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市場調查報告書

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1876661

人工智慧賦能營養配方市場預測至2032年：按技術、應用、最終用戶和地區分類的全球分析

AI-Driven Nutritional Formulation Market Forecasts to 2032 - Global Analysis By Technology, Application, End User and By Geography

出版日期: 2025年11月01日 | 出版商:

Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3個工作天內

價格

簡介目錄圖表

根據 Stratistics MRC 的一項研究，全球人工智慧營養配方市場預計到 2025 年將達到 12.9 億美元，到 2032 年將達到 41.3 億美元，在預測期內的複合年成長率為 18.0%。

人工智慧驅動的營養配方技術利用機器學習、預測模型和深度數據分析，為食品、膳食補充劑和保健品設計高度針對性的營養混合物。透過處理消費者的飲食、生物標記、成分相互作用和代謝行為等資訊，人工智慧能夠更快、更準確地提供配方建議。這使得研發人員能夠最佳化成分比例、降低研發成本，並透過預測配方在實際應用中的表現來提升營養功效。各大品牌正在利用這些工具打造專注於免疫力、體重管理、提升能量和改善思維清晰度的解決方案。總而言之，人工智慧推動了產品創新，並確保個人化營養解決方案能更好地滿足消費者的需求和健康目標。

《Forward Fooding (2025)》報告指出，到2030年，食品配方中的預測分析可透過減少廢棄物，每年節省高達1.27億美元。該報告也引述麥肯錫的估計，人工智慧預計每年創造高達5,000億美元的全球價值，使包括食品製造業在內的各產業的創新速度翻倍。

對個人化營養的需求日益成長

消費者對個人化飲食解決方案日益成長的興趣正在推動人工智慧營養處方市場的發展。如今，消費者需要根據自身健康指標、基因資訊、生活方式和健康需求量身定做的營養計劃和產品。人工智慧在分析包括生物標記、飲食史和持續健康數據在內的各種資料集方面發揮關鍵作用，從而製定精準的個人化處方。預防醫學意識的增強、慢性病發病率的上升以及改善長期健康的願望進一步推動了這一趨勢。隨著人們對精準個人化的期望不斷提高，各公司正在利用人工智慧平台開發客製化的營養補充劑、機能性食品和個人化營養方案，加速市場擴張。

高昂的實施成本

人工智慧技術實施成本高是限制人工智慧營養配方市場發展的主要阻礙因素。為了取得顯著成效，企業需要在人工智慧軟體、資料儲存系統、分析引擎和可靠資料集方面投入大量資金。中小製造商往往難以負擔這些費用，這限制了它們將人工智慧融入產品開發的能力。此外，維護、網路安全、培訓和軟體升級等持續性成本進一步加劇了企業的財務壓力。這些高昂的支出造成了大中小型企業之間的差距，限制了人工智慧的廣泛應用。因此，只有財力雄厚的企業才能部署先進的人工智慧配方工具，減緩了市場整體的成長速度。

擴展我們的個人化營養平台

個人化營養平台的日益普及為人工智慧驅動的營養處方領域帶來了巨大的機會。隨著人們尋求根據自身生物特徵數據、生活習慣和健康目標量身定做的營養方案，人工智慧能夠以高效且可擴展的方式實現高級個人化。品牌可以利用整合於穿戴式裝置、健康應用程式以及基因和微生物組評估的人工智慧模型，提供持續的洞察和客製化的營養解決方案。這將催生出個人化營養補充品包、客製化飲食計畫和訂閱式營養服務等選擇。隨著全球數位健康技術的普及，人工智慧驅動的個人化有望徹底改變消費者的營養體驗，並為利用這些先進技術的公司帶來巨大的市場擴張。

科技快速過時

技術變革的快速步伐對人工智慧驅動的營養配方產業構成重大威脅。人工智慧模型、計算工具和分析框架頻繁更新，迫使企業持續投資系統升級。這些快速發展推高了營運成本，縮短了現有技術的使用壽命。由於資源有限，小規模的公司往往落後於時代，造成競爭不平衡。過時的人工智慧工具會產生不可靠的配方結果，從而可能損害品牌聲譽。持續升級、演算法重新訓練以及新資料系統的整合增加了複雜性。這種不斷適應技術的需求加大了企業的壓力，並限制了其製定穩定的長期規劃。

