by Aidan Connolly, President, AgriTech Capital, USA & Camila Ulloa, Market Research Analyst, Purdue University, USA
Petfood manufacturing has always been a business of precision. Every batch depends on balancing ingredient variability, process conditions and nutritional targets, while maintaining consistent output at scale. What is changing today is not the complexity of the system, but the speed at which decisions must be made within it. In a US market worth approximately US$29 billion, operating with margins around nine percent, even small inefficiencies or variability can have a significant financial impact, particularly as input costs continue to fluctuate sharply. This pressure is not limited to the United States. The global petfood market is projected to grow significantly over the next decade, driven by continued premiumisation and increasing demand for more specialised, high-quality nutrition across international markets.
Artificial intelligence is starting to influence those decisions, often in ways that are practical rather than transformational, at least at first.
Across many facilities, AI is already embedded in specific parts of the operation. Production teams use it to monitor process stability, predict equipment issues and reduce variability in output. Quality systems are becoming more proactive, identifying potential deviations before they occur rather than reacting after the fact. In formulation, AI is helping to optimise ingredient use across multiple products simultaneously, improving both cost efficiency and consistency.
These applications matter. They improve yield, reduce waste and support more stable production. But they also reveal something important: most of the value today is still captured within individual steps of the process.
That creates a natural limit.
Where gains begin to plateau
As more companies adopt these tools, a pattern is emerging. Early improvements are relatively easy to achieve. Variability decreases, downtime is reduced, and operators gain better visibility into what is happening on the line.
Over time, however, those gains begin to slow.
This is not because the technology has reached its limit, but because the structure around it has not changed. In practice, this disconnect appears in several ways:
- Data generated in one part of the process often remains disconnected from decisions made elsewhere.
- Ingredient variability may be tracked in detail, but not fully linked to extrusion performance.
- Production outcomes may be measured precisely, but not consistently fed back into formulation or procurement strategies.
The result is familiar: better insights, but incomplete impact.
Examples from across the industry highlight both the opportunity and the constraint. AI-enabled sorting systems are improving defect detection and yield, allowing more product to reach market from the same raw material base. At the same time, AI-driven R&D platforms are accelerating product development cycles, enabling faster innovation in formulation.
Individually, these advances are significant. Collectively, their full value depends on how well they are connected.
A shift in how decisions are made
The real change taking place in petfood processing is not about automation, but about coordination.
Artificial intelligence allows decisions that were once made sequentially to be informed simultaneously. Instead of formulation being finalised before production begins, processing data can influence formulation choices in real time. Instead of quality being assessed after production, it can be predicted and adjusted during the process.
This creates a different way of operating.
Facilities begin to move from managing steps to managing systems. Information flows across functions, and decisions start to reflect conditions beyond their immediate context. Processing parameters are no longer set only by operator experience, but by a combination of historical data, real-time inputs and predictive models.
Some newer facilities are already being designed with this level of integration in mind, combining automation, IoT and AI to enable continuous monitoring and traceability across the entire operation. Equipment providers are also evolving, offering not just machinery, but integrated systems that connect production, quality and data into a single framework.
These developments point toward a broader shift, often compared to earlier transformations such as electrification, where the real value came not from the technology itself, but from how it reshaped entire operating systems, a perspective discussed in prior industry conversations.
Structuring the transition with DRIVE
Moving from isolated improvements to system-level performance requires more than technology. It requires structure.
The DRIVE framework offers a practical way to approach this transition. It starts with Data, ensuring that information flows across systems rather than remaining fragmented. It then emphasises the need to Run pilots, but with a clear focus on operational problems that matter economically. From there, Internal expertise becomes central, as teams shift from monitoring processes to interpreting outputs and guiding decisions. VIPs not excepted highlights the role of leadership in embedding AI into strategy rather than leaving it at the technical level. Finally, Execute now reflects the importance of implementation and iteration over prolonged experimentation.
What distinguishes leading organisations is not that they are more active in one of these areas, but that they are progressing across all of them simultaneously.
From efficiency to advantage
The implications for the petfood industry extend beyond operational efficiency.
As the market grows and becomes more competitive, differentiation based purely on formulation or branding becomes more difficult. In this context, the ability to operate consistently under variable conditions becomes a defining advantage.
Artificial intelligence supports this by improving how complexity is managed. It allows variability to be understood and adjusted in real time, rather than controlled only through standardisation.
But the outcome depends on how it is applied.
Companies that continue to implement AI within individual functions will see incremental gains. Those that use it to connect decisions across the system will begin to see cumulative effects, where small improvements reinforce each other over time.
Petfood manufacturing has always required balancing precision and variability. Artificial intelligence does not remove that challenge. It changes how effectively it can be addressed.
The question is no longer whether AI can improve processing. It is whether organisations are prepared to use it to rethink how processing decisions are made.
