Conventional food research and development (R&D) is bedeviled with critical inefficiency in long-term empirical method, resource-scaled prototyping, and disintegrated consumer data, in turn preventing the provision of a novel, sustainable, and differentiated item. Current review summarizes evidence that Artificial Intelligence (AI), through machine learning, generative modeling, digital twins, and natural language processing, is changing food R&D into a data-driven paradigm. Significant breakthroughs indicate the presence of tremendous efficiencies: AI-informed formulation can cut physical prototypes to 70190 % and shorten complex reformulations to weeks, predictive analytic can predict sensory profiles with 8592 % accuracy, shaving consumer testing by 60 %, physics-informed digital twins can optimize for scalability in manufacturing, reducing scale-up runs to 4070 %, and energy usage by 1525 %, and NLP-powered trend analysis can pinpoint new opportunities 612 months before sales All together, AI reduces the development cycle by 50-60 percent and reduces R&D costs by 30-60 percent and permits sustainable innovations and hyper-personalized products. The most pervasive issues remain: the lack of data regarding new ingredients, lack of information on most algorithms, a lack of an infrastructural platform to support a small and medium-sized enterprises, and an absence of regulation regarding AI-created foods. However, even amid these thoughs, AI becomes a strategic necessity, and bringing the R&D beyond the reactive type of innovation into an anticipatory one and establishing it as one of the key enablers of competitive resilience becomes its goal. The upcoming steps will rely on cross-functional data standards, ethical data control, and democratization of access to AI in order to utilize the potential offered by the technology to its fullest extent to address the global needs in terms of healthier, sustainable, and flexible food systems.
Azeem et al. (Thu,) studied this question.