For years, artificial intelligence in the food and beverage industry has carried an almost ornamental aura—full of promise, possibility and, often, more excitement than real-world impact. That is beginning to change. In this exclusive interview with AgroSpectrum, Michael Guantiero, Vice President and Head, Solution Consulting – Asia Pacific, Japan and India, looks at how AI is gradually finding its way from boardroom conversations and pilot projects into the everyday workings of food businesses. The discussion moves beyond the noise around generative AI to ask a more practical question: can technology actually help companies cut waste, improve efficiency and navigate an increasingly unpredictable marketplace? Guantiero’s answer comes with an important caveat—AI is only as useful as the data, systems and people behind it. As the industry looks towards 2030, the real winners may not be those chasing the most sophisticated technology, but those quietly building smarter, more connected and more responsive businesses.
Food and beverage manufacturers have spent the last few years experimenting with AI. Are we now entering the era where AI is becoming a boardroom investment decision rather than an innovation initiative? What signals convince you that the industry has reached this inflection point ?
We are seeing AI move from experimentation to becoming a boardroom priority. Over the past few years, many food and beverage manufacturers explored AI through pilots. Today, the conversation is centred on measurable business outcomes. Rising input costs, supply chain disruption, labour shortages, evolving consumer demand and increasing sustainability expectations have made AI a strategic investment rather than an innovation initiative. The clearest signal is that business leaders are no longer asking, "How do we use AI?" but "Where can AI deliver the greatest operational impact?" Success is increasingly measured through improvements in production efficiency, demand forecasting, yield optimisation, waste reduction and supply chain resilience.
At Infor, we see this reflected in customer outcomes. More than 1,000 food and beverage companies run on Infor, with customers achieving up to 30 per cent faster time to market for new products, €500K in annual savings through improved yield and significantly faster AI-driven product and pricing recommendations. These are tangible operational and commercial gains that demonstrate why AI is now being evaluated on measurable business impact rather than technical potential. For food and beverage manufacturers, the real value comes when AI is embedded into the operational systems that manage production, quality, inventory and distribution. Infor CloudSuite Food & Beverage and Infor Velocity Suite, enables organisations to combine industry-specific AI, automation and process intelligence to improve operational performance while addressing the unique requirements of sectors such as dairy, bakery, meat processing and beverages.
The industry has reached an inflection point because organisations now have the evidence to treat AI as a business investment with measurable financial and operational returns, rather than simply an innovation initiative.
Many manufacturers still operate on decades-old ERP systems that were never designed for AI-driven decision-making. In your experience, what is the biggest misconception companies have about digital transformation, and why do so many modernization programs fail to deliver the expected business value?
The biggest misconception is that digital transformation is simply a technology replacement exercise. Replacing an ERP system alone does not create business value. Successful transformation starts by identifying the business outcomes an organisation wants to achieve and then aligning technology to those priorities. Many programmes fall short because organisations focus on deploying new technology rather than improving business performance. At Infor, we believe the process should begin with understanding what the business needs to do faster or better, defining measurable outcomes, and then building a roadmap around those priorities.
For food and beverage manufacturers, industry context is equally important. A bakery, dairy processor or beverage manufacturer all operate differently, and those processes need to be reflected in the ERP, data model and AI capabilities from the outset. Hence, our industry-specific CloudSuites are designed around the operational realities of each sector, helping organisations modernise faster while delivering measurable business value.
Generative AI has dominated headlines, but factory floors depend equally on predictive analytics, automation, IoT and machine learning. Which of these technologies do you believe will create the greatest competitive advantage for food manufacturers over the next five years, and why?
While Generative AI has captured the spotlight, the greatest competitive advantage will not come from any single technology. Over the next five years, food manufacturers will differentiate themselves by combining predictive analytics, machine learning, IoT, automation and Generative AI within industry-specific operational workflows. Each technology contributes a different layer of intelligence. IoT and Manufacturing Execution Systems (MES) provide real-time visibility into production, equipment and quality, while predictive analytics and machine learning help improve forecast accuracy, optimise production schedules, manage raw material variability, increase yield and reduce waste. Generative AI builds on these capabilities by making insights easier to access and act upon, enabling employees to interact with enterprise systems using natural language, accelerate product innovation and optimise recipes and formulations.
The real opportunity lies in embedding these capabilities into the day-to-day flow of work rather than treating them as standalone technologies. Manufacturers that connect data across the value chain and combine AI with automation and advanced analytics will be better positioned to improve productivity, resilience and innovation. Ultimately, competitive advantage will not come from having the latest AI model; it will come from applying AI with industry context. Manufacturers that can connect data across the value chain and turn real-time insights into operational decisions will be better positioned to innovate faster, reduce costs and build more resilient, profitable businesses.
The food industry operates on extremely thin margins while facing rising input costs, labour shortages and increasing sustainability pressures. Can AI realistically solve these structural challenges, or is there a risk that companies are overestimating what technology alone can achieve?
AI is not a silver bullet for the structural challenges facing the food industry and there is certainly a risk of overestimating what technology alone can achieve. Rising input costs, labour shortages and climate-driven supply chain disruptions are complex issues that require a combination of business strategy, operational excellence and workforce transformation. AI can support these efforts, but it cannot replace them. Where AI delivers the best value is by helping manufacturers make faster, more informed decisions in the face of these challenges. It can improve demand forecasting, optimise production schedules, reduce waste by managing raw material variability, enhance yield, strengthen inventory planning and identify inefficiencies before they impact operations. These capabilities help manufacturers protect margins, improve resilience and make better use of limited resources.