新冠疫情的影響：

新冠疫情提升了消費者對增強免疫力、個人化營養和預防醫學的關注度，為人工智慧營養配方市場注入了強勁動力。對於尋求實證膳食指導的消費者而言，人工智慧配方工具已成為設計精準營養補充劑和機能性食品的得力助手。遠端醫療、數位健康平台和營養管理應用程式的日益普及也推動了人工智慧的應用。儘管供應鏈受到監管方面的干擾，實體研發流程也出現延誤，但對個人化營養解決方案日益成長的需求最終克服了這些障礙。疫情刺激了對人工智慧技術的投資，鞏固了數據驅動營養在醫療保健系統和消費者健康市場的長期地位。

預計在預測期內，營養成分分析和平衡細分市場將佔據最大的市場佔有率。

由於營養成分分析和平衡是精準高效產品設計的基礎，預計在預測期內，該細分市場將佔據最大的市場佔有率。此細分市場專注於評估營養成分、檢驗成分協同作用，並確定安全有效產品開發所需的最佳營養水平。人工智慧工具處理生化資料集、飲食行為洞察和成分特性，以設計滿足預期健康益處的配方。由於適當的營養平衡對於開發膳食補充劑、機能性食品、個人化飲食以及遵守監管準則至關重要，因此各公司正在廣泛使用這些系統。其普遍重要性和基礎性意義使其成為最大的細分市場。

預計在預測期內，個人化營養平台細分市場將實現最高的複合年成長率。

受消費者對數據驅動型客製化健康解決方案的強勁需求推動，個人化營養平台領域預計將在預測期內實現最高成長率。這些平台利用人工智慧分析健康追蹤器、基因資訊、生物標記測量和生活方式數據等資訊，從而製定高度精準的營養計劃。數位健康工具、遠端醫療服務和持續監測設備的興起進一步推動了該領域的成長。消費者對個人化營養補充品、動態調整的飲食和機能性食品的需求持續成長。隨著全球對預防性醫療保健的日益重視，人工智慧驅動的個人化服務正在迅速發展，使其成為營養技術生態系統中成長最快的領域。

佔比最大的地區：

預計北美將在預測期內佔據最大的市場佔有率，這主要得益於其成熟的醫療保健體系、廣泛應用的數位健康工具以及大量的研發投入。該地區蓬勃發展的膳食補充劑、機能性食品和健康產業為人工智慧在營養設計領域的應用提供了理想的環境。美國消費者對客製化和預防性營養的高需求正在推動人工智慧技術的應用。充滿活力的AIStart-Ups、研究中心和科技公司生態系統正在推動進一步的創新。有利於醫療保健技術和數據驅動型解決方案的法規環境也為成長提供了支持。這些因素共同作用，使北美主導人工智慧營養設計在多元化應用領域的拓展。

年複合成長率最高的地區：

預計亞太地區在預測期內將實現最高的複合年成長率。這一快速成長主要得益於健康意識的提高、智慧型手機和穿戴式裝置的普及，以及中國和印度等國家收入的成長。亞太地區越來越多的消費者開始傾向於數據驅動的健康選擇、預防性營養和機能性食品。同時，區域政府和企業也在增加對數位健康和人工智慧平台的投資。這些因素共同作用，使亞太地區成為人工智慧營養解決方案領域最具活力和成長最快的市場，其成長速度超過了成熟地區。

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公司概況
- 對其他市場參與者（最多 3 家公司）進行全面分析
- 主要參與者（最多3家公司）的SWOT分析
區域細分
- 根據客戶要求，提供主要國家的市場估算和預測以及複合年成長率（註：可行性需確認）。
競爭基準化分析
- 基於產品系列、地域覆蓋範圍和策略聯盟基準化分析

