AI in the Food and Beverage Industry Statistics 2026

The global AI in food and beverages market is forecast to reach $18.34 billion in 2026, with one projection placing it at $88.37 billion by 2031. Adoption now spans factories, supply chains, restaurants, retail, product development and consumer engagement, but company-wide scaling remains limited. The figures below separate broad adoption claims from measured outcomes such as forecast accuracy, waste reduction, throughput and food safety. Because market definitions vary between estimates, each forecast should be read as an estimate rather than a settled industry total.

Report Highlights

The AI food and beverage market is forecast at $18.34 billion in 2026, up from $13.39 billion in 2025.
In 2025, 77% of food and beverage companies were using or implementing AI, up from 39% a year earlier.
In 2025, 16% of food and beverage manufacturers scaled over half their AI projects across sites, versus 30% in pharma.
In 2024, AI restaurant sales forecasts had a 17.6% peak-period error, versus 26.8% for manual estimates.
An AI fresh-food trial across more than 1,300 stores cut waste by 14.8% per store on average.
In 2025, 82% of restaurant executives planned to raise AI investment, while only 2% expected a cut.
In 2025, 83% of UK and US consumers wanted AI-created foods disclosed, while only 4% opposed it.
Only 28% of food and drink businesses have a clear AI roadmap, versus 39% across all industries.

Global Market Size and Growth

Forecasts for AI in food and beverages vary with how each estimate defines the category. These figures set out the headline market value, its growth path and the segments that hold the largest shares.

Market value and segments

The headline forecast is shown alongside its component, technology and regional splits, plus the technology expected to grow fastest to 2031.

$18.34 billion forecast global AI in food and beverages market value in 2026, up from $13.39 billion in 2025, a 37% rise.
$88.37 billion projected market value by 2031, 4.8 times the 2026 figure and equal to a 37% compound annual growth rate from 2026.
In 2025, software solutions generated 47% of market revenue, about $6.3 billion of the $13.39 billion total, making software the largest component.
In 2025, computer vision accounted for 42% of market revenue, about $5.6 billion, the largest share of any technology.
41% forecast annual growth for robotics and automation through 2031, the fastest of any technology and above the 37% market average.
In 2025, North America held 33% of the global market, the largest share of any region.

AI Adoption and Investment

Most food and beverage businesses have moved past the question of whether to use AI at all. The figures below track how quickly adoption and investment intent have shifted, and the digital groundwork underneath them.

Adoption and digital foundations

Year-on-year survey waves show AI use rising alongside the cloud and automation it depends on.

In 2025, 77% of surveyed food and beverage companies were implementing or already using AI, up from 39% a year earlier.
In 2025, only 3% of surveyed food and beverage companies had no plans to use AI, down from 33% in the previous survey wave.
In 2025, 96% of agri-food leaders had already reserved budget for future AI investment.
In 2025, 74% of food and beverage companies had completed or started cloud migration, up from 57% a year earlier.
In 2025, 58% of food and beverage companies described their operations as mostly or completely automated, up from 44% in the prior survey.

Manufacturing and Maintenance

Manufacturers are spending more on AI, but few have rolled it out across every plant. This section sets that scaling gap against the returns measured in one multi-year plant deployment.

Scaling and measured returns

Survey data on scaling and spending comes first, followed by results from one three-year industrial AI deployment.

In 2025, only 16% of food and beverage manufacturers had scaled more than half of their AI projects across all sites, second only to pharmaceuticals at 30%.
In 2025, 83% of food and beverage manufacturers planned to increase AI spending, despite limited company-wide scaling.
In 2026, 57% of manufacturers had deployed predictive maintenance, making it the most widely implemented AI use case across the four countries surveyed.
In 2025, a 310% three-year return on investment was calculated for one industrial AI deployment, with payback in under 6 months.
In 2025, one three-year industrial AI deployment avoided 10,000 hours of unplanned downtime and recovered more than 25 million pounds of product, about 11,300 tonnes.
In 2025, one three-year industrial AI deployment delivered $16.8 million in downtime savings, about $5.6 million a year, and cut maintenance spending by 15%.
In 2025, AI-led bottleneck reduction lifted plant throughput by 5% without a matching expansion in capacity.

Demand Forecasting and Supply Chains

Forecasting is where AI results are easiest to measure, because predictions can be checked against actual sales. The figures below cover error reduction in restaurants and bakeries, then the inventory and logistics savings that follow.

Forecast accuracy

Most of these results compare AI models directly with the manual estimates they replaced.