The greatest value comes when AI is applied to real operational challenges using trusted data and industry-specific processes. When manufacturers integrate AI into planning, production and supply chain decision-making, they are better equipped to respond to volatility while maintaining quality, regulatory compliance and sustainability goals.
One of AI's greatest promises is real-time decision intelligence across procurement, production, inventory and distribution. What are the most compelling examples you've seen where AI has translated directly into measurable gains in productivity, waste reduction or profitability?
One of the strongest examples is production and yield optimisation. By combining machine learning with production and quality data, manufacturers can better manage raw material variability, optimise recipes, improve yields and reduce waste all while maintaining food safety and compliance. Another is supplying chain planning, where AI improves demand forecasting and inventory decisions by helping manufacturers respond more quickly to changing demand, optimise stock levels and strengthen end-to-end traceability. This enables better service levels while reducing excess inventory and waste.
We are also seeing measurable gains through process automation and real-time decision intelligence. Infor Velocity Suite, organisations can combine AI agents, process mining and automation to streamline routine processes, accelerate decision-making and improve operational efficiency. The impact is already measurable: for example, Frontier Co-op reduced reporting time by more than 95 per cent, giving leaders faster access to real-time business insights and enabling quicker decisions. Whether it is improving yield, reducing waste, accelerating decision-making or strengthening supply chain performance, the organisations seeing the greatest success are those applying AI to high-value business processes where the return on investment can be clearly measured.
Asia-Pacific is an incredibly diverse manufacturing landscape, ranging from digitally mature multinational enterprises to family-owned processors beginning their transformation journey. How should organisations calibrate their AI strategies to match their digital maturity rather than simply chasing the latest technology trends?
The single biggest strategic mistake I see is a company benchmarking its AI ambitions against a peer at a completely different stage of maturity. A digitally mature multinational and a family-owned processor starting their cloud journey should not be running the same playbook, even if they are competing in the same category. The right starting point is not "what is the latest AI capability" it is an honest assessment of your data and process foundation. If you do not yet have clean, trusted, real-time data flowing from your core operations, deploying an advanced AI agent on top of that foundation just automates bad decisions faster. That is not transformation, that is amplified risk.
For organisations earlier in their journey, the highest-value first move is usually foundational: establishing a trusted cloud-based system of record that connects core ERP and operational data. Once that foundation is in place, organisations are in a much stronger position to scale AI and automation with confidence. For more digitally mature organisations, the conversation shifts to orchestration: how do you combine multiple proven use cases and let them compound, rather than deploying AI point solutions in isolation? The most successful organisations do not chase technology trends; they align AI investments with their digital maturity and business priorities. A well-executed, "boring" data foundation project will outperform an ambitious AI pilot built on shaky ground every time.
As AI becomes increasingly embedded in enterprise operations, concerns around data quality, cybersecurity, governance and workforce readiness are growing just as quickly. Which of these do you see as the biggest barrier to enterprise-scale AI adoption, and how should business leaders address it ?
Data quality and context, without question. It is not just about having clean data; it is about ensuring the data accurately reflects the way a business operates. AI is only as effective as the operational context it is built on. Many organisations invest significant time and resources in building clean, consolidated data platforms, only to discover that the data lacks the industry-specific context needed to generate meaningful insights. The operational challenges of a food and beverage manufacturer, for example, are fundamentally different from those of a hospital or an industrial manufacturer. Clean data without business context still cannot power good AI decisions, and retrofitting that context later is often a costly and time-consuming exercise.
Cybersecurity and governance are equally important, particularly as AI evolves from generating recommendations to taking autonomous actions through agentic AI. As organisations deploy more AI agents, strong governance becomes essential to ensure they operate on trusted data, follow clearly defined policies and remain transparent and accountable. That means establishing governance from the outset through role-based access controls, explainable decision-making, clear data ownership and strong security policies. The organisations that scale AI successfully are the ones that invest in the foundations first. Trusted, contextual and well-governed data should come before ambitious AI initiatives. While AI applications often attract the most attention, it is the strength of the underlying data and governance framework that ultimately determines whether enterprise-scale AI adoption succeeds.
Looking ahead to 2030, what will distinguish the next generation of food manufacturers from those that fail to remain competitive? Will the defining advantage come from better AI models, stronger data ecosystems, more intelligent supply chains, or a fundamentally different way of running the enterprise?
By 2030, I do not believe the defining advantage will come from better AI models alone. It will come from operating a fundamentally different kind of enterprise: one that is connected, intelligent and able to respond continuously to change. AI models will become increasingly accessible. The real differentiator will be how effectively manufacturers connect data, people and processes across the value chain to make faster and better operational decisions. The manufacturers that lead will not simply automate individual tasks. They will build connected operations where procurement, production, inventory, quality and supply chain functions work together using real-time intelligence. That will allow them to anticipate disruption, optimise resources and adapt much faster to changing customer demand, ingredient availability and regulatory requirements.
This is the vision behind the Agentic Enterprise, where AI agents, process intelligence and automation work together to orchestrate business processes rather than simply support them. The goal is not to replace people, but to enable smarter, faster and more consistent decision-making across the organisation. By the end of the decade, the manufacturers that remain competitive will not necessarily be those with the most advanced AI; they will be the ones that have built the most intelligent, connected and resilient way of running their business.
-- Suchetana Choudhury (suchetana.choudhuri@agrospectrumindia.com)