介紹
北美洲
- 美國
- 加拿大
- 墨西哥
歐洲
- 德國
- 英國
- 義大利
- 法國
- 西班牙
- 其他歐洲
亞太地區
- 日本
- 中國
- 印度
- 澳洲
- 紐西蘭
- 韓國
- 亞太其他地區
南美洲
- 阿根廷
- 巴西
- 智利
- 南美洲其他地區
中東和非洲
- 沙烏地阿拉伯
- 阿拉伯聯合大公國
- 卡達
- 南非
- 其他中東和非洲地區

第9章：重大發展

協議、夥伴關係、合作和合資企業
收購與併購
新產品上市
業務拓展
其他關鍵策略

第10章：企業概況

Suggestic
EatLove
Viome Life Sciences
DNAfit（Prenetics）
DayTwo Ltd.
PIPA AI
NutrifyGenie AI
Amway
Nutrigenie
BetterMeal AI
Heali AI
Habit
Bioniq
ZOE
Nutrino

簡介目錄圖表

Product Code: SMRC32345

According to Stratistics MRC, the Global AI-Driven Nutritional Formulation Market is accounted for $1.29 billion in 2025 and is expected to reach $4.13 billion by 2032 growing at a CAGR of 18.0% during the forecast period. AI-powered nutritional formulation uses machine learning, predictive models, and deep data analysis to design highly targeted nutrient blends for foods, supplements, and wellness products. By processing information on consumer diets, biological indicators, ingredient interactions, and metabolic behavior, AI delivers faster and more accurate formulation insights. It enables developers to fine-tune ingredient ratios, lower R&D costs, and enhance nutritional performance by forecasting how formulations will function in real life. Brands apply these tools to create solutions focused on immunity, weight control, energy, or mental clarity. Overall, AI strengthens product innovation and ensures that personalized nutrition solutions better meet consumer needs and health objectives.

According to Forward Fooding (2025), data shows that predictive analytics in food formulation could save up to $127 million annually by 2030 through reduced waste. McKinsey estimates cited in the report suggest that AI could unlock up to $500 billion in annual global value, particularly by doubling the speed of innovation across industries, including food manufacturing.

Market Dynamics:

Driver:

Rising demand for personalized nutrition

Rising interest in personalized dietary solutions is significantly boosting the AI-driven nutritional formulation market. Consumers now want nutrition plans and products designed according to their unique health markers, genetics, lifestyle patterns, and wellness priorities. AI plays a crucial role by analyzing diverse datasets-including biomarkers, dietary history, and continuous health readings-to build precise and individualized formulations. This trend is strengthened by growing awareness of preventive health, increasing chronic illness cases, and the desire for better long-term wellness outcomes. As expectations for highly accurate personalization increase, companies are turning to AI-based platforms to create customized supplements, functional foods, and tailored nutrition programs, accelerating market expansion.

Restraint:

High implementation costs

The substantial cost associated with adopting AI technologies is a key limitation in the AI-driven nutritional formulation market. Organizations must invest heavily in AI software, data storage systems, analytics engines, and reliable datasets to achieve effective outcomes. Smaller manufacturers often find these expenses challenging, restricting their ability to integrate AI into product development. Additional recurring costs-including maintenance, cybersecurity, training, and software upgrades-further increase financial pressure. These high expenditures create disparities between large companies and smaller firms, limiting widespread AI adoption. Consequently, only businesses with strong financial resources can adopt advanced AI-driven formulation tools, slowing the overall growth of market implementation.

Opportunity:

Expansion of personalized nutrition platforms

The growing popularity of personalized nutrition platforms offers a strong opportunity for the AI-driven nutritional formulation sector. As individuals seek nutrition plans tailored to their biological data, lifestyle choices, and wellness objectives, AI makes high-level personalization both efficient and scalable. Brands can use AI models integrated with wearables, health apps, and genetic or microbiome assessments to deliver continuous insights and customized nutrient solutions. This unlocks options such as personalized supplement packs, bespoke diet programs, and subscription-based nutrition services. With digital health adoption rising globally, AI-enabled personalization can reshape consumer nutrition experiences and generate substantial market expansion for companies leveraging these advanced technologies.

