AI’s real pet food test is closing the manufacturing gap

Artificial intelligence may be one of pet food manufacturing’s most talked-about tools, but the sharper industry message now is that AI alone isn’t the fix. In GlobalPETS, the argument is that manufacturers see the biggest gains when they close the gap between formulation, production, and quality systems, so plant performance feeds back into recipe design instead of remaining trapped in disconnected software and siloed teams. (linkedin.com)

That view also pushes back on a broader concern raised in Pet Age: as brands lean harder on AI, algorithms, and similar market inputs, they risk converging on the same kinds of products and incremental claims rather than building real differentiation. In other words, AI may help companies move faster, but it doesn’t automatically make them smarter or more distinct. In manufacturing, the equivalent risk is optimizing isolated tasks while missing the larger operational system that determines whether a formula can be produced consistently at scale. (linkedin.com)

Recent industry reporting suggests the sector is already moving past the question of whether AI will be used and toward where it creates practical value. A July 22, 2026 report covered by PetfoodIndustry, based on a Datacor and Tech-Clarity study, found that 58% of surveyed food and animal nutrition companies had at least one AI pilot underway, 31% had completed at least one, and 95% of those completing pilots reported value within a year. The most sought-after uses were recipe formulation to specification and lab sample analysis, suggesting companies are starting with technical workflows that already generate structured data. (petfoodindustry.com)

At the same time, conference coverage from AFIA’s January 27, 2026 Pet Food Conference shows how quickly the conversation has broadened. The event highlighted AI applications “across the value chain,” from nutritional optimization to kibble production, alongside ingredient, market, and regulatory pressures facing manufacturers. AFIA’s broader 2024 state-of-industry report also underscored that pet food manufacturers are operating in an environment shaped by supply chain strain, FDA inspections, food safety demands, and ongoing regulatory modernization. (petfoodindustry.com)

Expert commentary from recent trade coverage points in the same direction as the GlobalPETS thesis: the problem isn’t a lack of data, but a lack of connection between the people and systems using it. PetfoodIndustry reported in 2026 that manufacturers are sitting on large amounts of untapped production data, and that quality and efficiency still depend too heavily on individual operators despite advances in automation and formulation software. That’s a useful reality check for veterinary professionals who may hear “AI” framed as a finished solution rather than a tool that depends on sound process control, validated inputs, and human oversight. (petfoodindustry.com)

Why it matters: For veterinarians and nutrition-focused teams, the manufacturing gap is not an abstract operations issue. FDA guidance on preventive controls for animal food specifically notes that nutrient deficiencies or toxicities can result from formulation miscalculations, the wrong mineral mix, or processing effects on nutrients. If formulation targets are not well linked to ingredient variability, line conditions, and quality verification, the result can be a diet that looks right on paper but drifts in practice. More integrated systems could improve batch consistency, support safer reformulation when ingredients change, and make it easier to investigate deviations that affect clinical trust in commercial diets. (hhs.gov)

There’s also a timely regulatory layer. AAFCO’s label modernization work and recent calcium-phosphorus enforcement updates mean many manufacturers are already reviewing formulations, analytical verification, quality systems, and manufacturing processes across product lines. In that environment, better linkage between formulation and plant data is not just an efficiency play; it may become increasingly important for documenting compliance and managing change without compromising nutritional adequacy. (aafco.org)

What to watch: The next phase will likely be less about flashy AI claims and more about whether companies can operationalize connected data systems that hold up under quality, regulatory, and scale pressures. Expect continued investment in integrated platforms, more pilot projects tied to formulation and QA, and growing scrutiny of whether AI-assisted manufacturing actually improves consistency in finished diets, not just speed in development. (petfoodindustry.com)