Artificial intelligence is beginning to reshape one of the most complex and expensive parts of agricultural innovation: determining which microorganisms, metabolites, biomolecules and naturally derived compounds have a realistic chance of becoming commercial products. In 2026, the convergence of AI, high-throughput screening, automation and advanced data analytics is gaining ground across agricultural R&D. The shift is increasingly relevant for biopesticides, biostimulants and other biological solutions, particularly in the United States and Latin America, where large agricultural markets and expanding biological portfolios are increasing demand for new crop-production technologies.

The significance goes beyond simply making laboratories faster. Conventional discovery programs can begin with thousands of potential candidates, while only a fraction justify moving into characterization, formulation, pilot production and field testing. AI does not eliminate those stages; it can change how researchers decide which candidates reach them. Computational models can analyze large biological and experimental datasets, identify relationships that would otherwise be difficult to detect and help prioritize promising candidates. High-throughput screening adds another layer by allowing researchers to evaluate more possibilities in less time.

Biologicals & Agtech

Artificial Intelligence Is Accelerating Agricultural Biosolutions and Reshaping Ag Innovation

1. From Thousands of Candidates to a Biological That Can Perform in the Field

One indication of how quickly this model is advancing can be found in the United States. Corteva Agriscience  reported that its researchers used artificial intelligence to model 10,000 potential crop-protection molecules in a matter of weeks, identifying dozens of promising candidates. Of particular relevance to the biologicals sector, the company also describes using AI to predict the diversity of biomolecules produced by microorganisms and to streamline fermentation processes for biological solutions.

That evolution is important because agricultural biologicals present a different discovery challenge from conventional chemistry. Researchers are dealing with living microorganisms, metabolites, natural compounds and interactions among plants, pathogens and environmental conditions. The ability to process increasingly large datasets can help narrow the search space, but biological activity discovered in a laboratory must still survive a much longer development pathway before reaching growers.

The same technological shift is reaching biostimulant development. Syngenta has been working with TraitSeq on artificial intelligence models designed to identify biomarkers that provide information about the cellular state of plants and their response to abiotic stress. The approach could help researchers identify biological candidates with greater precision before committing resources to later development stages.

In March 2026, Syngenta provided another indication of the direction of investment by announcing $130 million for BioSTaR, a new agricultural bioscience research center in the United Kingdom. The facility is expected to bring together biological sciences, molecular research, advanced analytics, digital innovation and artificial intelligence, with around 300 scientists working there once fully operational. The investment illustrates how the boundaries between biology, data science and conventional agricultural R&D are becoming increasingly interconnected.

But identifying a promising microorganism is only the beginning. For microbial products, fermentation can determine whether an interesting scientific discovery can actually become a reproducible commercial technology. Variables including pH, temperature, oxygen concentration, process duration, mixing, light and sterility can interact and influence microbial performance. Technical development data reviewed for this report highlight how controlling those parameters becomes critical when moving from laboratory discovery toward industrial production.

This is one of the less visible areas where AI could become particularly valuable for agricultural biosolutions. Fermentation produces multiple streams of information simultaneously, and relatively small changes can affect yield, stability or biological composition. Advanced analytics can help researchers identify relationships among those variables and better target experiments. The competitive advantage, therefore, may not belong simply to the company that discovers the most promising microorganism, but to the one capable of producing it consistently, economically and at commercial scale.

Formulation creates another bottleneck. Next-generation products may combine microorganisms, plant-derived substances or other active components that must remain stable throughout manufacturing, storage, transportation and application. That challenge is driving technologies including encapsulation and bi-phase formulation systems, which can protect sensitive ingredients or keep different components separated until application. Other systems must successfully maintain suspended solid particles, emulsified components and liquid matrices without compromising biological performance.

