Conversations about artificial intelligence (AI) are everywhere – and for good reason. AI is rapidly becoming a solution that boosts efficiency and productivity across industries, offering support to workers whether they’re in the office or at a remote jobsite.
While AI offers many benefits, its success hinges on aligning solutions with customer needs and thoughtful product design. As we develop AI-enabled products, we need to consider how these technologies will meet and support the evolving needs of customers. How will future AI solutions address the social, cultural, and environmental challenges ahead?
AI in Agriculture: Empowering Farmers for the Future
The United Nations estimates the world’s population will increase by more than 2 billion over the next 60 years, resulting in unprecedented demand for food and clothing. At the same time, the agriculture industry is dealing with a shrinking labor pool, increased weather volatility, and the depletion of arable land.
In response to these challenges, AI has emerged as more than just a tool to boost farmer output. It enables farmers to operate more efficiently, adapt to evolving environmental regulations, and continue to feed and clothe a rapidly growing population.
AI-Enhanced Precision for the Spraying Process
AI is transforming precision agriculture, particularly in improving plant-level accuracy on a large scale. Traditionally, after planting crops like corn, soybeans, and cotton, farmers typically spray herbicides to fend off the weeds that steal nutrients from growing plants and prevent them from meeting their full potential. Traditional spraying is done by the “broadcast” method – where everything gets treated (plant, weed, and soil). This is inefficient and ultimately impacts the farmer’s bottom line. It’s also less environmentally sustainable.
Today’s high-tech sprayers, enhanced with AI, sensors, and camera vision, can precisely scan fields and spray only weeds, reducing chemical use by up to two-thirds. The potential cost savings are a gamechanger for farmers, as herbicides are typically one of their most significant operational expenses.
These AI-powered sprayers collect valuable data that can be reviewed on farm management applications on smartphones or computers. With data such as detailed maps of the areas that have been sprayed, farmers can glean insights and make more informed decisions about their operation, such as deciding which parts of their fields need more spraying or less. The sprayers also get more accurate over time, as machine learning is used to continuously compare new images of weeds to the ones previously evaluated.
AI Integration: A Customer-Centric Approach
Technology leaders are actively exploring advancements like AI, aiming to leverage these innovations to enhance their businesses and better serve customers. More mature AI tools like computer vision and machine learning are already deployed at scale across industries, while emerging tools like large language models are finding their place in the market. However, adopting these capabilities requires a focus on solving specific challenges faced by customers.
As with any new feature or solution, success starts with a deep understanding of the customer. What challenges does the customer face? What gaps exist in product offerings to address those challenges? How does a potential solution meet market needs? Understanding how a new technology can serve customer needs helps ensure strong product market fit and helps avoid adopting technology for technology’s sake. Additionally, seeking broader stakeholder input and considering both the current landscape as well as challenges likely to emerge in the future will help direct product development efforts.
Equally important is fostering a business environment that embraces experimentation with emerging technologies. All new technology comes with potential to serve the product and the customer but also with technical limitations. Developing early prototypes and proofs of concept can help technologists understand the impact. Seeking early and continuous feedback from customers, getting real field time, and iterating on designs and solutions helps ensure that when a product is delivered, it finds success and benefits the customer. Businesses that iterate, continuously learn, and are open to experiments are likely to have the most successful AI implementations in the years ahead.
A Necessity for Food Security and Environmental Sustainability
As the global demand for food, fuel, and clothing grows while arable land decreases, AI has become an important part of the future of agriculture. AI-enabled equipment like advanced sprayers is just one example of how the technology is enabling farmers to manage plants at the micro level. And this is just the beginning.
As AI transforms nearly every industry, it’s crucial to keep customer needs at the forefront, ensuring solutions are thoughtfully designed and scalable. By doing so, we can harness AI’s full potential to help address the challenges of tomorrow.
About the Author
Sarah Schinckel is the director of emerging technologies in the Intelligent Solutions Group (ISG) at John Deere. Her team focuses on researching, developing, and supporting the deployment of Deere’s next-generation technologies to improve customer profitability and sustainability. She has 20 years of experience with software development for web and embedded systems, and has worked in a variety of roles across development, systems engineering, and engineering management. Sarah earned a bachelor’s degree in computer science, a master’s degree in engineering management from Iowa State University, and graduate certificates from Iowa State University and the Massachusetts Institute of Technology.
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