Artificial intelligence has fueled FOMO, perhaps more than it has boosted productivity. Much of the buzz around AI has been about not missing the boat. However, AI for AI’s sake is not only unproductive, but it may degrade a brand’s sustainability credibility. Pet food professionals have begun using AI tools to optimize formulations, navigate AAFCO regulations and automate marketing, among other tasks. At the same time, pet food companies have made greater commitments to ecological and social responsibility. Artificial intelligence data centers require lots of room for computers and lots of electricity to run them, both of which have environmental costs and affect neighboring communities. While artificial intelligence can deliver benefits, to maintain their sustainability stance, pet food companies should deploy AI selectively based on definable goals rather than trend chasing.
The ecological toll of AI infrastructure
The environmental message around AI is generally bad. Training and maintaining large-scale AI models can consume substantial electricity, often sourced from fossil fuels, depending on regional energy grids. Data centers, the backbone of AI deployment, also require extensive water use for cooling systems and land for construction. These facilities can strain local water supplies and contribute to heat emissions that affect surrounding communities. Air quality concerns may arise when energy sources rely on fossil fuels, while construction and operation can alter land use patterns.
Social implications are also part of the equation. Labor conditions in data center construction and maintenance, as well as in upstream mineral extraction for hardware components, have drawn scrutiny. Communities near large facilities may experience resource competition, particularly for water.
Nevertheless, in a literature review of 155 studies, researchers documented how artificial intelligence can significantly enhance the sustainability of regional ecosystems in Latin America, Africa, Asia and elsewhere, through applications such as environmental monitoring, agriculture optimization and resource management, while improving efficiency and adaptability. However, the study, published in Sustainability, also highlighted risks including socio-economic inequality, technological barriers and uneven regional impacts.
Similarly, in the book “Human Values, Ethics, and Dignity in the Age of Artificial Intelligence,” the authors note that AI offers potential to improve environmental management through enhanced data analysis, predictive modeling and resource optimization, while supporting climate forecasting, precision farming, pollution monitoring and other systems. To this silver lining they also added the cloud that AI’s benefits may not be distributed equally across regions and communities.
While AI can support sustainability goals, its environmental and social trade-offs require careful evaluation to ensure its benefits are not outweighed by its broader effects.
Rise of the machine-learning tools
Despite these concerns, adoption of AI across industries, including the pet food supply chain, has accelerated quickly. Ingredient suppliers are using predictive analytics to forecast crop yields and optimize sourcing. Equipment manufacturers are embedding AI into processing systems for real-time monitoring and maintenance prediction. Packaging companies are exploring AI-driven design and material optimization. Pet food brands themselves are leveraging AI for demand forecasting, personalized nutrition concepts and consumer engagement. The competitive pressure to adopt these tools is intensifying, particularly as early adopters report efficiency gains and cost savings.
This trend aligns with broader patterns observed across industries. Generative AI technologies have experienced one of the fastest adoption rates of any recent digital innovation, with organizations integrating these tools into workflows at a rapid pace. However, the results may be mixed depending on how AI is used.
According to research results prepublished online in the journal Organizational Science, A study of 758 knowledge workers found that artificial intelligence improved performance on tasks within its capability range, boosting speed, output and quality.
However, AI usage reduced accuracy on more complex tasks outside that range. The study highlighted how perceived competitive advantage and ease of deployment have driven rapid uptake, even in cases where long-term implications remain uncertain.
In the pet food sector too, this dynamic can create a “race to adopt” environment. Companies may feel compelled to implement AI not only for operational improvements but also to signal innovation to retailers, investors and consumers.
Adopting AI for a purpose versus peer pressure
The potential benefits of AI in pet food production could help the environment though. Improved formulation models can reduce ingredient waste. Predictive maintenance can minimize equipment downtime and improve energy efficiency. Supply chain optimization can lower transportation emissions by improving logistics efficiency. In theory, these gains could offset some of the environmental costs associated with AI itself. However, the environmental cost of AI is often external to the company using it, embedded instead in cloud providers, hardware manufacturing and energy systems. This can make the impact less visible in corporate sustainability accounting.
Moreover, not all AI usage delivers meaningful efficiency gains. In some cases, adoption is driven more by fear of missing out than by clearly defined operational needs. When AI is implemented primarily for marketing differentiation or trend alignment, the environmental trade-off becomes harder to justify.
The economic concept of externalities is central to this discussion. Environmental and social costs associated with AI, including carbon emissions, water use and community impacts, are often not directly borne by the companies deploying the technology. Instead, these costs are distributed across society. When the private benefits of AI, such as cost savings and productivity gains, outweigh the direct costs to the company, adoption appears rational. However, when broader environmental impacts are considered, the overall cost-benefit equation may shift.
For the pet food industry, which increasingly emphasizes sustainability in brand positioning, ignoring these externalities may pose reputational risks. Pet owners increasingly pay attention to full lifecycle impacts of the products they buy. Flipping the script, promoting a company’s awareness of tech-related environmental issues could boost their standing with pet owners.
Rather than rejecting AI outright, companies may benefit from a more selective approach. Prioritizing applications that deliver measurable environmental or operational improvements, while avoiding adoption driven primarily by competitive pressure or perception, can help align AI use with broader sustainability goals. Recognizing and accounting for the externalities associated with AI will be crucial.
Tim Wall | DALL-E