In 2024, an AI restaurant sales model cut root mean squared forecast error by 22% to 33% versus manual estimates, using 5 years of hourly sales data.
In 2024, AI restaurant sales forecasting cut mean absolute error by 19% to 31%, outperforming manual estimates in every evaluation period.
In 2024, AI restaurant sales forecasts had a peak-period error of 17.6%, versus 26.8% for manual estimates, a gap of 9.2 percentage points.
Up to 30% lower forecast error was recorded after a bakery network deployed AI forecasting across 11,000 delivery routes.

Inventory and logistics

Better forecasts feed directly into stock levels, order planning and freight costs.

25% higher forecast accuracy was reported by a global food and beverage company after replacing frozen forecasts with dynamic AI recommendations.
30% greater order-planning efficiency was recorded by a global food and beverage company after switching to dynamic AI forecasting.
15% lower air-freight costs were recorded within 6 months of AI forecasting deployment, cutting the cost of correcting inventory imbalances.
14% to 20% lower safety stock followed automated demand-driven forecasting, freeing working capital while protecting availability.

Quality Control and Food Safety

AI inspection is moving from lab benchmarks onto production lines and handheld devices. Most results below come from published studies, so they show what the technology can do under test conditions rather than average factory performance.

Inspection, pathogens and spoilage

Image-based grading and defect detection appear first, followed by pathogen and spoilage prediction.

In 2025, a food-quality image model achieved 98.7% classification accuracy and 100% recall, missing no defective items in testing.
In 2025, AI food-quality grading kept 98% accuracy across 2,000 smartphone trials, showing it can work outside controlled image datasets.
In 2024, AI detected foreign fragments in vegetables with 98.3% accuracy, taking 2.6 milliseconds per image.
90% to 95% accuracy is typical in vision-based fruit grading studies covering colour, size and defect classification.
In 2025, AI-enabled pathogen biosensors showed more than 90% sensitivity, although results came from controlled settings.
In 2025, AI forecasting of E. coli contamination reached up to 89% precision by combining weather, livestock and wastewater data.
In 2025, AI graded chilled pork freshness with 96% accuracy across 150 samples, combining imaging, sensors and laboratory reference tests.

Food Waste and Sustainability

Food waste is one of the largest cost and emissions problems AI is being used to tackle. The first set of figures sizes the problem, and the second shows what AI tools have achieved against it.

The scale of waste

These baseline figures cover waste at household, food service and retail level worldwide.

In 2022, 1.05 billion tonnes of food were wasted across households, food service and retail, equal to 19% of food available to consumers.
In 2022, food service wasted 290 million tonnes of food and retail 131 million tonnes, together 421 million tonnes or 40% of the 1.05 billion tonne total.
8% to 10% of global greenhouse gas emissions are linked to food loss and waste.
$1 trillion a year in economic costs is associated with global food waste, before counting wider social and environmental damage.

AI-enabled reductions

Store trials and kitchen pilots show what AI tools have delivered in live operations.

200 million pounds of food loss, about 90,700 tonnes, has been credited to AI demand forecasting across 26 countries.
14.8% average waste reduction per store was achieved in an AI fresh-food trial covering more than 1,300 retail locations.
23% to 51% lower food waste per meal was recorded at four of five AI kitchen pilot sites, while preparation waste fell by more than 80%.
$2.7 million in projected annual savings came from one grocery AI pilot, combining lower shrink, fresher stock and less ordering time.

Product Development and Innovation

AI is being used to shorten the slow, trial-heavy process of bringing new food products to market. The figures below compare AI-assisted timelines with typical development cycles and show why faster validation matters.

Development speed and launch risk

Each figure sets an AI-assisted project against the conventional timeline or failure rate it is trying to beat.

In 2025, one company cut concept development from 6 months to 6 weeks using AI, a 75% reduction.
10 months were needed to develop one AI-assisted packaged-food product, versus a typical 2 years, a 58% shorter timeline.
In 2019, AI identified a new snack flavour in under 3 weeks, cutting development time by more than 50%.
50% to 60% fewer formulation iterations are reported for some AI-assisted beverage projects, reducing repeated physical trials.
In 2025, an estimated 90% of new food and beverage products failed, making earlier validation one of the highest-value uses of AI.

Restaurants and Food Service

Restaurant AI adoption looks very different depending on who is asked. Executive surveys show heavy daily use and rising budgets, while broader operator surveys show most foodservice businesses have not yet started.

Executive investment and operator adoption

Executive priorities and daily use come first, followed by adoption rates across the wider operator base.

