Since OpenAI released ChatGPT in November 2022, use of artificial intelligence (AI) has seemingly taken off like wildfire. Of course, AI was already being employed by some people and organizations, but this marked the first release of an AI tool based on large language models (LLMs) that was available to everyone.
However, within industries like pet food and individual companies, the rate of adoption has been mixed. In an August 2025 poll of users of this site, PetfoodIndustry.com, 36% of the 139 pet food professionals responding said their companies were actively using AI, while 26% of their companies were not currently using any AI tools, and another 26% were unsure about their AI status or had no opinion on the technology.
How much, if at all, has adoption increased since then? In a recent survey on AI adoption within 113 food and animal nutrition companies — 29% are pet food companies — 58% of respondents reported having at least one AI pilot or proof of concept underway, and 31% have completed at least one. Only 3% of the companies reported having no plans to test AI. (Datacor, a firm offering tailored software to food and animal nutrition manufacturers, including in pet food, commissioned the survey, which was conducted by Tech-Clarity.)
Granted, these results are from two separate surveys with different methodologies and pools of respondents, so it’s not an apples-to-apples comparison or linear progression. Yet I can’t help wonder if the higher percentage of adoption within the companies surveyed by Datacor indicates that AI is moving closer to becoming a standard tool within pet food and animal feed manufacturing. And whether any rate of adoption can — or should try to — keep up as the technology continues to develop at warp speed.
How are pet food and animal nutrition companies using AI?
A question in the Datacor survey asking about primary R&D improvement targets for respondents’ plants yielded a tie for the top three: formulating recipes to specifications and orders, finding and summarizing external technical data and augmenting technical data, all at 60%. In the lab, 47% said they want AI to help analyze samples. (In this survey, respondents could choose more than one answer option to most of the questions.)
Specific to pet food companies and formulation, respondents said they want AI to identify recipes for reuse and optimize around variable ingredients, according to a Datacor representative, who said the report called out pet food’s nutritional-compliance constraints. These respondents also want faster validation of ingredient substitutions without affecting taste or texture.
In the August 2025 PetfoodIndustry.com poll, respondents identified marketing and sales as the area that would benefit most from AI implementation, chosen by 35%, followed by market insights at 21%. Interestingly, areas like production efficiency (18.5%), regulatory compliance and product formulation ranked much lower, with the latter two both at 12.6%. (For this poll question, respondents could choose only one answer.)
Perhaps this reflects a difference in functional areas and responsibilities of the respondents; our poll didn’t collect that data. Datacor’s survey did, reflecting a wide range of functions: processing and production (34%), procurement (8%), supply chain/logistics (7%), general management (6%), quality (6%), accounting and finance (6%), R&D and product development (5%), IT (5%), plant and facilities engineering (5%). Other areas represented included service and support, sales, environmental health and safety, marketing, industrial/manufacturing engineering, lab and project/program management.
Barriers to AI adoption don’t follow common narrative
Despite a common narrative that many people mistrust AI or fear it will replace jobs, those supposed barriers to adoption rank low for pet food and animal feed manufacturing facilities. In the Datacor survey, the same percentage of respondents, 33%, chose distrust or fear of losing jobs as a challenge for AI adoption. The most common barriers, on the other hand, were lack of data science skills at 55%, lack of knowledge at 49% and lack of time to focus on AI, also at 49%. (Again, respondents could choose more than one option.)
Similarly, in the PetfoodIndustry.com poll, lack of expertise showed up as the primary barrier, with nearly 25% citing insufficient technical knowledge or staff, significantly outpacing other challenges named, such as outdated systems and legacy workflows (15%) and data quality issues (10%). The other large category of barriers fell under “other” responses (27%). Interestingly, 17% of these respondents said they see no perceived need for AI, believing their current processes work adequately without technological enhancement. (Denial as a barrier to adoption?)
Data quality also showed up in the Datacor survey, cited by 50% of respondents in a question specifically about data-related challenges to AI adoption. Other issues they named included unstructured data files (45%), disconnected databases and not enough data (38% each) and proprietary data formats (37%).
“While almost two-thirds of food and animal nutrition companies say they are at least somewhat prepared to meet their AI objectives from a data perspective, only 22% say they are significantly prepared,” said the Datacor survey report, adding that while data readiness for AI is somewhat higher
in food and animal nutrition than in other process industries researched, “Confidence in data readiness is wide but shallow.”
So, it seems the pet food and animal nutrition industries have a way to go to reach wider adoption of AI, though at this point, no one knows the ceiling. As AI platforms continue to advance, that ceiling may be a moving target anyway.
