This is the third post of a four-part series showcasing how WRI’s Data Lab is addressing global challenges through innovative technology strategies and products. In each part we focus on the strategies, specific products and underlying technologies shaping our approach. Our goal is to inspire collaboration across sectors and highlight how data-driven solutions can drive meaningful, scalable impact. As we continue exploring transformative opportunities in impact tech that we’re excited about, we’re now turning our focus to specific products Data Lab is actively investigating and/or building.
While these products are still in the early stages, we see potential in each one to address key challenges and drive meaningful change.
Here are four products that are built on technology-driven solutions, designed to scale and create real-world impact. We invite potential users, partners and funders to dive into these ideas and join us in shaping their development.
1. Field collection technology at the edge: Next generation product and interfaces
Field data collection is a fundamental part of many WRI research and data products, giving us experience in the many challenges that come with its existing technology. Despite years of work, users still face major challenges including difficulty onboarding, clunky interfaces, a lack of multilingual support, products not optimized for their devices and connectivity constraints.
We see (and are actively investigating) major opportunities to improve field data collection technology across different use cases, particularly in WRI’s Global Restoration Initiative. Advances in artificial intelligence and large language models (LLM) allow for remote sensing datasets that can complement field data collection, as well as more scalable product onboarding and localization capabilities.
We’ve also been experimenting with how chat-based, LLM-powered interfaces allow for richer geospatial data product experiences on smartphones than ever before, which allows local stakeholders to more effectively utilize the datasets they are contributing to. On the other hand, the more advanced technology also needs to work with limited internet connectivity and on user devices, which in many cases are older smartphones.
Our research suggests that through AI and LLM advancements, field data collection technology can be easier to use, lead to more accurate outcomes with less time spent on quality assurance and enable the creation of more complex and impactful datasets.
2. AI assistants and agents for researchers
In the last few years, it’s become increasingly clear that generative AI has significant potential to help knowledge workers perform a variety of tasks more efficiently, and sometimes more effectively as well. While tools like ChatGPT are already used heavily by many knowledge workers and researchers, role-specific AI models, chat interfaces and agents are emerging for specific professions. We see a major opportunity to develop a suite of tools (likely a mix of “off the shelf” tools and novel products) to supercharge analysis and extend skills into new areas, such as software tool development.
When it comes to helping researchers with their day-to-day work, AI tools like Scholar AI and Julius AI are showing the potential of generic AI assistant for the research process and data analysis respectively. Meanwhile, AI coding and copywriting assistants represent a meaningful opportunity to allow researchers to build their own software and create accessible content from their work.
In the future, we see increased potential for autonomous, domain-specific AI agents or assistants to conduct research and analysis tasks, with the appropriate risk mitigation and safeguards.
3. AI-generated reports and documents from geospatial data
When analyzing user behavior and workflows for WRI products, we often see that technical users (such as an urban planner) use the product to conduct analysis or uncover insights and then report these findings to decision-makers (such as a city mayor). Almost always, a key step in this workflow is creating a document or graphic that helps the decision-maker best understand the technical analysis.
WRI’s geospatial data platforms (and many similar products) are used by a wide range of users. Historically, developing features for content creation that can effectively serve these many users and use cases has been challenging.
Advances in generative AI provide a fresh look at this problem: There’s now potential to develop custom-generated outputs of geospatial platform data at scale, with the ability to fine tune the outputs to match the correct format, language and technical depth of the target user. In short, we see the potential to help users easily generate the right document, at the right time, to help their analysis more effectively be used by decision-makers, whether it’s a water resources planner generating a policy memo from water stress indicators or an urban planner generating a summary PDF in their city from a heat stress dataset.
Several existing products already give hints of the potential for this “generative documents” concept, such as OpenAI’s “Deep Research” feature, Visme AI’s document generation abilities and tools like Copy AI and Jasper for generating content, but these options aren’t optimized for working with geospatial data.
4. Open data tools for assessing water impacts of critical minerals mining
Critical minerals such as copper, lithium, cobalt and nickel are the building blocks of many important technologies for our transition to a net-zero economy, including electric vehicles, solar panels and wind turbines. As the demand for critical minerals grows, there is a tension between balancing their importance to the energy transition and the environmental and social risks from their extraction process.
Specifically, we see that mining companies are largely underreporting the impact of critical minerals mining and processing on freshwater quantity and quality. Many companies are developing water risk and impact strategies across their value chains, but they are lacking key information (leading to inaction) with respect to these risks from critical minerals extraction.
To address this knowledge gap, we are building open data tools and platforms to help assess and communicate water-related risks from critical minerals production. Like WRI’s Global Forest Watch (GFW) for forests and Aqueduct for water risk, these tools are designed to surface geospatial insights in a usable way for governments, mining and downstream companies in the critical minerals supply chain.
These product concepts represent the early stages of exciting work happening at WRI’s Data Lab, each aimed at solving critical challenges with scalable, technology-driven solutions. As we continue building, we welcome collaboration with potential users, partners and funders who share our vision for impact.
In the fourth part of this series, we shift our focus to the underlying technologies and infrastructure that provide the foundation for these products, exploring the systems and innovations that support their development and scalability. Read the next post here: Underlying Technology and Infrastructure Opportunities in Impact Tech
Across this series, WRI’s Data Lab outlines the strategies, products and technologies we believe hold real promise for accelerating action across people, nature and climate. These approaches reflect where we see the biggest opportunities to close critical gaps.
While many of these concepts or strategies are already in development at the WRI Data Lab, others need collaboration, investment and technical expertise to move forward and realize their potential. By sharing these opportunities, we aim to spark dialogue and build partnerships with those equally committed to technology-driven solutions that address today’s urgent challenges.
If you’re working on any of these exciting areas — as a researcher, funder, policymaker or technologist — let’s talk. Reach out to us at datalab@wri.org.
Other posts in this series:
