Food and beverage companies face an increasingly difficult balancing act. Input costs remain elevated across commodities, packaging, energy and freight, while consumers and retailers are showing less willingness to absorb higher prices. As pricing power weakens, many organizations are looking inward for new ways to improve efficiency, reduce waste and make better decisions.
That shift is helping accelerate adoption of artificial intelligence across the industry. While generative AI applications often attract the most attention, there are other behind-the-scenes uses that deliver value to organizations. From demand forecasting and supply chain planning to predictive maintenance and quality inspection, AI is helping food and beverage companies improve operational performance and protect margins in a challenging economic environment.
Margin pressure is forcing a new approach
The industry’s profitability challenges are difficult to ignore. Over the past year, several major agricultural commodities have experienced significant price increases, including wheat, corn, soybeans and sugar. At the same time, food-at-home inflation has risen far more slowly, limiting producers’ ability to pass higher costs on to customers.
The impact is visible in company financial results. Beverage manufacturers with strong brands and premium pricing power have generally maintained higher margins, while many packaged food producers operate with significantly thinner cushions. Protein processors often face even tighter economics, operating on comparatively slim margins that leave little room for cost increases.
Additional pressures continue to emerge. Palm oil, coffee, cocoa, tea and dairy costs remain elevated, while freight, energy and packaging expenses have also increased. According to Bloomberg Intelligence, analysts estimate that even relatively modest increases in packaging inputs such as PET resin can have a meaningful impact on operating profit.
Against this backdrop, organizations are increasingly viewing AI as a business tool rather than a technology experiment. The question is no longer whether AI can provide value, but where it can deliver the greatest return.
AI’s greatest impact is happening behind the scenes
Across the food and beverage ecosystem, AI adoption is concentrated in operational functions that directly influence profitability.
Supply chain management and demand forecasting remain among the most common applications, particularly within grocery and retail organizations. Consumer packaged goods companies are investing heavily in operations and predictive maintenance, while manufacturers continue expanding their use of AI for quality assurance and process optimization.
A clear pattern has emerged: Companies are prioritizing AI deployments that help improve forecast accuracy, reduce downtime, optimize inventory levels and strengthen supply chain performance. While customer-facing applications continue to grow, the most immediate returns are often found in processes that directly affect costs and operational efficiency.
Moving beyond experimentation
Recent RSM middle market survey data suggests AI adoption is becoming increasingly widespread. Most respondents reported integrating AI into at least some business operations, though significantly fewer indicated that AI is fully embedded across core processes.
The survey also highlighted the outcomes organizations hope to achieve. Improved decision-making and more accurate forecasting were among the most frequently cited objectives, reinforcing the industry’s focus on using AI to drive operational performance.
At the same time, many companies continue to face challenges scaling their initiatives. More than half of respondents characterized recent AI pilots as only moderately successful or limited in impact. Data quality and system integration issues were cited as leading obstacles, underscoring the importance of building a strong foundation before expanding AI investments. These same conditions are also seen in food and beverage companies.
Four areas companies are realizing value from AI
Improving forecast accuracy and planning
Demand forecasting has become increasingly difficult as consumer preferences shift, supply chains remain volatile and input costs fluctuate. AI allows organizations to process large volumes of historical, operational and market data to improve forecasting accuracy and accelerate planning cycles.
Organizations deploying AI-enabled forecasting tools have reported improvements in forecast accuracy, planning efficiency and inventory management. Better forecasting can help reduce excess inventory, improve product availability and support faster decision-making across supply chain and commercial functions.
Increasing manufacturing efficiency
Manufacturers are increasingly using AI to optimize production processes and improve equipment reliability.
Machine learning models can analyze production variables in real time, helping operators adjust processes, identify inefficiencies and detect potential equipment failures before they occur. Predictive maintenance applications, in particular, are helping organizations reduce unplanned downtime, improve throughput and extend asset life.
For food and beverage companies operating on thin margins, even modest gains in plant efficiency can have a meaningful impact on profitability.
Strengthening quality and compliance
Quality assurance remains a critical priority across the industry, particularly as regulatory requirements and consumer expectations continue to evolve.
Computer vision, sensor technologies and advanced analytics are enabling organizations to identify defects, contamination risks and packaging issues more quickly and consistently than traditional inspection methods. These capabilities can help reduce waste, improve product quality and strengthen compliance efforts while allowing employees to focus on higher-value activities.
Improving supply chain visibility and resilience
Supply chain complexity continues to increase, creating new challenges around sourcing, logistics and risk management.
AI is helping organizations gain greater visibility into supply chain operations by improving demand sensing, inventory management and scenario planning. Advanced analytics can also support commodity purchasing decisions, logistics optimization and weather-related risk assessments, helping organizations make more informed decisions in uncertain market conditions.
As supply chains become increasingly interconnected, these capabilities can serve as an important competitive differentiator.
Product innovation may be the next frontier
While many current AI investments focus on operational efficiency, organizations are beginning to explore how the technology can support product development and innovation.
AI can help analyze consumer preferences, identify emerging flavor and ingredient trends, evaluate product formulations and accelerate development cycles. Although many of these applications remain in the early stages, they highlight a broader shift in how organizations may use AI in the future.
As adoption matures, AI’s value proposition may extend beyond cost reduction and operational efficiency to include growth, innovation and speed-to-market advantages.
Turning AI ambition into measurable value
For many food and beverage organizations, AI is moving from a competitive advantage to a business necessity. However, successful adoption requires more than selecting the right technology.
Organizations generating the greatest value from AI typically begin with a clearly defined business objective rather than an AI objective. They invest in data quality, ensuring models are built on reliable information. And they develop structured adoption plans that help employees integrate new tools into day-to-day operations.
Three foundational steps can help organizations maximize the value of their AI investments:
- Align AI initiatives with business priorities and measurable outcomes.
- Establish strong data governance and data quality practices.
- Create an adoption strategy that helps employees effectively use new tools and processes.
As margin pressures persist and operational complexity continues to increase, organizations that align AI investments with business priorities will be better positioned to improve profitability, enhance resilience and compete in an increasingly challenging market.
For more, check out our on-demand webinar, Making AI work in food and beverage: Plan, prioritize and scale AI.
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