized Nutrition Platforms Market, By Technology

• 6.1 Introduction
• 6.2 Machine Learning & Deep Learning
• 6.3 Natural Language Processing (NLP)
• 6.4 Computer Vision
• 6.5 Data Analytics

7 Global AI-Personalized Nutrition Platforms Market, By Application

• 7.1 Introduction
• 7.2 Disease Management
• 7.3 Weight Management
• 7.4 Sports Nutrition & Active Lifestyle
• 7.5 General Health & Wellness

8 Global AI-Personalized Nutrition Platforms Market, By End User

• 8.1 Introduction
• 8.2 Healthcare Providers & Professionals
• 8.3 Wellness & Fitness Centers
• 8.4 Corporate Organizations
• 8.5 Research Institutions

9 Global AI-Personalized Nutrition Platforms Market, By Business Model

• 9.1 Introduction
• 9.2 Business-to-Consumer (B2C)
  • 9.2.1 Subscription-based (SaaS)
  • 9.2.2 One-time Purchase
• 9.3 Business-to-Business (B2B)
  • 9.3.1 Licensing
  • 9.3.2 White-label Solutions

10 Global AI-Personalized Nutrition Platforms Market, By Geography

• 10.1 Introduction
• 10.2 North America
  • 10.2.1 US
  • 10.2.2 Canada
  • 10.2.3 Mexico
• 10.3 Europe
  • 10.3.1 Germany
  • 10.3.2 UK
  • 10.3.3 Italy
  • 10.3.4 France
  • 10.3.5 Spain
  • 10.3.6 Rest of Europe
• 10.4 Asia Pacific
  • 10.4.1 Japan
  • 10.4.2 China
  • 10.4.3 India
  • 10.4.4 Australia
  • 10.4.5 New Zealand
  • 10.4.6 South Korea
  • 10.4.7 Rest of Asia Pacific
• 10.5 South America
  • 10.5.1 Argentina
  • 10.5.2 Brazil
  • 10.5.3 Chile
  • 10.5.4 Rest of South America
• 10.6 Middle East & Africa
  • 10.6.1 Saudi Arabia
  • 10.6.2 UAE
  • 10.6.3 Qatar
  • 10.6.4 South Africa
  • 10.6.5 Rest of Middle East & Africa

11 Key Developments

• 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
• 11.2 Acquisitions & Mergers
• 11.3 New Product Launch
• 11.4 Expansions
• 11.5 Other Key Strategies

12 Company Profiling

• 12.1 Viome
• 12.2 ZOE
• 12.3 InsideTracker
• 12.4 NutriSense
• 12.5 Levels Health
• 12.6 EatLove
• 12.7 Suggestic
• 12.8 Foodsmart
• 12.9 Baze
• 12.10 Habit
• 12.11 DNAFit
• 12.12 Nutrigenomix
• 12.13 Fay
• 12.14 GenoPalate
• 12.15 Noom, Inc.
• 12.16 Medtronic plc