Nature conservation isnât the first thing that comes to mind when thinking about the AI revolution, but it could also be transformed by the sweeping technological changes underway. The last years have seen a boom in naturetech products, such as drones for monitoring or reforestation, environmental DNA for mapping the presence of species over large areas, or bioacoustics for monitoring rare or endangered species.
AI is processing increasingly large biodiversity datasets like satellite images, helping identify and count species, and will quickly become essential for environmental assessments, management, or credit issuance. Investment in naturetech has surged in recent years at an average annual growth rate of 52% since 2018, reaching around $2bn in 2022. This boom has been driven by investments in biodiversity monitoring, environmental assessments, and ecosystem restoration, to name just a few examples.
Powering this new sector is artificial intelligence, which offers cost reductions, efficiency gains, and improved accuracy, creating new revenue streams for those able to best harness the technology. As global ambition around biodiversity restoration increases and national regulations tighten, it will become increasingly important to create solutions to monitor, conserve, and restore nature that are cheaper, more efficient, and accurate. Those companies that can provide accurate and reliable assessments or models will come with a competitive advantage in this nascent market. Today, AI is powering this change and making new business models possible.
All businesses are exposed to biodiversity risk, whether it is a regulatory risk, an investment risk, or because they are dependent on the services nature provides. Monitoring the impact business has on nature most effectively and transparently for regulators and investors is becoming increasingly important. Large established companies with complex supply chains will need to do this at minimal cost and will often outsource this to new startups specializing in naturetech and AI for nature.
AI is becoming essential in processing and interpreting the huge amount of new nature-related data being generated by the flurry of new technologies developed to help with nature conservation, restoration, and monitoring. In the past few years, cheaper and better performing technologies like camera traps, bioacoustics (microphones), remote sensing (drones and satellites), and environmental DNA (eDNA) have generated and continue to generate huge amounts of data for conservationists. Thanks to AI, we can now turn this trove of raw data into valuable information. Wildlife Insights, for example, uses AI to manage, analyze, and share camera trap data and enables a faster response to threats.
âOur offering could not be possible without AI,â says Olivier Stähli of Synature, a Swiss-based startup in bioacoustics. âAI is the reason bioacoustics has taken off in recent years; the technology has been around since the 50s, but it was complicated to gather and collect; the manual labor involved would make it impossible to implement on a large scale.â
In other words, AI is making new business models possible: while it is fairly straightforward to set up microphones in nature to record species, it would be nearly impossible to interpret this data at scale without AI, which can turn data into actionable information. For example, it can interpret audio or visual data directly, identifying the presence and abundance of species and providing reports that are accessible to non-experts, including businesses. This doesnât just speed things up; it creates new value propositions, allowing not just for data, but actionable insights to emerge from areas of operations.
AI can also help model and predict the future state of an ecosystem. The fundamental complexities and interactions of different ecosystems make them particularly suited to benefit from generative AI, which can help conservation activities change qualitatively, shifting efforts from remediation to prevention.
AI can, for example, predict species interactions and migrations, or declines and tipping points for biodiversity, so conservationists can act earlier. It helps understand and model the impacts of invasive species in a given environment and propose anticipatory solutions. This can help with business investment decisions, for example, predicting water scarcity or the impact of operations on the local ecosystems, helping avoid future stranded assets.
AI can also be used to identify patterns in behavior and activities associated with other environmental crimes like wildlife poaching or deforestation. Forest Foresight, a project run by the WWF, was able to predict deforestation based on satellite input data with 80% accuracy. Through better predictions and modeling round risk, AI can help businesses to identify areas to either prioritize or avoid based on dynamic data and within budgetary constraints, so businesses can optimize where they contract or build infrastructure.
Most efforts at nature conservation and restoration in the context of nature-positive strategies can benefit from efficiencies by using AI. Whether from a CSR, ESG, or marketing perspective, corporate efforts to engage in nature conservation will be made easier with AI tools, and the return on investment more attractive. The Conservation Area Prioritization Through Artificial Intelligence (CAPTAIN) project used reinforcement learning to train models for conservation prioritization that best use available data and resources to optimize conservation efforts. The experiment demonstrated the effectiveness of reinforced learning to identify priority conservation areas, with the AI model ending up protecting âsignificantly more species from extinction than areas selected randomly.â As AI makes conservation efforts not only cheaper but more effective, this can pave the way for non-expert actors like corporations to engage in the global goals around biodiversity, making for smarter conservation investments.
This is just the beginning. There are multiple efforts to decode animal language using AI, for example, which could expand the number and type of stakeholders considered in corporate activities. AI will be disruptive in more ways than one.
âPutting too much emphasis on AI can lead to a distracting form of techno-optimism, where we think solutions will come painlessly.â
There are drawbacks to using AI for nature. The Graphics Processing Unit (GPU) chips used for training large language models are notoriously power-hungry and consume millions of liters of freshwater. Although for now, most of the AI being deployed or considered for biodiversity and conservation is non-generative and therefore not highly power-hungry, that may change. Any conservation-driven AI effort must carefully balance the benefits that AI brings with the energy and climate impact these have on the planet.
There is also a still significant data inequality in conservation, with billions of fauna and flora observations concentrated in less than 7% of the worldâs surface, and a particularly poor distribution in the tropics, where most of the worldâs biodiversity is. Using this biased data to train AI models threatens the integrity of any inferred model by AI. âGarbage in, garbage out,â warns Wendy Elliott, Biodiversity Leader at WWF. âAI is only useful where the input data is strong.â However, this is still missing in certain areas. There will also be disparities in computational and coding capabilities between countries, which could further entrench data inequality.
Finally, putting too much emphasis on AI can lead to a distracting form of techno-optimism, where we think solutions will come painlessly. However, the crisis in biodiversity loss will require humanity to rethink business and value chains entirely. Ultimately, we need to decouple economic prosperity from the destruction of nature. AI can be a powerful help, but it will require humanity to make the tough choices and to execute.
The integration of artificial intelligence into nature conservation represents a potentially pivotal shift for the biodiversity crisis.
The integration of artificial intelligence into nature conservation represents a potentially pivotal shift for the biodiversity crisis. As businesses are increasingly made to recognize the strategic importance of nature-positive initiatives, those that effectively leverage AI technologies â directly or indirectly through the booming naturetech sector â will be best positioned to meet regulatory requirements, optimize conservation investments, and discover new sustainable business models. The naturetech revolution, powered by AI, isn’t just changing how we monitor and protect biodiversity, it’s creating an entirely new marketplace where technological innovation and environmental stewardship converge.
Businesses that develop or adopt AI-powered solutions for biodiversity monitoring & management are positioning themselves at the forefront of a rapidly expanding market worth billions.
1. Naturetech is a strategic investment opportunity: The sector is experiencing explosive growth (52% annually since 2018), with AI as its foundation. Businesses that develop or adopt AI-powered solutions for biodiversity monitoring and management are positioning themselves at the forefront of a rapidly expanding market worth billions.
2. Competitive advantage through data insights: AI transforms complex ecological data into actionable business intelligence, enabling companies to better understand environmental risks, anticipate regulatory changes, and optimize their nature-related investments â providing a distinct advantage over competitors still using traditional assessment methods.
3. Cost-effective compliance: As global biodiversity regulations tighten, AI solutions offer businesses more efficient, accurate, and cost-effective ways to meet compliance requirements for environmental assessments, management plans, and sustainability reporting, potentially turning a regulatory burden into a business opportunity.
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