The implication is critical: faster discovery does not automatically mean faster commercialization. AI can potentially eliminate weak candidates earlier and direct resources toward stronger ones, but a selected biological still needs adequate stability, manufacturing scalability, field efficacy and storage performance. As discovery becomes faster, part of the bottleneck may therefore shift downstream-from finding biological activity to turning that activity into a reliable agricultural product.

2. The U.S. and Latin America Become Key Markets for the Next Biologicals Race

For the United States, this technological convergence comes as biologicals become increasingly integrated into the broader crop-protection and plant-health business. The U.S. Environmental Protection Agency regulates several categories of biopesticides, including microbial pesticides and biochemical pesticides, meaning that accelerated discovery does not remove the need to establish the information required for regulatory review. U.S. EPA Biopesticides Program

This creates an important distinction between discovery speed and regulatory readiness. AI may help scientists identify candidates earlier, organize experimental information and potentially improve the efficiency of dossier preparation. But regulators still need evidence addressing the characteristics, safety and intended use of a product. In biologicals, where microorganisms and naturally derived substances can differ substantially from conventional synthetic active ingredients, the quality and consistency of the underlying data become particularly important.

Latin America adds another dimension to the story. The region combines some of the world's largest crop-production areas with diverse climates and rapidly developing biological markets. Brazil stands out as a major testing ground for the expansion of biological inputs. Brazil's Ministry of Agriculture and Livestock reported that 162 products classified as bioinputs were registered in 2025, the highest annual number in the country's history. The category included biological and microbiological products, biochemicals, plant extracts, growth regulators and semiochemicals.

For developers, Brazil's growth creates both an opportunity and a challenge. More biological candidates require greater capacity for characterization, formulation, field validation, manufacturing and regulatory data management. This is precisely where the combination of AI, automation and biological research could become commercially significant. Faster candidate identification has limited value if companies cannot subsequently demonstrate that those products remain stable, scalable and effective under the conditions found in major agricultural regions.

Argentina is also adjusting its regulatory environment. SENASA  has updated procedures affecting the registration of bioinputs for plant protection. For technology developers, this reinforces the same fundamental principle seen in the U.S. and Brazil: a candidate identified through an AI-assisted discovery process still needs a regulatory package capable of supporting its identity, quality, efficacy and safe use before commercialization.

The emerging development chain therefore extends well beyond artificial intelligence itself:

That last part may be particularly important for North and South American agriculture. AI can accelerate selection, but the field remains the ultimate test. A candidate identified through computational models must perform under variable temperatures, soil conditions, humidity, application systems, crop-management programs and different pest or disease pressures. A biological that performs well in a controlled environment may respond differently across the U.S. Corn Belt, California specialty crops, Brazil's Cerrado or Argentina's Pampas.

For U.S. agricultural companies, this creates a new competitive landscape connecting crop protection, biotechnology, biologicals and data science. For Latin America, the opportunity could be even broader. Brazil, Argentina and other regional agricultural markets combine biodiversity, extensive commercial farming systems and growing experience with biological inputs. Connecting those advantages with sequencing, bioinformatics, AI, automated screening and modern fermentation platforms could allow the region to participate not only as an adoption market, but also as a development and manufacturing hub.

The strategic asset of the next biologicals cycle may therefore be much larger than a microorganism collection or a promising natural compound. Increasingly, it is the entire platform: biological data + artificial intelligence + microbiology + fermentation + formulation + field validation + regulatory expertise. Companies capable of connecting those capabilities could eliminate unsuccessful candidates earlier, allocate R&D capital more efficiently and focus experimental resources on technologies with stronger development potential.

That may ultimately be the most consequential change AI brings to agricultural biologicals. It is not about replacing scientists, agronomists or field trials with an algorithm. It is about building an R&D system capable of extracting useful decisions from volumes of biological and agronomic information that are becoming increasingly difficult to analyze manually. The race is already moving from concept toward investment and application. The next challenge will determine its agricultural impact: turning faster digital discovery into biological products that are stable, scalable, registrable and, above all, capable of delivering consistent performance in farmers' fields.