Moving from Competitive Edge to Operational Necessity

By Binsentry

Artificial intelligence (AI) has gone from being a buzzword, a vision of tomorrow, to a priority topic in many businesses today.

And in agriculture, nowhere is that shift more visible than in animal protein production.

For larger pork producers, integrators, and retailers, AI is no longer a distant concept or experimental tool; it’s rapidly becoming the difference-maker in terms of operational efficiency, supply chain resilience, and long-term competitiveness.

“AI’s coming at us like a freight train and it’s really starting to be a tale of the haves and have-nots,” stated Ben Allen, Chief Executive Officer of BinSentry, a Kitchener, Ontario, Canada, company specializing in AI‑enabled feed supply chain management, providing real‑time visibility and automation across tens of thousands of on‑farm feed bins and thousands of mill and grain‑handling silos.

That divide—between companies that are actively adopting AI-enabled technologies and those that are not—is widening.

And the implications are showing up clearly in performance metrics. Organizations that have leaned into AI are already seeing measurable gains, while others risk falling behind.

AI’S ROLE IN MODERN PROTEIN PRODUCTION

At its core, AI is not about replacing producers or fundamentally changing the nature of agriculture. Instead, as Allen explained, it’s about enhancing decision-making, improving efficiency, and eliminating costly manual processes.

In protein production, one of the most immediate and impactful applications of AI is within the feed supply chain. Unlike some on-farm innovations that are still in early research and development stages, supply chain optimization offers near-term, tangible value.

“The feed supply chain is just a data-rich environment,” Allen related. “Artificial intelligence needs data to work, and supply chains have a lot of data sets and a lot of moving pieces.”

For BinSentry, that broad dataset comes from tens of thousands of on-farm feed bins, plus thousands more silos and ingredient bins at mills and grain handling facilities across North America.

This data density creates the ideal conditions for AI—and BinSentry’s technology—to thrive. From feed inventory management to logistics planning and forecasting, AI can process vast amounts of information far faster—and more accurately—than traditional methods.

The result? For BinSentry’s customers, it has meant faster decisions, fewer disruptions and feed outages, and a significant reduction in manual labor.

MOVING FROM MANUAL PROCESSES TO INTELLIGENT SYSTEMS

Historically, many aspects of the feed and protein supply chain have relied heavily on manual processes—people physically checking bins, updating spreadsheets, and reacting to issues as they arise. While these methods have worked, they are inefficient and prone to error.

Allen is direct about the cost of maintaining the status quo: “If you map out a business process and say, ‘what’s the most expensive way I can do this?’ The answer is almost always humans performing manual tasks.”

He continued, “AI-enabled systems change those cost structures entirely by automating data collection and analysis. This enables companies to shift from reactive to proactive operations. Instead of discovering problems after they occur, they can anticipate and prevent them.

“It’s like turning on the lights in a dark room.”

Allen pointed out that this AI-enabled visibility is especially valuable in feed supply chains, where small inefficiencies can quickly scale into significant costs. With AI, operators can monitor assets continuously, optimize delivery schedules, and reduce waste—all while freeing up human resources for higher-value work.

RAPID ROI AND REAL-WORLD IMPACT

Perhaps one of the most compelling aspects of AI adoption in agriculture is the speed at which it can deliver results. Unlike large-scale infrastructure changes, many AI-enabled solutions can be implemented quickly and begin generating value within weeks.

“People are applying our technology at BinSentry to their supply chain management, and it’s making an impact right away—as in, within weeks—not years,” said Allen.

This rapid return on investment is critical within an industry where the margins are tight and operational efficiency is paramount.

From BinSentry’s perspective, Allen said that the company has seen nearly 100 percent customer retention along with significant expansion within current customers.

“Our average customer grew their spend by 190 percent last year,” he related. “That tells us the technology is delivering real, sustained value and our customers just want more.”

For producers and executives, this reinforces an important takeaway: AI is not just theoretical. It is already driving measurable improvements in performance and profitability.

