In her own words, Ali Swanson is a “tech skeptic.”

This may seem odd, given Swanson’s role as Conservation International’s director of nature tech and innovation. But Swanson rarely uses words like “transformational” or “revolutionary” to describe artificial intelligence (AI), the broad term for emerging technology that can mimic human cognitive abilities. She has yet to catch the AI-will-kill-us-all fever, nor does she think AI is humanity’s salvation.

Still, Swanson, an ecologist by background, says she believes that AI can help conservationists dramatically increase the scale and speed of their work. And she spends her time and brainpower exploring ways to make that happen.

Conservation News spoke with Swanson to unpack the perils and promise of integrating AI with conservation.

Conservation News: What problems can artificial intelligence solve for conservation?

Ali Swanson: I think there are three key opportunities. First, this technology can help us understand the world faster and at greater scales. We’ll be able to map and monitor changes and threats with far greater precision and speed.

Second, AI can help us both optimize — and future-proof — our decisions. A lot of conservation is about maximizing our impact with limited resources and information. Broadening the scope of information will help increase certainty, which is something funders covet. AI lets us not only consider so many more variables in our calculations but also predict what might happen in the future — so we can make the best decisions today that last for generations to come.

And then the third piece, and this is something I'm really excited about, is thinking about how we can help democratize conservation action — putting our science into the hands of the people who can put it into practice.

Tell me more about that.

AS: Let’s take nature credits. There are dozens of different ‘nature credit’ methodologies out there, and they're all over the map on what they ask you to measure and report on — even the goals.

Imagine you could take all those frameworks, all the ecological data that's ever been collected for a place that you're working in, and all the global data sets that are out there — and throw them into AI. Then, you could ask it, in simple language, ‘Hey, I’m trying to get a nature credit in Yaguas National Park in Peru. What framework do I use? What biodiversity do I monitor to get that credit? How much will all this cost me?’ We are partnering with AI developers to build AI tools to help teams navigate these very questions — and testing the answers in CI’s nature credit pilots.

Right now, the conservation movement struggles to make informed decisions quickly. And it's not unique to nature credits; this is relevant to any kind of financing mechanism. This technology can push the bounds of what's possible in that respect. And I think it's the kind of thing that is achievable within the next ten years.

So far, we’ve discussed hypotheticals. Where is Conservation International already embedding AI into its programs, and what have we learned?

AS: Historically, we’ve used machine learning or deep learning to process large volumes of data much faster — like camera trap images in the Wildlife Insights program, or satellite data in our Climate Smart Shrimp program.

But we’re looking to dramatically increase the sustainability and accessibility of AI tools. We have an incredible opportunity with funding from the Patrick J. McGovern Foundation to pursue AI innovation. With this support, we are building out a dedicated AI curriculum at CI to upskill our staff and empower them to use AI strategically and responsibly. We are also partnering with AI developers to prototype AI agents that can help stakeholders make better conservation decisions, such as with nature credits or restoration.

We’re still figuring out what works and what doesn’t. At the same time, while I think AI holds enormous potential to help us do our work better and faster, we need to recognize it's not a panacea. As with any technology, it will create more challenges and problems to solve.

One of those challenges is AI’s massive environmental footprint. The average American datacenter uses roughly 17,000 showers’ worth of water each day, for example. How do you think about the tradeoff of potential long-term progress at a real short-term environmental cost?

AS: There’s no easy answer here, but I think the first step is visibility. Just over the state line from my house sit two coal-fired power plants that feed data centers. It makes visible for me a cost that is otherwise invisible just sitting at a computer prompting a chatbot.

In the big picture, I do think the benefit of doing our work faster, bigger and better will outweigh the costs of our AI use, in part because we're leveraging a tool that has largely been built and developed already.

But at Conservation International, we have a unique opportunity — and therefore, a responsibility — to bring different actors to the table, including major tech partners, other NGOs and world leaders to have this discussion. How can we, as a conservation sector, harness this tool for good, recognizing the costs that it creates? It’s one of the most important questions of our generation.

You’re an ecologist, but you now work on technology. How did that happen?

AS: I very much stumbled into this world of technology as a graduate student, studying how large carnivores co-exist in East Africa. The tools and methods that I needed to answer questions I was asking about lions, hyenas, cheetahs and wild dogs simply didn't exist. There was this clear need.

So, I partnered with technology developers to build the tools and approaches to collect data, interpret it and turn it into actionable scientific knowledge.

And now at Conservation International, I work across the organization to help our teams access the tools and technologies that they need to advance their work on the ground.

Max Marcovitch is a senior writer at Conservation International. Want to read more stories like this? Sign up for email updates. Also, please consider supporting our critical work.