The industrial AI market is forecast to grow by 23.2 percent annually through 2026. In baking, this shift is already measurable, with machine vision cutting inspection times by up to 90 percent and reducing scrap by 20 to 50 percent.
Key Takeaways
- 12% of manufacturers reported not using AI/ML but planning to (2023)
- 31% of manufacturers report using AI/ML for predictive maintenance—indicating demand for AI models in equipment monitoring within industrial operations
- 38% of organizations report deploying AI models into production already (2024 survey)—showing operational maturity for manufacturing use cases
- 14% of jobs in OECD countries are at high risk of automation by AI (scenario estimate)
- 60% of executives said AI will create new jobs rather than eliminate them (2024 survey)
- Re-skilling/upskilling is the most-cited required response (55% of executives) in 2023 WEF survey
- The global predictive maintenance market is expected to grow from $7.7B in 2022 to $19.9B by 2027 (MarketsandMarkets)
- The industrial AI market is expected to grow at a CAGR of 23.2% from 2021 to 2026 (MarketsandMarkets)
- The global AI in retail market is projected to reach $7.3B by 2024 (Statista/Digital Market Insights)
- Pathogens cause about 420,000 deaths per year from foodborne disease from selected bacterial, parasitic, viral and chemical hazards (WHO)
- The FAO estimates food loss and waste equals 8% of global GHG emissions (FAO)
- Machine learning-based quality inspection systems can reduce inspection time by 50–90% compared with manual checks (computer vision benchmarking review)—a measurable productivity impact for baked goods QA
- US manufacturing labor productivity increased about 2.1% per year from 2017 to 2022 (BLS, labor productivity and costs)
- The average total cost of a data breach rose 15% year over year in 2023 (IBM)
- AI can reduce energy consumption in industrial settings by 10–20% (IEA report on energy efficiency and AI)
AI adoption is accelerating in baking, improving maintenance, quality, and food safety while driving reskilling.
Related reading
01 · Category
User Adoption3 stats
01
12% of manufacturers reported not using AI/ML but planning to (2023)
02
31% of manufacturers report using AI/ML for predictive maintenance—indicating demand for AI models in equipment monitoring within industrial operations
03
38% of organizations report deploying AI models into production already (2024 survey)—showing operational maturity for manufacturing use cases
Interpretation
User Adoption Interpretation
User adoption is accelerating in manufacturing baking operations, with 38% of organizations already deploying AI models into production and 31% using AI/ML for predictive maintenance, while 12% of manufacturers still not using it but planning to, signaling a clear ramp-up toward wider everyday use.
02 · Category
Workforce Impact3 stats
01
14% of jobs in OECD countries are at high risk of automation by AI (scenario estimate)
02
60% of executives said AI will create new jobs rather than eliminate them (2024 survey)
03
Re-skilling/upskilling is the most-cited required response (55% of executives) in 2023 WEF survey
Interpretation
Workforce Impact Interpretation
In the workforce impact lens, while 14% of OECD jobs are estimated to be at high risk of AI automation, 60% of executives expect AI to create new roles and 55% say re-skilling and upskilling is the key response, signaling a shift toward managed transition rather than job loss.
03 · Category
Market Size5 stats
01
The global predictive maintenance market is expected to grow from $7.7B in 2022 to $19.9B by 2027 (MarketsandMarkets)
02
The industrial AI market is expected to grow at a CAGR of 23.2% from 2021 to 2026 (MarketsandMarkets)
03
The global AI in retail market is projected to reach $7.3B by 2024 (Statista/Digital Market Insights)
04
The machine learning in food & beverage market is projected to grow to $1.2B by 2027 (Research and Markets)
05
In the US, retail flour prices increased year-over-year from 2020 to 2022 (US BLS Producer/Consumer data for flour)
Interpretation
Market Size Interpretation
For the market size perspective, AI adoption signals strong growth momentum across related sectors, with predictive maintenance expected to jump from $7.7B in 2022 to $19.9B by 2027 and industrial AI projected to rise at a 23.2% CAGR from 2021 to 2026, which suggests a widening economic opportunity for AI in baking operations and supply chains.
