Artificial intelligence (AI) is increasingly recognized as a foundational technology capable of transforming how society operates and exerting paradigm shifts across disciplines, including environmental and conservation science.
Its potential is emerging at a critical moment given the rise of climate-related shocks and stressors around the world. Despite progress in conservation and restoration efforts, the complexity and urgency of these challenges call for new tools that can operate at greater scale and speed. AI presents a powerful opportunity to create these tools.
How companies are deploying AI for nature
From predicting environmental risks to species monitoring and ecosystem modeling, here’s how some companies are already using AI to improve decision-making while proactively managing operational risks related to nature and biodiversity loss:
1. Risk detection, mitigation and response
Early detection is critical when managing landscape risks like wildfires, drought or invasive species. It enables businesses, conservation and land management agencies, and communities to respond proactively to potential crises.
AI techniques have been used for many years to map key ecological features and changes, underpinning ecosystem assessment and management. Combining high-resolution aerial data and AI detection tools can also help identify significant risks to ecosystems, such as the spread of invasive plants.
For example, TELUS is advancing post-wildfire resilience in Canada’s forests by integrating connected technologies into restoration efforts. Through partnerships with technology company Dryad Networks and drone services provider Flash Forest, TELUS uses Internet of Things (IoT) sensor networks to enable ultra-early wildfire detection and risk monitoring at key ecosystem restoration sites.
Other AI-powered platforms like Pano AI help businesses and governments to identify early-stage wildfires by combining sensor networks and predictive modelling to protect assets and infrastructure, including critical ecosystems.
2. Wildlife and biodiversity monitoring
Trained machine learning models, trail cameras and autonomous systems are being used to analyse soundscapes, track biodiversity outcomes from forest-based interventions and monitor threats to wildlife from poaching and other illegal activity. Effective monitoring can enable businesses to manage the risk of biodiversity loss and adhere to evolving environmental disclosure frameworks.
As part of the Wildlife Crime Technology Project, the World Wide for Nature (WWF) and Kenya Wildlife Service have deployed thermal cameras at Kenya’s Solio Game Reserve to provide continuous virtual monitoring. The AI-integrated system automatically detects and classifies humans, wildlife and vehicles, sending real-time alerts to operators to enable rapid response to intrusions.
And in South America, Project Guacamaya is working with Microsoft’s AI for Good Lab to use solar-powered microphones, satellite imagery, camera traps and bioacoustics to monitor real-time soundscapes, protect biodiversity in tropical forests and accelerate conservation work in the Amazon.
3. Ecosystem rewilding and site selection
When used in rewilding projects, AI tools can provide predictive planning and modelling of long-term ecosystem changes. By layering soil, hydrology and climate datasets, AI tools simulate different restoration scenarios and assess potential outcomes. For example, AI tools coupled with bioacoustic sensors have generated fine-scale data on pollinator diversity to deliver clear evidence of rewilding success.
Google’s Tree Canopy tool uses AI and aerial imagery to enable nearly 350 cities to map, manage and enhance their urban forests. This helps them address extreme heat vulnerability and supports city-level climate resilience. Current research is also exploring autonomous rewilding interventions, such as drone seeding and invasive species eradication, as scalable alternatives to traditional restoration methods.
4. Storytelling and engagement
AI can be a powerful tool for storytelling. Generative AI (GenAI) models have been used to create “before-and-after” visions of degraded and restored landscapes, for example. These tools offer compelling images for education and outreach, partnership development, fundraising, ESG reporting and building stakeholder trust.
AI can also support scenario planning and futures thinking activities, playing a role in participatory planning and community conservation initiatives.
5. Supporting Indigenous knowledge and practices
AI also enables climate adaptation and Indigenous-led resource planning. In Sanikiluaq in Canada, a custom AI system combines Indigenous knowledge with satellite imagery and other Western science methods to map prime habitat for scallops, kelp and clams in areas that are rapidly changing due to climate change.
An integrated approach like this helps close data gaps, supports sustainable mariculture and demonstrates how blending technology with traditional knowledge can strengthen resilience and local livelihoods.
Putting nature and people at the centre of AI tools
AI is not neutral, it reflects the values, assumptions and biases of those who build it. AI systems must not reinforce inequities that may already exist in conservation science and practice.
Data used to train models is often incomplete or biased, and may exclude or misrepresent knowledge systems beyond Western science, such as Traditional Ecological Knowledge. The algorithms at the foundation of AI tools can also be opaque and undermine transparency around how decisions are made or why certain outcomes are favoured. As such, data sovereignty and inclusive design must be central to how AI is developed and deployed in ecological contexts.
Indigenous People play outsized roles in land stewardship and biodiversity protection, so they must lead in shaping how these AI tools are created and applied in the natural world. This should include grounding the development of AI and related technologies in common Indigenous principles like relationality, reciprocity and ecological responsibility.
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Increased AI use will also have environmental consequences, including enormous consumption of water and energy. The electricity used by data centres is expected to more than double by 2030 and their liquid cooling needs have already significantly increased water usage in recent years. Although AI can serve as a positive force for nature-based climate action, mitigating the negative environmental impacts of widespread technology adoption and identifying avenues for more sustainable technology and AI solutions should be a priority.
As the integration of AI into nature conservation and restoration efforts grows, these technologies must actively generate nature co-benefits – from improved biodiversity outcomes to cultural preservation. AI tools should be designed and used not just for technical efficiency, but to strengthen ecological integrity, support species recovery and contribute meaningfully to long-term climate resilience.
