Concerns about the energy and water demands of artificial intelligence are increasing, and nonprofit leaders are asking if adoption is aligned with their values.
Generative AI models require substantial electricity to train and to operate at scale, and data centers often rely on water‑intensive cooling systems to manage the heat these systems generate.
For mission‑driven organizations, this raises legitimate questions. Not because nonprofits are the primary drivers of AI’s environmental footprint, but because ethics, values, and responsible stewardship are core to our leadership.
But nonprofits are not powerless. While individual organizations do not control how global AI infrastructure is built, they do control how AI is used internally, which tools are selected, and how thoughtfully those tools are governed. And Heller has found that can have a big impact on the organization’s contribution.
Start by understanding how AI works
One of the most effective ways nonprofits can mitigate the environmental impact of AI is by understanding where that impact comes from.
AI systems consume resources in two primary ways. The first is training, which involves large, concentrated bursts of computing power to build models. This is typically handled by major technology vendors. The second—and increasingly significant—is inference, which is the everyday use of AI when staff generate text, summarize documents, or analyze data.
When building an AI roadmap for an eco-minded organization, Heller surfaced research that a single ChatGPT‑style query can use approximately five times more electricity than a standard web search. While that difference may seem negligible in isolation, it compounds quickly when AI is used frequently or embedded into daily workflows.
Water use is even less visible. A University of California, Riverside study estimates that running 20 to 50 AI prompts can evaporate roughly half a liter of fresh water, due to the combined effects of electricity generation and data‑center cooling.
For nonprofit leaders, the ethical question is not whether to use AI at all: It is how to use it intentionally and proportionately, in ways that deliver value without unnecessary waste.
Different AI use cases, different environmental footprint
Nonprofit leaders should weigh the relative environmental intensity of various AI applications when planning projects. A lightweight “chatbot” that assists staff will have a far smaller footprint per use than a large-scale data analytics model or custom AI research project. The table below is based on research compiled by Heller Consulting for an eco-minded nonprofit considering implementing AI solutions.
AI Application Type | Example & Scope | Relative Compute or Energy Use |
| Conversational Assistants (e.g. chatbots, writing aids) | Uses pre-trained models for tasks like Q&A, drafting content in real-time. | Low–moderate per query. Each prompt uses only a fraction of a watt-hour (comparable to a web search). However, heavy user adoption can scale up overall energy demand. Minimal incremental water use per query (cooling needs mostly aggregate at data-center scale). |
| Analytical AI & ML Tools (e.g. predictive models, data mining on nonprofit datasets) | Runs more complex models on larger data (e.g. trend analysis, scenario modeling). | Moderate–high per job. Such tasks often require more compute cycles than simple chat – potentially 10× or more energy per run compared to a basic query if complex reasoning or large data sets are involved. Higher compute means higher cooling water usage as well. |
| Advanced AI / Model Development (e.g. training or fine-tuning large models, intense multi-turn reasoning applications) | Develops or uses very large-scale AI (LLMs or similar) tailored for research or specialized tasks. | Very high. Training a cutting-edge model can draw thousands of kWh and significant water for cooling. Even inference with the largest models consumes many times more energy per query than smaller AI systems. These intensive applications have the biggest environmental footprint. |
Given these differences, nonprofits should incorporate sustainability into AI decisions. For instance, if a lighter-weight AI tool can meet a need, it may be preferable to an extremely compute-heavy solution. IT leaders might also favor vendors that are transparent about their data center energy mix or have “green AI” commitments (many hyperscalers aim to be carbon-neutral or water-positive by 2030). In governance terms, treating energy and water efficiency as criteria in your AI roadmap can align technology adoption with your organization’s broader environmental values and responsibility to communities.
Nonprofit AI operations and ethics
For COOs, operations leaders, and senior IT leaders, AI’s environmental footprint connects directly to everyday responsibilities.
First, there is the matter of ethical stewardship. Nonprofits are trusted to use all resources—financial, human, and environmental—carefully. Even when sustainability is not a core programmatic focus, avoiding unnecessary consumption aligns with nonprofit ethics. Choosing a highly resource‑intensive AI solution when a simpler tool would suffice is not just a technical decision; it is an ethical one.
Second, there is credibility and trust. Many nonprofits serve communities impacted by climate change, water scarcity, or infrastructure strain. As public awareness of AI’s environmental costs grows, boards, funders, and staff may increasingly ask how technology choices align with organizational values. Being able to answer that question thoughtfully reinforces trust and leadership credibility.
Finally, there is operational discipline. AI use has downstream effects on cloud costs, vendor dependence, and long‑term resilience. Treating AI as frictionless or “free” can undermine operational planning. Responsible AI use supports both ethical alignment and sound operations.
How your nonprofit can use AI ethically
Nonprofits do not need to become climate experts to make better AI decisions. What they need is clarity, governance, and shared expectations.
It starts with leadership alignment. Before evaluating models or vendors, organizations benefit from agreeing on a simple principle: what values should guide our use of AI? For some nonprofits, this may explicitly include minimizing environmental harm. For others, it may mean restraint, transparency, or avoiding unnecessary complexity. Making those values explicit creates an ethical foundation for downstream choices.
From there, responsible use often comes down to matching the tool to the task. Not every problem requires the largest or most advanced AI model. In many cases, built‑in AI tools within existing platforms are more efficient than standalone systems, and smaller or more targeted models can deliver sufficient value at a much lower resource cost. Reducing unnecessary AI interactions is one of the fastest ways nonprofits can lower their cumulative footprint.
Staff guidance also matters. Clear expectations help teams understand when AI adds real value and when simpler approaches are preferable. Framing AI as a support tool rather than a default solution encourages more thoughtful—and more ethical—use.
Vendor selection plays a role as well. Major providers such as Google and Microsoft have publicly committed to becoming water‑positive by 2030, largely in response to growing scrutiny of data‑center impacts.
Nonprofits can reinforce these trends by asking vendors about energy sources, cooling strategies, and transparency commitments, and by favoring partners whose practices align with organizational values.
Values first, then technology choices
AI can bring real benefits to nonprofit organizations, but how it is used reflects who we are and what we stand for.
Our advice at Heller is to align internally on your values first, and then let those values guide how your team uses AI, which models you choose, and which vendors you partner with.
If your organization is developing an AI roadmap, refining governance policies, or evaluating new tools, Heller can help you navigate these tradeoffs and make practical, mission‑aligned decisions that balance innovation with responsibility.
Responsible AI is not about doing less. It is about doing the right things, deliberately.
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