How to win with AI: Lessons from the food & beverage industry

To better understand this issue, it is necessary to identify the ground-level conditions that are putting the food and beverage sector under pressure.  

Cost volatility 

Cost pressures in 2026 are no longer simply a function of market cycles; they are being driven by geopolitical shocks that have direct supply chain consequences. The food and beverage sector operates on incredibly tight margins and is highly exposed to even small changes in global trade, with up to 50% of input costs for some producers directly or indirectly linked to the US dollar, leaving businesses acutely vulnerable to currency swings and commodity price surges. Climate-related events, geopolitical tension, trade and tariff uncertainties, and ongoing labor shortages are colliding in ways that rigid, cost-only operating models simply cannot absorb. This is amplifying an already well-documented consumer sensitivity: as a McKinsey report highlights, 61% of consumers say price matters more today than two years ago, and cost and perception of value are the top two reasons shoppers stop buying a given brand. 

Consumer shifts 

There is also an increased demand for health-conscious, personalized, and sustainable food products among consumers, which is pressuring the industry to change. The same  McKinsey report reveals 57% of consumers rank healthiness among their top three decision factors, “representing the largest increase in importance of any attribute over the past two years” for consumer shifts. 

Competitive pressure 

Another developing trend within the industry is the growth of private labels and niche disruptor brands, catering precisely to the kind of consumer needs mentioned above. The fact that 28% of respondents in a survey conducted by McKinsey said that they “purchase more private-label products today than they did two years ago” reveals this shift within the sector. 

Speed to market 

While consumers look for more affordable and healthier food and beverage options, shorter innovation cycles are becoming critical to meet market demands. Rapidly shifting consumer preferences are forcing brands to reformulate and relaunch products faster than traditional development cycles allow. As consumer choices, eating habits, and meal production patterns diversify, food and beverage companies must accelerate their processes, pull together more resources, and compress their innovation cycles to remain competitive. 

Supply chain resilience 

Underpinning all of the above is a structural fragility in global food and beverage supply chains that the current geopolitical environment has brought sharply into focus. As the global food and beverage market expands, supply chain complexity is intensifying with faster demand shifts, tighter margins, and more frequent disruption across sourcing, production, and distribution, making traditional operating models increasingly inadequate. Fixed planning cycles, concentrated supplier networks, and manual decision-making are increasingly misaligned with the speed and variability of today’s food and beverage markets, making the case for a smarter, more adaptive approach to operations more urgent than ever. 

AI’s potential in the food and beverage industry can be mapped across a few key areas: manufacturing and product innovation, storage and supply chain, and procurement and purchasing from the company’s end, as well as retail and customer experience from the customer’s side. 

Manufacturing and product innovation 

AI in food manufacturing and food tech innovation can design predictive models to determine the taste, texture, and shelf life of food products. AI can support faster product development and reformulation, reduce waste through optimized production, and conduct visual inspection through AI-driven quality control. 

Retail and customer experience 

AI-driven predictive analytics in the food and beverage industry can be used for demand forecasting and inventory planning to better meet customer needs. Companies can generate personalized recommendations and targeted promotions for improved product discoverability through structured data. AI-driven pricing and trade optimization can also present food and beverage products at the most competitive price point for customers while maintaining profitability. 

Storage & supply chain 

Storage and supply chain constraints can be addressed through predictive demand planning and inventory optimization powered by agentic AI. AI can also provide real-time logistics adjustments and route optimization for distribution, while reducing food waste and improving end-to-end supply chain visibility across the full network.

Fortude’s demand planning and inventory levelling agent combines SKU-level forecasting with real-world signals like weather, promotions, and customer sentiments to recommend purchase orders, flag stockout risks, and put the right products in the right places.

Procurement & purchasing 

AI can be applied to procurement optimization, where AI-powered ingredient and input cost forecasts help reduce cost exposure during the production stage. Supplier selection and risk analysis, and smarter sourcing decisions in volatile markets, are other capabilities that AI brings to the procurement function. 

 As the McKinsey & Company report notes, the next wave of AI and tech adoption could shave an extra 2% to 3% off total costs. This frees up the exact funding needed to reshape product portfolios, refresh brands, and scale up customer reach. It is therefore useful to examine how a few businesses have already put this into practice.

• Mondelēz International  

Chicago-based Mondelēz International, Inc., one of the world’s largest snack companies, which sells its products in over 150 countries, developed a machine learning recipe engine that digitally iterates on attributes like aroma, flavor, and ingredient costs. This allowed food scientists to validate prototype recipes before physical testing, supporting over 70 product launches and contributing to a 5.4% sales lift.  

• Ferrara Candy Company / Nerds  

The Chicago-based Ferrara Candy Company acquired Nestlé’s US candy portfolio in 2018 and mobilized its Research and Development team to reinvigorate the Nerds brand. During this process, Ferrara used consumer data to identify demand for “multi-textured” candy. This led to the creation of Nerds Gummy Clusters, combining a crunchy shell with a soft center, generating $500 million in sales in 2024 and significantly accelerating brand growth. 

• Danone  

According to the McKinsey report, the Paris-based Danone is piloting “various AI and machine learning use cases to better forecast costs and develop should-cost models for each product ingredient,” as part of its ambitions to digitalize its operations. This has enabled more responsive procurement decisions and improved margin management, in addition to incremental savings. 

Success in today’s food and beverage landscape depends on more than deploying the right technology. Data readiness, process redesign, and organizational alignment are the foundations that determine whether AI delivers on its promise, and businesses that treat these as prerequisites, rather than afterthoughts, are the ones translating AI investment into measurable growth. 

The opportunity is clear: AI-led productivity gains, when reinvested rather than simply absorbed into the bottom line, become a growth driver. Reinvestment restores volume. Restored volume compounds performance. In a market defined by volatility and tightening margins, that flywheel is not a long-term aspiration; it is an immediate competitive advantage. 

Fortude supports AI adoption in food and beverage businesses by establishing robust data platforms that unify data across ERP, supply chain, and customer systems, ensuring it is clean, structured, and ready for AI-driven insights. 

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