Artificial intelligence is rapidly reshaping global agri-food systems, moving from pilot projects to strategic infrastructure. With 70 per cent of businesses already adopting AI, the challenge now lies in building the digital foundations, skills and governance needed to unlock its full potential.
As global agri-food systems brace for sweeping disruption, artificial intelligence has quietly transitioned from hype to hardware. No longer just a buzzword, AI is being embedded across production lines, distribution hubs and digital menus with an intent that is both experimental and strategic. From predictive scheduling to image-based quality control, it is becoming the cognitive scaffolding behind tomorrow’s food supply. The path ahead, however, is not frictionless. Data readiness, regulatory clarity and workforce capability are now the soil conditions on which AI’s promise will grow or wither.
Optimisation begins at the edge of adoption
The belief that AI will be an industry game changer has not entirely faded. It has evolved. What began as cautious experimentation has matured into budgeted transformation. According to the AI & Agri-Food: Attitudes, Adoption and Ambition white paper from Foods Connected, a supply chain software provider specialising in the food industry, 70 per cent of agri-food businesses have either adopted or are currently adopting AI, while nearly nine in ten now allocate specific funds for its implementation. However, the real story is not in the volume of investment but in the distribution of expectations.
Food safety and quality remain the strongest footholds for AI technologies, followed by demand forecasting and supply chain personalisation. These are areas where machine learning already complements legacy control systems and where digital twins, image recognition, and process automation offer direct benefits. Yet the spotlight on these functions can cast shadows over other opportunities. Sustainability, for instance, was identified by only one in five businesses as a strategic AI priority despite the technology’s ability to deliver granular insights across environmental, social, and governance metrics.
That misalignment may be explained by the fact that many businesses still stand in their digital journey. Although over 70 per cent of respondents claim to be mostly or highly digitised, the remaining 28 per cent remain tethered to analogue or semi-automated processes. According to Stephen McCabe, Interim Executive Director at Momentum One Zero, even among more progressive firms, “understanding what data you have is the first problem and then working out if it is in a form that can be used to train AI models”. He is blunt about the stakes: “What decisions could that inform? If you do not know, you are not ready.”
The stakes extend beyond internal efficiencies. As pressure builds around traceability and provenance, AI is increasingly viewed as a mechanism to ensure supply chain resilience and respond to intensifying consumer scrutiny. The food industry’s exposure to climate events, international logistics, and geopolitical risk makes agility a competitive advantage. In this context, AI becomes more than a tool – it is a prerequisite for future proofing.
Return on intelligence requires more than data
Agri-food businesses are not looking at AI through rose-tinted glasses. They are scrutinising it through spreadsheets. In a sector squeezed by inflation, energy costs and retail price resistance, every digital investment must now pass an ROI stress test. At Finnebrogue, where AI is being explored for demand planning and factory scheduling, technology is assessed on multiple fronts, including cost savings, waste reduction, workforce efficiency, and seasonal agility. “All of these factors are considered carefully,” Declan Ferguson, Technical Director at the company, says, “when investing and signing up to a platform that requires long-term commitment.”
That sense of commitment is not universal. Roughly a quarter of the surveyed businesses remain unconvinced that AI will have a positive impact on the agri-food sector. This scepticism is most acute in the United States, where concerns over job displacement and data security are layered onto the already dense regulatory and cultural complexity of food production. In contrast, UK firms appear more likely to see AI as a compensatory tool for workforce shortages and a mechanism to enhance resilience.
Part of the divide may be cultural, but it is also infrastructural in nature. According to McCabe, smaller players are particularly constrained by short-term operational targets and a lack of internal resources. “Carving out time and expertise to transform messy data into a machine-readable state is not just a technical task,” he argues, “it is a strategic one. If leadership do not prioritise it, AI is never going to scale.”
