AI in Food Market (By Technology: Machine Learning, Computer Vision, Robotics and Automation; By Application: Precision Agriculture, Food Processing, Supply Chain Management, Retail Services; By End-user: Food Manufacturers, Farmers and Growers, Restaurants, Others) - Global Industry Analysis, Size, Share, Growth, Trends, Regional Outlook, and Forecast 2024-2033
The global AI in food market size is expected to rise with an impressive growth rate and generate the highest revenue during the period 2024 to 2033. AI in food market is revolutionizing the whole food industry by pursuing improved efficiency, personalized nutrition, and data-driven decision-making.
To Access our Exclusive Data Intelligence Tool with 15000+ Database, Visit: Precedence Statistics
The AI in food market offers the application of artificial intelligence (AI) technologies in various aspects of the food industry to enhance efficiency, innovation, and overall operational processes. AI is utilized to optimize and streamline food supply chains, from production and distribution to inventory management, reducing waste and enhancing overall efficiency.
Culinary innovation
AI-powered kitchen appliances and culinary applications bring innovation to home cooking, offering personalized recipes, cooking assistance, and flavor profiling. This trend in AI food market is observed to enhance the utilization of AI technology owing to the requirement for personalized diet plans.
Menu optimization
Restaurants are leveraging artificial intelligence to analyze customer preferences, optimize menus, and tailor offerings to match trends, enhancing the dining experience and operational efficiency.
AI in Food, Predictive Analytics and Cost Reduction
With its capabilities, the integration of AI in food is observed to be helpful to reduce the overall cost for food and beverage businesses. With the advanced capability of predictive analytics, AI is capable of offering solutions for product launch, success rate along with safety monitoring.
The cost reduction offered by such capabilities is observed to act as a supplement to the growth of AI in food market.
Report Coverage | Details |
Largest Market | North America |
Base Year | 2023 |
Forecast Period | 2024 to 2033 |
Segments Covered | By Technology, By Application, and By End-user |
Regions Covered | North America, Europe, Asia-Pacific, Latin America, and Middle East & Africa |
Driver
Utilization of AI by online food delivery businesses
Online food delivery businesses are customizing consumer experiences by using AI technologies. By monitoring user behaviour and preferences, recommender systems make relevant meal recommendations that increase user pleasure and loyalty. AI assists in anticipating client demand trends, which enables companies to maximise their stock levels. This reduces food waste and lowers the possibility of stockouts by ensuring that popular foods are sufficiently stocked. Thereby, the rising utilization of AI technology by online food delivery platforms is observed to act as a driver for the AI in food market.
Restraint
Lack of standardized data
Without standardized data formats and structures, interoperability between different systems becomes challenging. This can hinder the seamless integration of AI solutions across various stages of the food supply chain, from production to distribution and retail. A degree of quality and consistency is guaranteed by standardised data. The accuracy and dependability of AI algorithms may be severely impacted by inconsistent data formats, inaccuracies, and insufficient information in the absence of standards.
Opportunity
Rising emphasis on understanding consumer patterns
AI algorithms can analyze vast amounts of consumer data to understand individual preferences, dietary restrictions, and consumption patterns. This enables the delivery of personalized food recommendations, creating a more tailored and satisfying culinary experience for consumers. This acts as a significant opportunity for AI in food market. Restaurants and food services can leverage AI to analyze customer preferences and tailor their menus accordingly. This allows for dynamic menu adjustments, promoting dishes that align with popular trends and individual tastes.
The machine learning segment held the dominating share of the AI in food market in 2023. Machine learning algorithms are employed to optimize supply chain processes, from predicting demand and optimizing inventory levels to streamlining logistics and distribution. This helps reduce waste, lower costs, and improve overall supply chain efficiency. Machine learning is used to analyze customer preferences, popular trends, and historical sales data to optimize menus for restaurants and cafes. This assists in creating appealing and profitable menu offerings.
The robotics and automation segment is observed to witness a significant rate of growth during the forecast period. AI-driven robotics are used in precision agriculture for tasks like planting, harvesting, and monitoring crop health. Autonomous vehicles equipped with AI can navigate fields, analyze data, and optimize farming practices, leading to increased crop yields and resource efficiency.
The food processing segment dominated the AI in food market in 2023 and the segment is observed to witness a notable growth in the upcoming years. AI systems enable better traceability of food products throughout the processing journey. In case of recalls, the technology helps identify affected batches swiftly, enhancing food safety. AI facilitates predictive maintenance in food processing equipment. By analyzing data from sensors and machinery, AI systems can predict when equipment might require maintenance, minimizing downtime and disruptions in the processing line.