Threat:

Rapid technological obsolescence

The fast pace of technological change represents a significant threat to the AI-driven nutritional formulation industry. AI models, computing tools, and analytical frameworks are frequently updated, forcing companies to invest continuously in system upgrades. These rapid advancements raise operational costs and shorten the lifespan of existing technologies. Smaller firms often fall behind due to limited resources, leading to competitive inequality. Outdated AI tools can generate unreliable formulation results, potentially harming brand reputation. Constant upgrades, retraining of algorithms, and integration of new data systems create additional complexity. This ongoing need for technological adaptation increases pressure on organizations and restricts stable long-term planning.

Covid-19 Impact:

The COVID-19 pandemic created strong momentum for the AI-driven nutritional formulation market by elevating consumer interest in immunity enhancement, personalized nutrition, and preventive wellness. As individuals sought scientifically informed dietary guidance, AI-enabled formulation tools became valuable for designing precise supplements and functional foods. Increased reliance on telemedicine, digital health platforms, and nutrition-tracking apps further supported AI adoption. Although restrictions disrupted supply chains and slowed physical R&D processes, the heightened demand for tailored nutrition solutions ultimately outweighed these setbacks. The pandemic encouraged greater investment in AI technologies and reinforced the long-term role of data-driven nutrition in both healthcare systems and consumer health markets.

The nutrient profiling & balancing segment is expected to be the largest during the forecast period

The nutrient profiling & balancing segment is expected to account for the largest market share during the forecast period because it acts as the essential engine behind precise and effective formulation work. This segment focuses on assessing nutrient compositions, evaluating ingredient synergies, and determining optimal nutrient levels to build safe and performance-driven products. AI tools here process biochemical datasets, dietary behavior insights, and ingredient characteristics to design formulas that match desired health benefits. Since proper nutrient balancing is critical for developing supplements, functional foods, personalized diets, and meeting regulatory guidelines, businesses depend on these systems extensively. Its universal relevance and foundational importance position it as the largest market segment.

The personalized nutrition platforms segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the personalized nutrition platforms segment is predicted to witness the highest growth rate because consumers are shifting strongly toward data-driven, customized wellness solutions. These platforms use AI to analyze information from health trackers, genetic insights, biomarker readings, and lifestyle inputs to create highly targeted nutrition plans. The widespread use of digital wellness tools, remote health services, and continuous monitoring devices further boosts this segment's momentum. Demand for individualized supplements, dynamically adjusted diets, and personalized functional foods continue to rise. With increasing global focus on preventive healthcare, AI-powered personalization expands rapidly, making this segment the highest-growing within the nutrition technology ecosystem.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share owing to its mature healthcare systems, extensive use of digital health tools, and heavy R&D spending. The region's thriving nutraceutical, functional-food, and wellness industries make it ideal for leveraging AI in nutrient design. High consumer demand for tailored and preventive nutrition in the U.S. fuels adoption. A thriving ecosystem of AI startups, research centers, and tech companies further propels innovation. Favorable regulatory conditions for health-tech and data-driven solutions also support growth. These combined factors give North America a leading edge in scaling AI-enabled nutritional formulation across applications.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR. This surge is driven by growing health consciousness, widespread smartphone and wearable use, and rising incomes in nations such as China and India. More consumers in APAC are gravitating toward data-backed wellness choices, preventive nutrition, and tailored functional foods. At the same time, regional governments and entrepreneurs are ramping up investments in digital health and AI-driven platforms. Together, these factors make Asia-Pacific the most dynamic and rapidly expanding market for AI-based nutrition solutions, surpassing mature regions in growth momentum.

Key players in the market

Some of the key players in AI-Driven Nutritional Formulation Market include Suggestic, EatLove, Viome Life Sciences, DNAfit (Prenetics), DayTwo Ltd., PIPA AI, NutrifyGenie AI, Amway, Nutrigenie, BetterMeal AI, Heali AI, Habit, Bioniq, ZOE and Nutrino.

Key Developments:

In October 2025, Amway will invest USD 12 million in India over the next three to five years to set up stores across the country, which it expects to become among its top three global markets. The company, which has completed ten years of manufacturing in India, is looking to enhance exports from the country, Nelson, who is in his maiden visit to India since assuming the President and CEO role, told PTI Videos in an interview.