OVERCOMING THE INTIMIDATION FACTOR

Despite its benefits, Allen acknowledged that AI adoption can feel daunting. A big part of the issue is the broad and often confusing way AI is discussed in the media.

From defense applications to generative tools, the term “AI” can encompass a wide range of technologies, not all of which are relevant to agriculture.

The key, explained Allen, is to cut through the noise and focus on practical outcomes.

“You just have to look at your P&L and say, are there things in my business that I can automate today that are going to have a short payback?” he said. “Having this mindset shifts the conversation from technology to business value. Instead of asking ‘What is AI?’, business leaders can ask, ‘Where can this improve my operation right now?’”

WHAT TO LOOK FOR IN AN AI TECHNOLOGY PARTNER

As AI becomes more integral to operations, choosing the right technology partner is just as important as choosing the technology itself.

In agriculture, most companies do not have dedicated AI development teams. That means success often depends on selecting vendors who can deliver stable, proven solutions.

So, what should agricultural executives look for in a vendor?

The first factor, commented Allen, is stability and staying power. AI implementation shouldn’t be treated as a one-time project—but rather an ongoing partnership. Companies need vendors who will be around for the long term and can support continuous improvement.

“You’ve got to make sure you’re choosing a vendor that has both the strength and the staying power to help you for years to come,” he mentioned.

In a space filled with startups and emerging technologies, financial stability and a proven track record are critical.

The second component is having proven, production-ready technology.

Not all AI solutions are created equal. Some are still in experimental phases, while others are already delivering results at scale.

Allen emphasized the importance of focusing on application rather than experimentation.

“It’s not about R&D work as much as it is application and integration work. You need stable products that can have an impact for you today,” he said.

For most companies, this means having to prioritize solutions that are already being used successfully in real-world environments.

A third major component is data infrastructure and quality. AI is only as effective as the data it uses. Without reliable, high-quality data streams, even the most advanced algorithms can’t deliver meaningful or accurate insights.

“To use AI, you’ve got to be able to point those software tools at large data sets—you need that always-on data capability,” related Allen.

Technologies that automate data collection—such as sensors and monitoring systems—are essential for unlocking AI’s full potential.

The underlying software behind an AI-based technology must also be robust, scalable, and continuously improving. Allen says operators should evaluate not just what a system does today, but how it will evolve for them.

Ultimately, the success of any AI initiative comes down to results. Technology should always make operations faster, more reliable, and more cost-effective.

“Enterprise value is about doing the work faster and more reliably and more accurately than ever before,” said Allen. “But if you can’t see clear, measurable improvements, then that solution might not be the right fit.”

BUILDING MOMENTUM + THE PATH FORWARD

One of the most effective ways to adopt AI is to build momentum with small targeted applications, prove value, and then roll out the solution at scale.

This iterative strategy allows organizations to effectively minimize risk while significantly maximizing impact, often achieving full corporate implementations in less than 12 months from start to finish.

“Early wins create confidence, which, in turn, drives adoption,” Allen commented. “It’s not about trying to make AI successful, it’s about making your business successful.”

The animal protein industry has always been defined by its ability to adapt—whether through genetics, nutrition, or management practices. AI represents the next evolution in that journey.

For operators and executives, the question is no longer whether or not AI will play a role, but how quickly and effectively it can be integrated into the operations.

The companies that move decisively—focusing on practical applications, choosing the right partners, and aligning technology with business goals—are already seeing the benefits.

And as Allen’s observations suggest, the gap between those companies and their competitors is only going to grow.

“In a landscape where efficiency, precision, and scalability are critical, AI is not just an advantage,” he summed up. “It is becoming a necessity.”

Ben Allen
BinSentry

Ben is an expert on how AI, data analytics and agtech are transforming the animal feed supply chain, which moves over US $500B worth of animal feed each year globally.

Ben joined BinSentry as Chief Executive Officer in 2022. Under his leadership, the company is revolutionizing how feed mills and poultry and swine producers run their operations, replacing “guesstimates” and ineffective manual processes with advanced 3D sensors and user-friendly software for precise, real-time inventory data and decision making.