In 2025, 82% of global restaurant executives planned to increase AI investment in the next fiscal year, while only 2% expected a decrease.
In 2025, 60% of restaurant executives ranked better customer experience among their top three hoped-for AI benefits, ahead of restaurant operations at 36%.
In 2025, 31% of restaurant executives prioritised loyalty-programme improvement, while 25% targeted food preparation and waste management.
In 2025, 63% of restaurant executives surveyed reported daily AI use for customer experience, with another 26% running pilots or limited rollouts.
In 2025, 55% of restaurant executives surveyed reported daily AI use in inventory management, and a further 25% were testing it.
In 2025, only 21% of more than 550 foodservice operators used AI at all, while 51% were interested but had not adopted it.
In 2025, 28% of foodservice operators had no plans to adopt AI, a share unchanged for 2 years.
In 2025, 59% of restaurant operators planned to automate more online functions, compared with 43% targeting on-premise functions.

Consumer Attitudes and Trust

Consumers are split on AI-made food, but they agree strongly on one point: they want to be told when it is used. The figures below cover acceptance, disclosure expectations and what shapes trust.

Acceptance, disclosure and trust

Acceptance and disclosure figures come first, followed by the safety concerns that separate likely buyers from sceptics.

In 2025, 42% of UK and US consumers viewed AI in food production and design positively, leaving support below half.
In 2025, 83% of UK and US consumers wanted companies to disclose when AI was used to create food products, while only 4% opposed disclosure.
In 2025, 64% of US consumers rated disclosure of AI use in food production as very or extremely important, a stricter test than simply wanting disclosure.
In 2025, 48% of consumers surveyed were open to AI analysing their genetics for nutrition recommendations, compared with 29% who opposed it.
In 2025, 45% of consumers surveyed said they were likely to choose an AI-assisted food over an otherwise identical product, while 29% were neutral.
In 2025, 70% of consumers unlikely to choose AI-assisted food cited doubts about its food safety, making safety the leading trust barrier.
In 2025, 53% of consumers likely to choose AI-assisted food believed AI could improve food safety, the mirror image of sceptics' concerns.
In 2025, 47% of global consumers were comfortable using generative AI for at least one grocery or meal-planning task.

Implementation Barriers and Workforce

Cost, old systems and skills gaps are holding back AI more than lack of interest. These figures show where food businesses are getting stuck and how they compare with other industries.

Systems and data

Technology and data barriers are shown here, several with cross-industry comparisons.

In 2025, 70% of food-industry respondents cited cost as a barrier to digital transformation.
In 2025, 31% of food-industry respondents identified employee reskilling as a barrier to digital transformation, while 28% cited weak support between departments.
In 2026, only 28% of food and drink businesses had a clear AI integration roadmap, below the 39% cross-industry rate.
In 2026, 38% of food and drink businesses identified legacy IT as a major AI barrier, compared with 28% across all industries surveyed.
31% of food and drink businesses selected data quality, availability or governance as a leading AI obstacle.

Skills and operating models

Workforce readiness and day-to-day integration make up the other half of the scaling problem.

In 2026, 35% of food and drink businesses cited shortages of in-house expertise and skills, making internal capability a central scaling constraint.
40% of more than 400 food and beverage professionals named disconnected systems and data as their biggest barrier to speed.
85% of 300 food and beverage decision-makers said integration with core business systems is the real AI challenge.
In 2026, only 23% of food and beverage decision-makers had made AI essential to daily operations, even though 83% believed laggards risked obsolescence.
In 2026, 60% of workers said they needed additional AI training, while confidence in using AI fell to 64% from 78%.

2026 Outlook

Investment intent for 2026 is close to universal, but the data and confidence needed to use AI well are lagging behind. This section closes with the regulatory change now applying to companies that deploy AI in the EU.

Investment plans and priorities

Planned spending by technology is set against how well businesses are using the data and tools they already have.

In 2026, 96% of food and beverage organisations have invested in or plan to invest in generative or causal AI.
In 2026, 93% of food and beverage organisations have invested in or plan to invest in machine learning, just behind generative AI at 96%.
In 2026, 95% of food and beverage organisations are evaluating, piloting or using smart manufacturing technology.
In 2026, only 45% of collected operational data is used effectively, leaving 55% underused.
In 2026, 84% of food and beverage decision-makers say they are adopting AI for its capabilities, not simply because of competitor pressure.
In 2026, 72% of food and beverage brands use AI for personalisation, yet only 6% feel very confident their technology stack can meet future needs.
From 2 August 2026, EU AI Act transparency rules and enforcement began to apply, including to food companies that deploy covered AI systems.

Sources

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