On the other hand, the supply chain management segment is observed to expand at a rapid pace during the forecast period. AI-powered systems automate inventory management by continuously monitoring stock levels, expiration dates, and other factors. This ensures better control over inventory, minimizes stockouts, and reduces excess inventory holding costs.
The food manufacturers segment led the market in 2023, the segment also holds a noteworthy potential for growth for the predicted timeframe. AI enables food manufacturers to optimize their supply chains by predicting demand, managing inventory efficiently, and enhancing overall logistics. This results in improved production planning and reduced wastage.
Manufacturing of food is more efficient when regular operations and procedures are automated with AI-driven robotics and machine learning. This involves operations like handling, packing, and sorting, freeing up human labour for more difficult jobs. AI technology help manufacturing plants use less energy. AI algorithms are used by smart energy management systems to improve energy use, lowering expenses and impact on the environment. By maximising resource utilisation, cutting waste, and enhancing overall environmental effect, AI assists food makers in implementing sustainable practices.
The restaurants segment is observed to witness the fastest rate of expansion in the AI in food market during the forecast period. AI technologies enable restaurants to offer personalized and seamless customer experiences. From AI-powered chatbots handling orders to personalized menu recommendations based on customer preferences, the focus is on improving overall satisfaction. AI-powered predictive maintenance ensures that restaurant equipment, such as ovens and refrigerators, is well-maintained. This reduces downtime, operational disruptions, and unexpected repair costs.
North America dominated the AI in food market with the largest share in 2023.
North America is observed to sustain the dominance in the upcoming years. The United States, in North America constitute a global centre of technical innovation. Leading technological firms and academic institutions are present in the country, which encourages the creation and application of AI solutions in the food sector. With universities, research centres, and private businesses actively involved in AI research, the area has a strong ecosystem for research and development.
This cooperative setting speeds up the development of AI applications for a range of industries, including the food sector. Large sums of money are invested in AI startups and IT enterprises in North America. The development and expansion of AI solutions for the food sector, such as supply chain optimisation and precision agriculture, are made possible by this capital inflow. This element encourages the region's AI in food market expansion.
Key advancements in North America AI in food market
Collaboration between tech companies, startups, and established players in the food industry is common in North America. These collaborations often lead to the development and integration of AI solutions into existing food production and distribution systems.
Asia Pacific is observing the fastest expansion in the market with noteworthy growth
While expanding at a rapid pace, Asia Pacific poses a strong position in the AI in food market owing to the ongoing technological advancements in the region. Asia Pacific countries have been quick to adopt new technologies. Increased connectivity and tech-savvy consumers contribute to the rapid integration of AI solutions in the food industry. With a large and increasingly urbanized population, the demand for convenient and tech-driven solutions in the food sector is rising. AI technologies offer efficiency in production, distribution, and delivery.
The surge in online food delivery and e-commerce platforms in the Asia Pacific region creates opportunities for AI applications in optimizing delivery routes, personalizing recommendations, and managing logistics.
Key advancements in the Asia Pacific AI in food market
Segments Covered in the Report
