Enhancing our passion for food and agriculture with AI

Artificial Intelligence can bring many benefits to the food, beverage, and agriculture sector, but it also comes with some challenges and risks. Therefore, business owners who want to adopt AI in this domain should consider the following points:

• AI is not a magic bullet. AI can augment human capabilities, but it cannot replace them. AI systems need human input, guidance, and oversight to ensure their reliability, accuracy, and ethical application. Business owners should not rely solely on AI, but rather use it as a tool to complement and enhance human knowledge, expertise, and judgment.
• AI is not a one-size-fits-all solution. AI systems need to be tailored to the specific context, goals, and constraints of each use case. Business owners should not adopt AI blindly, but rather evaluate its feasibility, suitability, and value for their particular use case. They should also consider the data availability, quality, and security, as well as the technical, legal, and social implications of using AI in their domain.
• AI is not a static technology. AI systems are constantly evolving, learning, and improving, based on new data, feedback, and innovations. Business owners should not adopt AI once and for all, but rather update and maintain it regularly, to ensure its performance, relevance, and compatibility with changing conditions and requirements. They should also monitor and measure the impact and outcomes of AI and adjust their strategies and practices accordingly.

Generative AI requires two key components of trust for adoption: trust in data sources and trust in outcomes. Generative AI engines are trained using a vast expanse of data that is largely available to the public. However, not all publicly accessible data sources are reliable nor, in most cases, do they include all the data individual organisations will need to leverage the technology.

These shortcomings illustrate the importance of building a connected data infrastructure that will allow AI and GenAI to thrive and deliver focused insights, drive better decision-making, spot risks, and capitalise on opportunities that might otherwise go overlooked. Importantly, a connected data infrastructure that supports cross-functional data can help organisations learn more about customers and their end-to-end journey. Food and Beverage companies can benefit from more connected data infrastructures to tackle many of the risks and new reporting requirements around global supply chains. Furthermore, better data infrastructure can be used to communicate information about the nutritional and sustainability status of food and beverage products to the consumer. 

Many organisations are already using AI in some form and it is important to create and maintain a framework for the responsible use of AI which should include governance and ethical intentions, taking into consideration regulatory compliance, the impact on customers, employees, and other stakeholders. As Food and Beverage companies begin to use and exploit GenAI the development of a responsible AI framework will be critical for maintaining an authentic relationship with customers, gaining their trust, and, in the process, protecting their reputations for responsible use of their personal data.

There are risks around the use of GenAI relating to inaccurate results, fraud and potential infringement of intellectual property and as such Food and Beverage companies will need to ensure that the technology is being used not only ethically, but also legally. In addition to mitigating risk, building a responsible AI framework will allow food and beverage companies to develop and use AI in a more optimised manner, allowing for faster speed to value for the business.

As part of a broader data and AI strategy, companies should clearly state that GenAI is worth exploration and investment. Companies should develop a clear strategy that identifies roles and responsibilities, including the people and teams that will be responsible for decision-making around the GenAI agenda and for the technology’s adoption.

KPMG advises Food and Beverage companies to establishing a roadmap and conduct proof of concepts or pilots using GenAI to prepare for future adoption. As this technology’s capabilities are expected to transform rapidly over the coming years, companies are advised to have a technology strategy and to perform a continual scan of market capabilities against the requirements of their use cases.

GenAI holds significant potential to improve business processes and create a more engaging experience for customers. However, underpinning its success is the design and management of robust data principles and trusted data sources.

Many Food and Beverage companies already have strategies and systems to gather, store and track data on their business processes, to take full advantage of that data, companies can now deploy an enterprise-class GenAI engine, one that can be trained in a secure environment using proprietary data that is uniquely applicable to an individual business. In addition to safeguarding data while sharpening the value of the GenAI output, GenAI will also facilitate the safeguarding of the questions or “prompts” given to the engine to trigger new outputs. Those prompts or questions have inherent value to companies and are best kept away from public platforms that can be accessed by competitors.  

For organisations to properly execute on their AI strategy and implement use cases they will require the right people and talent. These positions will include experienced data scientists, software engineers, data engineers, and others with industry and domain expertise, all of whom have been important to leveraging AI in the past. But need new specialists, e.g., prompt engineers, will also be needed to test and validate GenAI models and outputs. Food and Beverage companies should empower and support these teams to explore this game changing technology to the benefit of their businesses.