Data and AI have become central to agriculture productivity, but what does it mean for processors?

These days, data and artificial intelligence are practically ubiquitous concepts. Across job roles, industries, and regions, data and AI are playing increasingly important roles in how the modern-day business world operates. 

But how these tools are evolving — and how organizations can get the most out of them — changes fast. Trying to process the firehose of information about data and AI can be overwhelming. Not to mention, internal organization and storage of data can be messy, making it more difficult to figure out what to actually do with all that info. 

But “data” shouldn’t just sit around unused. Similarly, AI isn’t just a robot that can write a resume or play music in your home when you ask. Instead, they can work together to make a business’s operations easier — including in the agriculture processing value chain. 

Processors can leverage these modern tools to make better business decisions, provide more value to their customers, optimize logistics, and better plan for the future. 

But first, they have to know where to start — and where the opportunities lie. 

Data and AI optimization in other industries 

By now, many other sectors have harnessed the power of data and AI. Those uses can’t be 100% duplicated in the agriculture processing industry. But processors can still learn from already-established use cases. 

Take these examples: 

• Oil and gas: According to Ernst & Young, 92 percent of oil and gas companies have either already invested in AI or are planning to in the next five years. The industry is already making good use of those investments. Some companies use data and AI for “predictive maintenance” — an analysis of equipment to forecast when it will need maintenance. Ernst & Young predicts this could save organizations upwards of $600 billion by 2025. AI can also be a huge help in reducing downtime at refineries by using data to predict shutdowns or even pipeline blockages.
  
  
• Energy and utility companies: When it comes to keeping the lights on, utility companies have to manage and monitor a huge swath of equipment, such as transformers, over broad territories. Some organizations are looking into using drones to collect images, then bringing in AI to evaluate all that data. This strategy could predict equipment failure quicker than human-led inspections could. AI could also be a tool to analyze and predict energy usage patterns to optimize energy distribution and shore up grid resilience.
  
  
• Food and beverage: Customer engagement is another increasingly common use case, including in food service. Restaurants can use data to analyze customers’ previous purchase history, then make targeted, personalized recommendations. They can also send specific deals to customers if the data shows they’re more likely to purchase with an X% discount or a promise of bonus points. Some fast food restaurants, including Wendy’s, have even started directly engaging customers with AI — through AI-powered drive-through experiences.

Not every data or AI use case from other industries will relate to processors. It’s not like they have a power grid or pipelines to monitor, for example. But they can still find related uses for data-powered tools within their own sector. Processors are an essential part of maintaining the quality of life for humans on Earth. The food, fuel, fiber, and feed they process keep grocery stores stocked, clothes on our backs, keep livestock fed, and help reduce fossil fuel usage via ethanol production. Making processors more efficient through data and AI has positive knock-on effects for the entire economy. 

How can processors maximize data and AI at their organizations?

How to leverage the power of data and AI will vary by company. Every business will have different needs, strengths, and requests from clients. 

But here are a few ideas:

1- Monetize data up and down the value chain

For every organization in a position to collect data… there’s likely another organization nearby in the value chain that would pay for that data. This can be true for processors.

For example, there’s an increasing appetite for “low-carbon” grain. With that in mind, CPGs could be willing to pay a premium if growers provide data on their sustainability efforts or carbon usage at their operations. Processors could be the ones to facilitate the movement of that data: collect the info straight from growers, input it into a data marketplace, and then move it upward to CPGs. 

There might even be an opportunity to resell that data — provided there are privacy controls in place for growers (or a monetary incentive to encourage them to share their data). 

2- Use accurate data to facilitate better decision-making

No business leader wants to feel like they’re making long-term decisions based on guesswork. Data can play a massive role in reducing that guesswork and keeping leaders from flying blind. 

Processors could be on the front end of helping their customers make those better, data-backed decisions. This could even transform into higher customer loyalty. 

Take wait times at grain elevators and facilities: similarly to the refinery example above, where AI could predict potential downtime, processors could use AI to forecast wait times for growers to unload their grain at each grain elevator. That info helps growers better determine if it’s worth it to drive further to one of those locations and wait less time — vs. driving a shorter distance to a competitor who might have longer wait times. 

3- Forecast future trends 

Processors are already good at planning logistics. They have to be — it’s what their business depends on. 

But what about further into the future, past the day-to-day logistics? Processors could use data and AI to more effectively forecast demand. The more that they can predict future trends, the more they can optimize what they’re doing right now. 

Leveraging AI to analyze data and boost forecasting capabilities could help processors answer questions like: 

• Globally, where is the demand for protein increasing? How does that impact prices?
  
  
• If new tariffs are announced to/from Country X, how would that affect demand?
  
  
• When climate impacts certain geographies, how does that change supply? Who can fill those gaps in the supply chain?

These are all questions that advanced AI models could help estimate and predict. And those data-backed forecasts can facilitate better decision-making about where to focus your business. For example, knowing more about where demand or pricing are headed can help you figure out how many more barges to invest in, which routes could bring the biggest ROI, or where to build future facilities.

Chart your path forward with data and AI 

With no shortage of data in this era, there’s also no shortage of ideas for how to utilize it. But that wide landscape can be equally as overwhelming as having no ideas at all. Every company has limited resources and can’t do it all. 

To start evaluating where data and AI could fit into your organization’s future, choose just one potential use for data and AI. Invest there first. 

This first project might be the easiest to take on, either logistically or financially. It could be a use case based on something your customers need today, and that you haven’t been able to provide yet. Or, an initial AI project could be one that holds the most potential ROI. 

Next, you’ll need a strong data foundation. For this, start at the source. What’s the best method to collect the data you need? How do you meet growers where they are? For instance, is it easier to ride alongside them in a harvester and do one-on-one interviews? Or would it be easier for growers to manually upload information to a dashboard? 

Ensuring the right collection method, and then shoring up your data foundations, are the most important first steps. From that foundation, you experiment with where AI can make the most difference at your organization. 

Data and AI are here today, and will only increase their presence in the future. Understanding how best to leverage these tools will be the difference between organizations who fall behind — and those who will flourish in the data-powered future.