Some of that hesitancy stems from a lack of clarity about what AI is really for. Businesses may understand automation, but intelligence, particularly in the form of generative and predictive systems, still feels abstract. That is not just a perception issue. It highlights the need for AI use cases that directly link to strategic outcomes, such as reducing spoilage in transit, predicting shifts in protein consumption, and automating compliance with ever-changing safety standards. When AI is contextualised within operational realities, the path to ROI becomes clearer.
Building capability must come before buying solutions
Even among larger organisations, the skills to implement and govern AI are in short supply. One in five companies surveyed admitted to lacking a dedicated team for digital transformation. The figure rises in the US, where digital is still often viewed as an IT function rather than a strategic pillar. This mismatch between ambition and capability risks leaving businesses dependent on generic, off-the-shelf AI solutions that deliver neither competitive advantage nor measurable value.
Closing the skills gap will require more than external training courses. It means developing AI literacy within commercial, operational and compliance teams so that data becomes a shared asset rather than a specialised function. Stephanie Brooks, Head of Research and Innovation at Foods Connected, makes the case for embedding data understanding across leadership: “ROI is not always immediately obvious when it comes to digital technology,” she says. “Providers and data experts need to help organisations see how AI can be used as a strategic asset, not just a technical one.”
Momentum One Zero’s approach is more structural. It leverages innovation funding to produce graduates with hybrid capabilities: individuals who understand both the complexity of agri-food systems and the nuances of model training and data governance. These people, McCabe argues, are the ones “companies can build teams around.” It is a reminder that AI success is often less about the algorithm and more about the architect.
Without this internal expertise, many firms risk defaulting to outsourcing their digital transformation entirely. While partnerships can offer speed, they rarely provide the kind of embedded intelligence that long-term competitive advantage requires. The critical shift is from AI as a service to AI as a competency.
Short-term budgets need long-term thinking
Confidence in AI is high across the sector. An overwhelming 96 per cent of agri-food businesses plan to make further investments within five years, with the majority acting within the next 24 months. The rapid infusion of funding creates both opportunities and risks. On the one hand, late adopters may fall further behind as competitors automate, personalise and optimise. On the other hand, rushing to deploy AI without clear objectives could fragment capabilities and erode trust.
Too many businesses warns Sarah Duchazeaubeneix of NIQ, are caught between extremes. Some resist AI entirely. Others pursue it across multiple use cases without a coherent strategic goal. “The adaptability of leaders to envision the potential is key,” she says, “but we also observe the reverse: large investments without precise planning.”
The future shape of AI in food will depend on more than investment cycles. It will rest on regulatory alignment, workforce evolution and technical maturity. In Europe, the AI Act is already providing a harmonised framework for risk-based deployment. Its impact may be global. 47 per cent of surveyed firms want more guidance on data privacy and security, and over a third want ethical governance baked into the adoption process. If regulators can provide clear guardrails, they may reduce both enterprise hesitancy and consumer mistrust.
Meanwhile, the most advanced firms are thinking beyond operational efficiency. McCabe anticipates a surge in predictive analytics, digital twins and scenario-based decision tools. “AI will make supply chains more resilient and traceable,” he says. For Duchazeaubeneix, the horizon is equally transformative. “Advanced personalisation will go far beyond predicting churn or preference. The convergence of different data sets will increase relevance for each individual.”
Harvesting the future requires intelligent cultivation
AI is not a panacea for the operational or environmental fragilities of food. But it is becoming an indispensable part of the solution architecture. As agribusinesses navigate inflation, climate pressure, labour shortages and geopolitical volatility, they will increasingly need decision support that is faster, smarter and adaptive to change.
That means asking more challenging questions now. What does success look like beyond efficiency? Which data should be elevated from insight to infrastructure? Who is accountable when algorithms shape supply or pricing decisions? And how can regulation keep pace without stifling innovation?
For agri-food leaders, the goal is not simply to adopt AI but to operationalise intelligence. This will not be achieved solely through procurement. It will require strategic patience, cross-functional collaboration and a deeper understanding of the data that sits behind every crop, crate and customer. Ultimately, the true promise of AI in food is not automation. It is an augmentation of human decision-making in a system too complex for instinct alone.
