Artificial intelligence has given conservationists a better pair of eyes. Machine learning can count species in camera-trap images, and satellite models can spot forest loss almost as it happens. These tools provide governments and NGOs with a clearer view of ecological decline. Yet observation alone does little to change the activities causing that decline. It provides a better measure of the tide running out, rather than a way to turn it.
The larger opportunity for AI is to reshape the products and practices to improve performance, productivity, and efficiency while at the same time reducing the pressures that drive nature loss. At several points in the value chain, companies are designing more selective crop-protection chemicals, applying agricultural inputs more precisely, and developing alternatives to commodities linked to deforestation.
These are three cases representing a much broader opportunity, with potential applications across the major drivers of nature loss. In each case, the environmental benefit is tied to a business rationale companies already understand.
- Safer crop protection can improve product performance and reduce development risk.
- Precision agriculture can lower input costs while sustaining yields.
- Alternative ingredients can provide a more controllable source of supply.
Together, these applications create a new class of conservation opportunities financed through research and development, procurement, and operating budgets. (See Exhibit 1.)
Companies, investors, conservationists, and governments all have an interest in seeing these technologies succeed. Each also has a role to play in removing the obstacles that currently prevent them from reaching scale.
AI Can Make Crop Protection More Selective
Agricultural intensification threatens 34% of approximately 47,000 assessed species, according to the International Union for Conservation of Nature. Fertilizers alter waterways, while crop-protection chemicals can disrupt the insects and soil organisms on which healthy agricultural systems depend. A recent analysis across 26 countries found negative effects on soil invertebrates in 70% of the sites examined.
The problem is difficult to address once a chemical is already in widespread use. Environmental effects may become fully apparent only after approval and deployment at scale. At the same time, withdrawing an established product can be economically and politically contentious. Broader efforts to reduce pesticide use have also encountered resistance from farmers concerned about yields, crop losses, and the absence of effective substitutes.
AI creates another route by enabling companies to develop crop-protection products that remain effective against weeds and pests while reducing harm to other species. Traditional discovery processes synthesize and screen large numbers of compounds, with some environmental effects becoming apparent only during field trials or later stages of development. Machine-learning models can evaluate proposed compounds much earlier. They can predict protein structures, identify biological targets specific to a pest, screen large libraries of candidate molecules, and assess how those molecules may interact with non-target species.
Companies such as Bayer and Enko Chem are applying techniques developed in pharmaceutical research to crop protection. Bayer is using a system named CropKey to help match the protein structure of a weed with a chemical molecule that targets it, without impacting other plants or wildlife. Enko combines machine learning, DNA-encoded libraries, and structural biology to identify new molecules and modes of action. Its platform identified a novel weed-control target and biologically active inhibitors in four months, compressing a process that historically took far longer.
The commercial proposition is faster discovery and more differentiated products, rather than environmental performance alone. Crop-protection companies can reduce the time and cost involved in finding viable compounds. Farmers gain products that protect yields while reducing environmental risks, strengthening their position with regulators, buyers, and consumers.
Scientific promise will still have to translate into reliable field performance, affordable production, and regulatory approval. Early trials with agricultural businesses can establish efficacy under real conditions, while procurement commitments and targeted incentives can give developers greater confidence that demand will exist once a product reaches the market.
Precision Agriculture Can Cut Inputs and Costs
More selective chemistry will take time to develop and commercialize. A second lever is already available through using far fewer of the inputs farmers rely on today. For farmers, environmental impact and operating expenses are closely connected. Fertilizer alone accounts for 33% to 45% of operating costs for US corn and wheat producers. Crop-protection products are also applied across large areas because conventional machinery cannot always distinguish precisely between the plants that need treatment and those that do not.
AI-enabled precision agriculture changes that calculation. Systems combine real-time information about soil, weather, crops, and field conditions to determine where inputs are needed and where they can be avoided.
John Deere’s See & Spray system, for example, uses boom-mounted cameras and deep neural networks to distinguish weeds from crops in real time, including green weeds growing among green crops. Across more than 5 million acres during the 2025 growing season, customers reduced non-residual herbicide use by nearly 50%, saving close to 31 million gallons of herbicide mix. Trials also indicated higher soybean yields than conventional broadcast applications. See & Spray is sold on better crop and cost performance, which gives the technology a credible route to scale. Farmers purchase less herbicide and can improve yields, while fewer chemicals enter the surrounding landscape. That direct return helps explain why the precision-agriculture market is growing at roughly 20% a year.
Adoption still depends on the realities of farming. Equipment must work reliably in difficult field conditions, integrate with existing machinery, and operate where connectivity is weak. The strongest investments will pair effective software with the distribution, service, and financing needed to put it to work across farms, crops, and regions.