04 · Category
Performance Metrics8 stats
01
Pathogens cause about 420,000 deaths per year from foodborne disease from selected bacterial, parasitic, viral and chemical hazards (WHO)
02
The FAO estimates food loss and waste equals 8% of global GHG emissions (FAO)
03
Machine learning-based quality inspection systems can reduce inspection time by 50–90% compared with manual checks (computer vision benchmarking review)—a measurable productivity impact for baked goods QA
04
Shelf-life extension claims using predictive models and optimization are reported to increase usable product life by up to 20% in controlled supply-chain conditions (peer-reviewed review)—supporting AI-enabled freshness management
05
Computer vision inspection can increase yield and reduce defects; one industrial case study reports a 15% reduction in defect rate after implementing vision-based SPC for manufacturing (peer-reviewed / proceedings publication)—a quality metric for baking QA
06
In food production trials using AI for process control, temperature/time control errors can be reduced by 30–60% relative to baseline controllers (review of AI process control)—a direct metric for process stability
07
AI forecasting can reduce inventory costs by 10–20% in retail and consumer supply chains (peer-reviewed operations research and industry synthesis)—relevant to flour/sugar/ingredient inventory
08
A review of machine-vision inspection systems reports typical defect detection accuracies of 90%+ for industrial food inspection tasks under controlled conditions—supporting measurable quality outcomes
Interpretation
Performance Metrics Interpretation
Across performance metrics, AI in baking is showing measurable gains, including cutting inspection time by 50–90 percent and reducing process control errors by 30–60 percent, while improvements in quality and shelf life can extend usable product life by up to 20 percent.
More related reading
05 · Category
Cost Analysis7 stats
01
US manufacturing labor productivity increased about 2.1% per year from 2017 to 2022 (BLS, labor productivity and costs)
02
The average total cost of a data breach rose 15% year over year in 2023 (IBM)
03
AI can reduce energy consumption in industrial settings by 10–20% (IEA report on energy efficiency and AI)
04
Computer vision quality inspection can reduce scrap and rework by 20–50% (IDC industry briefing)
05
Cybercrime costs are projected to reach $10.5 trillion annually by 2025 (estimate from a global cyber risk report based on industry models)—driving investment in secure AI adoption
06
In the US, the average industrial energy price for natural gas was about $5.60per thousand cubic feet in 2023 (EIA)—supporting the business case for AI optimization of energy-intensive baking processes
07
In the US, industrial electricity prices averaged about 13.9 cents per kWh in 2023 (EIA)—a measurable input cost for AI optimization in large ovens and HVAC
Interpretation
Cost Analysis Interpretation
From a cost analysis perspective, AI and related technologies could materially lower manufacturing expenses since computer vision can cut scrap and rework by 20–50% while data-breach costs climbed 15% year over year in 2023, making tighter cybersecurity and efficiency gains especially valuable.
06 · Category
Environmental Impact3 stats
01
2.3% of all global food value-chain greenhouse gas emissions come from post-harvest losses (2017–2018 estimate range: 1.6–5.5%)—showing the magnitude of waste drivers that AI could help reduce
02
Foodborne illness affects 600 million people globally each year (one in 10)—underscoring the potential public-health value of AI-enabled detection, inspection, and risk scoring
03
In the US, 48.8 million people (about 14.1%) were sick from foodborne illnesses each year (CDC estimate timeframe: 2011–2016)—supporting demand for advanced food-safety analytics
Interpretation
Environmental Impact Interpretation
Because post-harvest losses account for 2.3% of global food value-chain greenhouse gas emissions, with estimates ranging from 1.6% to 5.5%, reducing waste in the baking supply chain is a clear environmental-impact win where AI could help.
07 · Category
Industry Trends3 stats
01
AI use in supply chain functions is growing fastest in planning and scheduling, with 46% of organizations using analytics/AI for this purpose (2022–2023 survey)—relevant to bakery ingredient procurement and production scheduling
02
The global market for industrial AI is forecast to reach $18.9B by 2024 and $60.8B by 2030 (IDC, 2021)—quantifying the long-run investment rationale for AI in manufacturing
03
35% of organizations report that AI projects are hindered by data quality issues (2023 enterprise survey)—a key constraint for bakery process data readiness
Interpretation
Industry Trends Interpretation
Under Industry Trends, AI adoption in baking operations is accelerating where it can drive planning and scheduling, with 46% of organizations already using analytics for that purpose, even as 35% say data quality issues still slow AI projects and the broader industrial AI market is projected to surge from $18.9B by 2024 to $60.8B by 2030.
report visual · Key figures
AI adoption in manufacturing is moving from pilots to production
A growing share of organizations are already deploying AI models in production, while others are planning to adopt—signaling accelerating implementation in industrial operations relevant to baking.
31%
31% of manufacturers report using AI/ML for predictive maintenance—indicating demand for AI models in equipment monitori
38%
38% of organizations report deploying AI models into production already (2024 survey)—showing operational maturity for m
12%
12% of manufacturers reported not using AI/ML but planning to (2023)
source-verifiedgartner.com · ibm.com2024
Reference
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
APA
Marie Larsen. (2026, February 13). AI In The Baking Industry Statistics. Gitnux. https://gitnux.org/ai-in-the-baking-industry-statistics
MLA
Marie Larsen. "AI In The Baking Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/ai-in-the-baking-industry-statistics.
Chicago
Marie Larsen. 2026. "AI In The Baking Industry Statistics." Gitnux. https://gitnux.org/ai-in-the-baking-industry-statistics.
Sources & references
32 datasets cited across this report · attribution is report-level
+11 additional datasets cited (not shown individually)