In January 2024, Bioniq is thrilled to announce its strategic partnership with Meta, the pioneering hybrid healthcare company combining Digital Therapeutics (DTx) with human-centric clinical care. Bioniq will be serving members under both of Meta's platforms, GluCare.Health and Zone.Health. This collaboration marks a significant milestone in the quest for redefining healthcare services in the GCC, particularly in the realm of metabolic health, chronic disease management, preventive health, and longevity.

In May 2023, Suggestic announced that it has acquired Wishroute, a leading provider of human-powered engagement and healthy habit coaching. The acquisition allows Suggestic to expand its product and service offerings, enter new markets, and accelerate its growth in the Telewellness and Behavioral Engagement market.

Technologies Covered:

Machine Learning for Predictive Formulation
NLP for Ingredient Labeling & Consumer Feedback
Computer Vision for Ingredient Quality & Recognition
Formulation Software with AI-Driven Optimization
Biomarker-Linked AI Engines for Personalization
AI-IoT Integration for Real-Time Nutritional Monitoring

Applications Covered:

Ingredient Selection Algorithms
Nutrient Profiling & Balancing
Custom Supplement Design
Functional Food Engineering
Allergen & Sensitivity Detection
Sensory Modeling
Health Outcome Prediction
Adaptive Nutrition Feedback Systems

End Users Covered:

Nutraceutical Product Manufacturers
Functional Food Innovators
Personalized Nutrition Platforms
R&D Labs & Contract Formulators
Sports & Performance Nutrition Brands
Pet Nutrition & Animal Health Companies
Clinical & Therapeutic Nutrition Providers

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

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-Driven Nutritional Formulation Market, By Technology

5.1 Introduction
5.2 Machine Learning for Predictive Formulation
5.3 NLP for Ingredient Labeling & Consumer Feedback
5.4 Computer Vision for Ingredient Quality & Recognition
5.5 Formulation Software with AI-Driven Optimization
5.6 Biomarker-Linked AI Engines for Personalization
5.7 AI-IoT Integration for Real-Time Nutritional Monitoring

6 Global AI-Driven Nutritional Formulation Market, By Application

6.1 Introduction
6.2 Ingredient Selection Algorithms
6.3 Nutrient Profiling & Balancing
6.4 Custom Supplement Design
6.5 Functional Food Engineering
6.6 Allergen & Sensitivity Detection
6.7 Sensory Modeling
6.8 Health Outcome Prediction
6.9 Adaptive Nutrition Feedback Systems

7 Global AI-Driven Nutritional Formulation Market, By End User

7.1 Introduction
7.2 Nutraceutical Product Manufacturers
7.3 Functional Food Innovators
7.4 Personalized Nutrition Platforms
7.5 R&D Labs & Contract Formulators
7.6 Sports & Performance Nutrition Brands
7.7 Pet Nutrition & Animal Health Companies
7.8 Clinical & Therapeutic Nutrition Providers

8 Global AI-Driven Nutritional Formulation Market, By Geography

8.1 Introduction
8.2 North America
- 8.2.1 US
- 8.2.2 Canada
- 8.2.3 Mexico
8.3 Europe
- 8.3.1 Germany
- 8.3.2 UK
- 8.3.3 Italy
- 8.3.4 France
- 8.3.5 Spain
- 8.3.6 Rest of Europe
8.4 Asia Pacific
- 8.4.1 Japan
- 8.4.2 China
- 8.4.3 India
- 8.4.4 Australia
- 8.4.5 New Zealand
- 8.4.6 South Korea
- 8.4.7 Rest of Asia Pacific
8.5 South America
- 8.5.1 Argentina
- 8.5.2 Brazil
- 8.5.3 Chile
- 8.5.4 Rest of South America
8.6 Middle East & Africa
- 8.6.1 Saudi Arabia
- 8.6.2 UAE
- 8.6.3 Qatar
- 8.6.4 South Africa
- 8.6.5 Rest of Middle East & Africa

9 Key Developments

9.1 Agreements, Partnerships, Collaborations and Joint Ventures
9.2 Acquisitions & Mergers
9.3 New Product Launch
9.4 Expansions
9.5 Other Key Strategies

