AI + Environment
Useful intelligence shouldn’t cost the earth.
Dataro uses AI to help organisation teams know who to focus on and what to do next. We also take responsibility for the carbon created by that work. We measure our footprint, reduce what we can, and offset what remains.
Our commitment is simple: account for the impact
Responsible AI starts with visibility. We measure the emissions connected to our AI services, reduce avoidable compute, and compensate for the remainder with transparent, high-quality offsets.
Measure what we use.
We estimate the emissions associated with model use and the cloud infrastructure that supports it.
Reduce before we compensate.
We choose efficient models, shorten unnecessary workloads, and design product experiences that do more with less compute.
Offset the remainder.
For emissions we cannot eliminate yet, we support high-quality climate projects.
We use the smallest model that does the job
Most of what Dataro does runs on predictive AI, not generative AI. That difference matters for your results and for the planet.
Predictive models read your giving history and rank your donors: who to ask, who may lapse, and who is ready to give more.
Most predictions run on gradient-boosted trees, a proven, lightweight method that trains and runs on modest computing power.
PREDICTIVE AT THE CORE
Predictions stay lean.
Sharp, reliable donor rankings without the heavy footprint of large generative systems.
SMALLEST MODEL THAT WORKS
Efficiency is built in.
We use the smallest model that solves the problem, so modest compute delivers useful results.
HEAVY MODELS, HANDLE WITH CARE
Generative AI must earn its place.
We reserve it for cited donor research, not the predictions that drive your fundraising.
Better targeting, fewer wasted contacts and a smaller footprint, all at once.
Tree planting offsets the footprint we can't yet remove
Through our partnership with Tree-Nation, Dataro funds tree planting across verified reforestation, agroforestry and ecosystem-restoration projects. Our monthly team program is one part of that commitment; we also plant trees to address the wider footprint of running our business.
Every planting is recorded in our public digital forest, where anyone can see the trees funded, estimated CO₂ capture and updates from project sites. We reduce emissions first by choosing efficient technology, then fund tree planting to help offset the footprint that remains.
2.1K+ trees
Total planted
2.04+ hectares
Total reforested
279+ tonnes
Total CO₂ captured
WHITEPAPER
The Efficient AI Question
AI covers three very different technologies with very different energy footprints. This white paper explains why the most responsible choice is usually the smallest model that solves the problem, and what that means for fundraising teams.
01
Predictive, generative, and agentic AI compared.
02
Why lightweight predictive models suit donor scoring.
03
How Dataro reduces first and offsets second.
Questions about AI, efficiency, and our footprint.
How Dataro chooses efficient models, measures impact, and supports tree planting through Tree-Nation.
Does predictive AI use less energy than generative AI?
Yes. Predictive AI typically uses far less energy than generative AI because it runs on smaller, task-specific models. In one controlled test on the same task, a gradient-boosted tree (XGBoost) used about 0.03 Wh while a 70-billion-parameter generative model used 34.15 Wh, roughly a thousandfold difference (Zschache and Hartwig, 2026). Figures like these are directional third-party estimates, not fixed rules.
What is the difference between predictive, generative, and agentic AI?
Predictive AI scores and ranks data to forecast an outcome, such as which donors are likely to give. Generative AI creates new content, like text or images. Agentic AI chains many generative steps together to complete a task on its own. Each step up adds computing work, so agentic systems generally use the most energy per task.
How can organisations use AI responsibly?
Organisations can use AI responsibly by matching the tool to the job and keeping a human in the loop. Use predictive models to rank and prioritize donors, use generative AI to draft content that a person reviews, and never ask a chatbot to forecast who will give. Grounding every output in approved data reduces both error and energy use.
What are gradient-boosted trees and why do they matter for fundraising?
Gradient-boosted trees are a proven machine-learning method that performs well on the kind of structured, tabular data charities hold in their CRMs, such as giving history. They matter because they produce accurate donor rankings while training and running on modest computing power, so fundraisers get sharp targeting without the heavy footprint of large generative models.
Is AI bad for the environment?
AI’s environmental impact depends heavily on the type of model and how it is used. Large generative and agentic systems are energy-intensive to train and run, and data centres used an estimated 460 TWh globally in 2022, projected to exceed 1,000 TWh by 2026 (IEA, 2024). Smaller predictive models use a fraction of that, so choosing the lightest model that solves the problem lowers the footprint.