Alternative Ingredients Can Replace Land-Intensive Commodities
Land-use change is the greatest driver of nature loss. The conversion of forests, grasslands, and other habitats for agriculture, extraction, and development is associated with the declining conservation status of 72% of threatened species.
Palm oil illustrates the underlying economic challenge. It is cheap, versatile, and exceptionally productive, which is why it appears in products ranging from cosmetics to packaged food. Meeting demand, however, has contributed to the clearance of more than 30 million hectares of tropical forest in Malaysia and Indonesia alone.
Fermentation offers another way to produce comparable oils. Companies can use microorganisms such as yeast to create ingredients with many of the same functional properties. The challenge has been engineering those organisms to produce consistent outputs, at the yield and cost required by manufacturers. Historically, that process involved years of laboratory trial and error.
AI can accelerate development. Machine-learning models can analyze genomic datasets, predict which genetic modifications are most likely to work, and optimize the many variables involved in fermentation. That allows researchers to screen more possibilities digitally, concentrate laboratory work on the most promising strains, and compress development cycles that once took years.
Companies such as C16 Biosciences, Zero Acre Farms, and SMEY are using these approaches to develop oils for cosmetics, food, and other consumer products. C16’s first product, made with its bioengineered oil, sold out in less than three hours. Personal care offers a natural entry point because higher prices make it easier to absorb the cost of a new production process. Investors have committed tens of millions of dollars to companies in the sector.
Other businesses are addressing the production constraints that determine whether such ingredients can move beyond premium applications. Differential Bio, for example, uses AI to optimize microbial strains, feedstocks, and fermentation conditions. These improvements will be essential to achieving the consistency, yield, and economics required for commodity-scale production.
For large consumer companies, alternative ingredients could eventually provide a more controllable source of supply while reducing exposure to the reputational risks surrounding palm oil. The potential market is substantial. Palm oil is worth an estimated $78 billion today and could reach $114 billion within the next decade.
Reaching the mass market will require much lower production costs. Fermentation yields must improve, feedstocks must become more consistent, and manufacturing must scale enough to compete with palm oil at roughly $1 per kilogram. New food ingredients will also require regulatory approval. AI can improve the development and production process, but cost will determine how widely these alternatives are adopted.
Turn White Space into Markets
Our research points to considerable white space where AI can act directly on the drivers of nature loss. (See Exhibit 2.) The examples differ in their technology, economics, and maturity, but they share a central feature. Each can reduce ecological pressure by solving an operating problem for a customer. And the opportunities are broad. AI can trace critical minerals through complex supply chains, distinguish ore from waste in mining, and help developers steer infrastructure away from high-biodiversity habitats.
Converting those opportunities into scaled businesses will require the organizations that buy, finance, validate, and regulate these technologies to address different barriers.
- Businesses can find competitive advantage in conserving nature. Companies can map their use of harmful inputs, their largest costs, and their exposure to land-intensive or constrained commodities. That exercise can reveal opportunities to use AI to adopt safer products, reduce resource use, or replace vulnerable ingredients. Large buyers can also help create markets through paid trials, co-development agreements, and long-term procurement commitments that give suppliers the confidence to invest in commercialization and scale.
- Investors can finance deployment as well as discovery. Many of these technologies do not fit neatly into a conventional venture-capital model. They require specialist scientific and industrial knowledge, extended periods of operational validation, and, in some cases, significant investment in equipment or manufacturing capacity. That can leave them between AI-focused investors that lack sector expertise and industrial investors that are less equipped to assess the technology. Purpose-built investment vehicles can bring those capabilities together. Capital should also be matched to the stage of development. Venture funding may be appropriate for discovery, while project finance, infrastructure capital, or blended structures may be better suited to manufacturing and deployment. Anchor-buyer commitments and multiyear procurement contracts can provide the evidence of demand needed to unlock that capital. Investors can also use portfolio companies as early customers and test beds, helping technologies demonstrate value in operating environments.
- Conservation organizations can guide the applications of AI for nature. Their scientific and local expertise can identify where AI applications are most likely to reduce systemic pressure on nature, establish meaningful measures of impact, and assess possible effects on non-target species. They can also determine where digital or laboratory evidence needs to be supported by field verification. That role should also extend to AI’s own environmental footprint, helping to ensure that the infrastructure behind these solutions is built and operated as sustainably as possible. By helping businesses, investors, and consumers distinguish meaningful improvements from weak claims, conservation organizations can strengthen credibility and help early-adopting companies build demand for products that deliver demonstrable benefits.
- Governments can address the constraints that markets cannot resolve alone. Rigorous and timely regulatory pathways can give new products a clearer route to approval. Financing and tax incentives can improve the early payback from deploying new technology. Adapting subsidies and public procurement can also reward early adoption where a technology provides demonstrable benefits but has yet to reach competitive scale. Public support should remain focused on systemic obstacles preventing an otherwise viable technology from being adopted.