10 Company Profiling

10.1 Suggestic
10.2 EatLove
10.3 Viome Life Sciences
10.4 DNAfit (Prenetics)
10.5 DayTwo Ltd.
10.6 PIPA AI
10.7 NutrifyGenie AI
10.8 Amway
10.9 Nutrigenie
10.10 BetterMeal AI
10.11 Heali AI
10.12 Habit
10.13 Bioniq
10.14 ZOE
10.15 Nutrino

簡介目錄圖表

List of Tables

Table 1 Global AI-Driven Nutritional Formulation Market Outlook, By Region (2024-2032) ($MN)
Table 2 Global AI-Driven Nutritional Formulation Market Outlook, By Technology (2024-2032) ($MN)
Table 3 Global AI-Driven Nutritional Formulation Market Outlook, By Machine Learning for Predictive Formulation (2024-2032) ($MN)
Table 4 Global AI-Driven Nutritional Formulation Market Outlook, By NLP for Ingredient Labeling & Consumer Feedback (2024-2032) ($MN)
Table 5 Global AI-Driven Nutritional Formulation Market Outlook, By Computer Vision for Ingredient Quality & Recognition (2024-2032) ($MN)
Table 6 Global AI-Driven Nutritional Formulation Market Outlook, By Formulation Software with AI-Driven Optimization (2024-2032) ($MN)
Table 7 Global AI-Driven Nutritional Formulation Market Outlook, By Biomarker-Linked AI Engines for Personalization (2024-2032) ($MN)
Table 8 Global AI-Driven Nutritional Formulation Market Outlook, By AI-IoT Integration for Real-Time Nutritional Monitoring (2024-2032) ($MN)
Table 9 Global AI-Driven Nutritional Formulation Market Outlook, By Application (2024-2032) ($MN)
Table 10 Global AI-Driven Nutritional Formulation Market Outlook, By Ingredient Selection Algorithms (2024-2032) ($MN)
Table 11 Global AI-Driven Nutritional Formulation Market Outlook, By Nutrient Profiling & Balancing (2024-2032) ($MN)
Table 12 Global AI-Driven Nutritional Formulation Market Outlook, By Custom Supplement Design (2024-2032) ($MN)
Table 13 Global AI-Driven Nutritional Formulation Market Outlook, By Functional Food Engineering (2024-2032) ($MN)
Table 14 Global AI-Driven Nutritional Formulation Market Outlook, By Allergen & Sensitivity Detection (2024-2032) ($MN)
Table 15 Global AI-Driven Nutritional Formulation Market Outlook, By Sensory Modeling (2024-2032) ($MN)
Table 16 Global AI-Driven Nutritional Formulation Market Outlook, By Health Outcome Prediction (2024-2032) ($MN)
Table 17 Global AI-Driven Nutritional Formulation Market Outlook, By Adaptive Nutrition Feedback Systems (2024-2032) ($MN)
Table 18 Global AI-Driven Nutritional Formulation Market Outlook, By End User (2024-2032) ($MN)
Table 19 Global AI-Driven Nutritional Formulation Market Outlook, By Nutraceutical Product Manufacturers (2024-2032) ($MN)
Table 20 Global AI-Driven Nutritional Formulation Market Outlook, By Functional Food Innovators (2024-2032) ($MN)
Table 21 Global AI-Driven Nutritional Formulation Market Outlook, By Personalized Nutrition Platforms (2024-2032) ($MN)
Table 22 Global AI-Driven Nutritional Formulation Market Outlook, By R&D Labs & Contract Formulators (2024-2032) ($MN)
Table 23 Global AI-Driven Nutritional Formulation Market Outlook, By Sports & Performance Nutrition Brands (2024-2032) ($MN)
Table 24 Global AI-Driven Nutritional Formulation Market Outlook, By Pet Nutrition & Animal Health Companies (2024-2032) ($MN)
Table 25 Global AI-Driven Nutritional Formulation Market Outlook, By Clinical & Therapeutic Nutrition Providers (2024-2032) ($MN)