Processing has always been about control. Control of inputs, control of processes and control of outputs. For decades, that control has been built on experience, discipline and incremental improvements in equipment and operations. That model has worked well, but the environment around it is changing in ways that are becoming harder to manage with traditional approaches alone.

In the U.S. flour milling sector alone, the industry generates more than $21 billion in revenue, operating on relatively tight margins of around 7% to 8%, where small inefficiencies quickly accumulate. In soybean processing, the scale is even larger, with over $54 billion in revenue but also significant exposure to input price volatility and global trade dynamics.

Variability in raw materials is increasing, whether in grains, oilseeds or other inputs. At the same time, energy systems are becoming more volatile, shaped by geopolitical tensions and shifting supply dynamics that continue to influence costs and availability. Customer expectations are also rising, with greater demand for consistency, traceability and sustainability. These pressures are not new individually, but their combination is creating a different level of complexity for processors.

Artificial intelligence is entering this environment with significant attention. There is clear excitement about what it could do but also uncertainty about how it fits into day-to-day operations. That mix of interest and skepticism is not surprising. Across the industry, many are still exploring what these tools actually mean in practice, often starting with relatively simple applications such as summarizing information, analyzing data or supporting routine decisions.

This Is Not About Automation — It Is About Decisions

The real shift, however, is not about automation alone. Processing operations have already invested heavily in automation over the past decades. The more important change is how actions are coordinated across the system. Many operations still function in silos, where raw material procurement, processing adjustments, energy management and quality control operate independently. Each area may perform well on its own, but the system as a whole is not fully aligned.

Artificial intelligence begins to change that by connecting these actions. It allows information from across the operation to influence actions earlier and more consistently. For example, variability in raw materials can be evaluated alongside processing parameters and energy conditions before production decisions are finalized. This does not remove uncertainty, but it allows it to be managed more deliberately.

This is where the conversation moves beyond efficiency. Processing has traditionally focused on improving yields, reducing waste and lowering costs. These remain critical, especially in industries operating under tight margins and competitive pressure. However, AI introduces another dimension: confidence in decisions.

When markets are volatile, processors often take a cautious approach. Commodity price fluctuations, where even small changes in acreage, consumption or weather expectations can move markets and margins quickly, reinforce this behavior, particularly in soybean markets where these dynamics shift constantly. Decisions are delayed, adjusted incrementally or hedged to manage downside risk. While this protects against losses, it can also limit the ability to respond to opportunities.

AI systems allow processors to evaluate outcomes before acting. This can include adjusting processing parameters based on expected variability in incoming raw materials, aligning production schedules with energy costs or anticipating quality deviations before they occur rather than reacting after the fact. By integrating historical performance, real-time data and external signals, these systems provide a clearer view of how different actions are likely to play out. The question shifts from “What happened?” to “What is likely to happen if we act now?” That distinction becomes increasingly important as variability grows and margins remain under pressure.

Where Progress Slows and Advantage Is Lost

Despite this potential, adoption across processing remains uneven. One reason is technical. Many operations rely on legacy systems that were not designed to communicate with each other. Data is often fragmented, making integration difficult. At the same time, the expansion of digital tools introduces new risks. As processing operations become more connected, cybersecurity becomes a critical concern. The increasing use of IoT devices, cloud platforms and data-driven systems creates new vulnerabilities that must be managed carefully.

However, the larger constraint is not technical. It is organizational. Many companies are already experimenting with AI, running pilots in maintenance, quality control or logistics. These initiatives often deliver value, but they remain isolated. Insights improve, but decisions do not always change. This creates a gap between what is known and what is done.

A structured way to move forward is through the DRIVE framework:

  • Data comes first — not as isolated datasets but as a connected system across procurement, processing and output.
  • Purposeful pilots focus efforts on clear, measurable challenges.
  • Internal expertise remains central, as AI enhances rather than replaces operational knowledge.
  • VIPs are not exempt. Leadership determines whether these tools are treated as strategic or experimental.
  • Most importantly, execution must follow because advantage comes from implementation, not analysis.

A useful way to understand this shift is through infrastructure. When high-speed rail systems were introduced, the innovation was not just the train. The entire system had to change, including tracks, signaling and operations. Artificial intelligence presents a similar moment for processing. Introducing advanced tools without redesigning how decisions connect across the system limits their impact. The technology may be capable, but the structure around it determines its value.

This helps explain both the excitement and the hesitation around AI. On one hand, there is clear recognition that these tools can improve how complex operations are managed. On the other, there are valid concerns about cost, complexity and risk. Trust also plays a role. Operators need to understand why a system recommends a specific action, particularly when decisions affect product quality, safety and financial outcomes.

Progress often comes from starting with focused applications. Rather than attempting broad transformation, organizations can begin with specific challenges, such as managing variability in inputs, improving energy efficiency or enhancing scheduling decisions. Over time, these applications build confidence, and that confidence allows adoption to expand.

The Next Phase Will Be Defined by How Systems Work Together

Processing industries are entering a period defined by tighter margins, greater variability, increasing energy complexity and more exposure to global market shifts. Artificial intelligence does not remove these challenges, but it changes how they are addressed. The next phase of performance will not come from more data alone or from isolated improvements within individual processes. It will come from how effectively operations respond to variability across the system.

Because in processing today, the advantage will not come from having more information. It will come from how effectively that information is used.

Aidan Connolly, president of AgriTech Capital, is described by Forbes as “a food/feed/farm futurologist.” He is the author of the book “The Future of Agriculture,” now in four languages, and a recent white paper on AI in agrifood systems.

Camila Ulloa is a market research analyst in agricultural economics. Her work combines industry analysis and applied research on innovation, sustainability and emerging trends across agriculture and food systems.