By Technology
By Application
By End-user
By Geography
Chapter 1. Introduction
1.1. Research Objective
1.2. Scope of the Study
1.3. Definition
Chapter 2. Research Methodology (Premium Insights)
2.1. Research Approach
2.2. Data Sources
2.3. Assumptions & Limitations
Chapter 3. Executive Summary
3.1. Market Snapshot
Chapter 4. Market Variables and Scope
4.1. Introduction
4.2. Market Classification and Scope
4.3. Industry Value Chain Analysis
4.3.1. Raw Material Procurement Analysis
4.3.2. Sales and Distribution Channel Analysis
4.3.3. Downstream Buyer Analysis
Chapter 5. COVID 19 Impact on AI in Food Market
5.1. COVID-19 Landscape: AI in Food Industry Impact
5.2. COVID 19 - Impact Assessment for the Industry
5.3. COVID 19 Impact: Global Major Government Policy
5.4. Market Trends and Opportunities in the COVID-19 Landscape
Chapter 6. Market Dynamics Analysis and Trends
6.1. Market Dynamics
6.1.1. Market Drivers
6.1.2. Market Restraints
6.1.3. Market Opportunities
6.2. Porter’s Five Forces Analysis
6.2.1. Bargaining power of suppliers
6.2.2. Bargaining power of buyers
6.2.3. Threat of substitute
6.2.4. Threat of new entrants
6.2.5. Degree of competition
Chapter 7. Competitive Landscape
7.1.1. Company Market Share/Positioning Analysis
7.1.2. Key Strategies Adopted by Players
7.1.3. Vendor Landscape
7.1.3.1. List of Suppliers
7.1.3.2. List of Buyers
Chapter 8. Global AI in Food Market, By Technology
8.1. AI in Food Market, by Technology, 2024-2033
8.1.1 Machine Learning
8.1.1.1. Market Revenue and Forecast (2021-2033)
8.1.2. Computer Vision
8.1.2.1. Market Revenue and Forecast (2021-2033)
8.1.3. Robotics and Automation
8.1.3.1. Market Revenue and Forecast (2021-2033)
Chapter 9. Global AI in Food Market, By Application
9.1. AI in Food Market, by Application, 2024-2033
9.1.1. Precision Agriculture
9.1.1.1. Market Revenue and Forecast (2021-2033)
9.1.2. Food Processing
9.1.2.1. Market Revenue and Forecast (2021-2033)
9.1.3. Supply Chain Management
9.1.3.1. Market Revenue and Forecast (2021-2033)
9.1.4. Retail Services
9.1.4.1. Market Revenue and Forecast (2021-2033)
Chapter 10. Global AI in Food Market, By End-user
10.1. AI in Food Market, by End-user, 2024-2033
10.1.1. Food Manufacturers
10.1.1.1. Market Revenue and Forecast (2021-2033)
10.1.2. Farmers and Growers
10.1.2.1. Market Revenue and Forecast (2021-2033)
10.1.3. Restaurants
10.1.3.1. Market Revenue and Forecast (2021-2033)
10.1.4. Others
10.1.4.1. Market Revenue and Forecast (2021-2033)
Chapter 11. Global AI in Food Market, Regional Estimates and Trend Forecast
11.1. North America
11.1.1. Market Revenue and Forecast, by Technology (2021-2033)
11.1.2. Market Revenue and Forecast, by Application (2021-2033)
11.1.3. Market Revenue and Forecast, by End-user (2021-2033)
11.1.4. U.S.
11.1.4.1. Market Revenue and Forecast, by Technology (2021-2033)
11.1.4.2. Market Revenue and Forecast, by Application (2021-2033)
11.1.4.3. Market Revenue and Forecast, by End-user (2021-2033)
11.1.5. Rest of North America
11.1.5.1. Market Revenue and Forecast, by Technology (2021-2033)
11.1.5.2. Market Revenue and Forecast, by Application (2021-2033)
11.1.5.3. Market Revenue and Forecast, by End-user (2021-2033)
11.2. Europe
11.2.1. Market Revenue and Forecast, by Technology (2021-2033)
11.2.2. Market Revenue and Forecast, by Application (2021-2033)
11.2.3. Market Revenue and Forecast, by End-user (2021-2033)
11.2.4. UK
11.2.4.1. Market Revenue and Forecast, by Technology (2021-2033)
11.2.4.2. Market Revenue and Forecast, by Application (2021-2033)
11.2.4.3. Market Revenue and Forecast, by End-user (2021-2033)
11.2.5. Germany
11.2.5.1. Market Revenue and Forecast, by Technology (2021-2033)
11.2.5.2. Market Revenue and Forecast, by Application (2021-2033)
11.2.5.3. Market Revenue and Forecast, by End-user (2021-2033)
11.2.6. France
11.2.6.1. Market Revenue and Forecast, by Technology (2021-2033)
11.2.6.2. Market Revenue and Forecast, by Application (2021-2033)
11.2.6.3. Market Revenue and Forecast, by End-user (2021-2033)
11.2.7. Rest of Europe
11.2.7.1. Market Revenue and Forecast, by Technology (2021-2033)
