Learn how to leverage generative AI to enhance the everyday technical data sheet in the chemical industry.
Chemical Manufacturing
January 16, 2025
The agrochemical industry plays a vital role in modern society. Essential products like fertilizers, pesticides, and plant regulators contribute to overall crop yields and the quality of consumer goods. With a projected market growth of $305 billion by 2030 at a CAGR of 3.4%, agrochemicals are invaluable to food security for the world’s growing population.
As the agricultural sector as a whole evolves, artificial intelligence (AI) is an innovative solution to some of its longstanding challenges. In this article, we’ll explore the intersection of AI and agrochemicals as it relates to the opportunities, challenges, and practical applications that are leading the way for more sustainable and efficient farming.
Agrochemicals encompass any chemical products that industrial agriculture operations use to protect crops and optimize yields. The most common types of chemicals in agriculture include:
Chemical substances in these products contribute to everything from soil fertility to pest control to increased food production among modern farms. Additionally, as sustainability measures become more stringent and consumers opt for more organic alternatives, these chemical formulations are constantly changing.
Along with the chemical industry as a whole, there are several pressing challenges in agrochemicals. These include:
With the right approach, chemical companies can harness profitable opportunities with generative AI (Gen AI) platforms in agriculture.
Gen AI supports agricultural departments from R&D to supply chain traceability to sales. AI specifically offers technical sales teams powerful features that simplify their product messaging and improve customer interactions. With the increase of sustainable pesticide formulations, AI-based deep learning tools help sales reps prompt more eco-friendly solutions to meet their customers’ environmental concerns.
For example, Syngenta’s adoption of AI has reduced the time and resources required to bring new, environmentally friendly products to market. Plus, through machine learning and agronomical data, sales reps can now suggest specialized options that improve yield and sustainability.
More and more, AI solutions are integrating with precision agriculture applications so that sales teams can provide not only tailored product recommendations but also equipment suggestions to customers. For instance, sales reps can determine which chemical applications are best for unmanned aerial vehicles (UAVs) that enable intelligent crop spraying, which could be a compelling selling point.
AI accelerates product development cycles, giving technical sales teams a real-time competitive edge so that they can explain cutting-edge solutions to customers. Image recognition technology, such as BASF's collaboration with Alteia for field trials, provides sales teams with visual data that can demonstrate product efficacy. This AI-driven approach to R&D not only speeds up the development process but also yields more targeted and effective solutions, empowering sales teams to address specific client needs with greater precision.
While AI offers numerous benefits, its implementation in the agrochemical sector faces several hurdles.
Since crop and livestock management are such high-stakes practices, farmers require 100% accuracy of the chemical products they use. Therefore, AI systems must be rigorously tested and validated to earn trust in the field, whether implemented in manufacturing, sales, or precision farming operations. Addressing trust issues regarding AI is crucial for technical sales teams so that they don’t recommend the wrong products.
Agrochemical companies often have established agronomy protocols to ensure soil health and crop production. Many companies expect AI solutions to effectively complement these existing systems, which would require a careful balance of AI capabilities with traditional agronomic expertise.
Intense regulatory standards in agriculture can slow the adoption of AI innovations in the chemical side of things. It’s crucial that companies confirm potential data solutions meet all necessary compliance standards and can integrate with reporting measures like safety data sheets. That said, GenAI features can support agrochemical teams by ensuring existing or new formulations are meeting the latest regulatory requirements.
The capabilities of AI in agrochemicals will only continue to advance. Already, GenAI is shaping the industry across sectors and will likely have a growing impact.
Technologies as powerful as AI help the world address the challenges of modern agriculture. From streamlining application methods to helping sales teams succeed, AI is helping drive innovation and efficiency across this invaluable sector.
Successful adoption of AI in agriculture requires a collaborative approach. Farmers, regulators, and technology providers must work together to make sure new AI solutions are accurate, trustworthy, and compatible with the needs of sustainable agriculture.
For agrochemical companies spearheading the way forward, embracing AI is no longer optional — it's essential. By using chemical sales platforms like Nesh, you support your technical sales processes and ultimately contribute to a more productive agricultural future.
Nesh Sales AI provides unbeatable solutions for agrochemical teams who want to take the next step toward a more intelligent process. Sound like you? Book a demo with Nesh to learn more about the capabilities of generative AI for your company.
Want to learn more about GenAI use cases for the chemical industry? Check out the fact sheet, The Future of Chemical Sales: 6 GenAI Use Cases.
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