11.2.7.2. Market Revenue and Forecast, by Application (2021-2033)
11.2.7.3. Market Revenue and Forecast, by End-user (2021-2033)
11.3. APAC
11.3.1. Market Revenue and Forecast, by Technology (2021-2033)
11.3.2. Market Revenue and Forecast, by Application (2021-2033)
11.3.3. Market Revenue and Forecast, by End-user (2021-2033)
11.3.4. India
11.3.4.1. Market Revenue and Forecast, by Technology (2021-2033)
11.3.4.2. Market Revenue and Forecast, by Application (2021-2033)
11.3.4.3. Market Revenue and Forecast, by End-user (2021-2033)
11.3.5. China
11.3.5.1. Market Revenue and Forecast, by Technology (2021-2033)
11.3.5.2. Market Revenue and Forecast, by Application (2021-2033)
11.3.5.3. Market Revenue and Forecast, by End-user (2021-2033)
11.3.6. Japan
11.3.6.1. Market Revenue and Forecast, by Technology (2021-2033)
11.3.6.2. Market Revenue and Forecast, by Application (2021-2033)
11.3.6.3. Market Revenue and Forecast, by End-user (2021-2033)
11.3.7. Rest of APAC
11.3.7.1. Market Revenue and Forecast, by Technology (2021-2033)
11.3.7.2. Market Revenue and Forecast, by Application (2021-2033)
11.3.7.3. Market Revenue and Forecast, by End-user (2021-2033)
11.4. MEA
11.4.1. Market Revenue and Forecast, by Technology (2021-2033)
11.4.2. Market Revenue and Forecast, by Application (2021-2033)
11.4.3. Market Revenue and Forecast, by End-user (2021-2033)
11.4.4. GCC
11.4.4.1. Market Revenue and Forecast, by Technology (2021-2033)
11.4.4.2. Market Revenue and Forecast, by Application (2021-2033)
11.4.4.3. Market Revenue and Forecast, by End-user (2021-2033)
11.4.5. North Africa
11.4.5.1. Market Revenue and Forecast, by Technology (2021-2033)
11.4.5.2. Market Revenue and Forecast, by Application (2021-2033)
11.4.5.3. Market Revenue and Forecast, by End-user (2021-2033)
11.4.6. South Africa
11.4.6.1. Market Revenue and Forecast, by Technology (2021-2033)
11.4.6.2. Market Revenue and Forecast, by Application (2021-2033)
11.4.6.3. Market Revenue and Forecast, by End-user (2021-2033)
11.4.7. Rest of MEA
11.4.7.1. Market Revenue and Forecast, by Technology (2021-2033)
11.4.7.2. Market Revenue and Forecast, by Application (2021-2033)
11.4.7.3. Market Revenue and Forecast, by End-user (2021-2033)
11.5. Latin America
11.5.1. Market Revenue and Forecast, by Technology (2021-2033)
11.5.2. Market Revenue and Forecast, by Application (2021-2033)
11.5.3. Market Revenue and Forecast, by End-user (2021-2033)
11.5.4. Brazil
11.5.4.1. Market Revenue and Forecast, by Technology (2021-2033)
11.5.4.2. Market Revenue and Forecast, by Application (2021-2033)
11.5.4.3. Market Revenue and Forecast, by End-user (2021-2033)
11.5.5. Rest of LATAM
11.5.5.1. Market Revenue and Forecast, by Technology (2021-2033)
11.5.5.2. Market Revenue and Forecast, by Application (2021-2033)
11.5.5.3. Market Revenue and Forecast, by End-user (2021-2033)
Chapter 12. Company Profiles
12.1. Google LLC
12.1.1. Company Overview
12.1.2. Product Offerings
12.1.3. Financial Performance
12.1.4. Recent Initiatives
12.2. IBM Corporation
12.2.1. Company Overview
12.2.2. Product Offerings
12.2.3. Financial Performance
12.2.4. Recent Initiatives
12.3. ABB Ltd
12.3.1. Company Overview
12.3.2. Product Offerings
12.3.3. Financial Performance
12.3.4. Recent Initiatives
12.4. NVIDIA Corporation
12.4.1. Company Overview
12.4.2. Product Offerings
12.4.3. Financial Performance
12.4.4. Recent Initiatives
12.5. Microsoft Corporation
12.5.1. Company Overview
12.5.2. Product Offerings
12.5.3. Financial Performance
12.5.4. Recent Initiatives
12.6. SAP SE
12.6.1. Company Overview
12.6.2. Product Offerings
12.6.3. Financial Performance
12.6.4. Recent Initiatives
12.7. Buhler Group
12.7.1. Company Overview
12.7.2. Product Offerings
12.7.3. Financial Performance
12.7.4. Recent Initiatives
12.8. Impact Vision
12.8.1. Company Overview
12.8.2. Product Offerings
12.8.3. Financial Performance
12.8.4. Recent Initiatives
Chapter 13. Research Methodology
13.1. Primary Research
13.2. Secondary Research
13.3. Assumptions
Chapter 14. Appendix
14.1. About Us
14.2. Glossary of Terms
No cookie-cutter, only authentic analysis – take the 1st step to become a Precedence Research client