AI and the Environment: Balancing Strategy and Culture in Your AI Approach

By: Zayn Moosa

As artificial intelligence becomes more deeply embedded in healthcare and other sectors, it’s important to consider not just its capabilities but also its environmental footprint. Training and running large AI models require vast computing power, which in turn consumes significant energy and water resources. For instance, a recent MIT study found that training a single model such as GPT-3, which is used in healthcare and other applications, can require hundreds of thousands of litres of water to cool the data centers that support it. 

By 2027, AI development initiatives could use billions of cubic meters of freshwater globally. These findings reveal two important dynamics. On one hand, there is a strong push to harness the power of AI across sectors like healthcare. On the other hand, they highlight growing concerns about the environmental cost of that progress.

For organizations, these developments are prompting new questions, not just about whether they are equipped to adopt and manage AI effectively, but also about how to plan for the long term, balancing technological advancement with environmental responsibility and sustainability. As AI becomes a more central part of how organizations grow, compete, and deliver on their purpose, it becomes essential to align strategic intent with values, shaping how decisions are made.

AI offers a wide spectrum of capabilities. It can streamline operations, enhance forecasting, reduce waste, and support emissions monitoring. Yet these capabilities come with costs: energy to train large models, water for cooling data centres, and rare materials for hardware components.

How organizations respond to this tension often comes down to strategic priorities and culture. To illustrate, consider these two hypothetical but realistic scenarios, each reflecting a different approach to AI usage and implementation.

Scenario One: The Fast Mover

Context: This organization sees AI as core to advancing their strategic goals. Their adoption agenda is ambitious. Efficiency, automation, and scale are top of mind.

Cultural markers:

• Leadership rewards speed and experimentation
• AI projects are championed and funded quickly
• Environmental impact is acknowledged but rarely quantified

Outcomes:

• Implementation advances quickly
• Oversight of environmental impacts remains limited
• After rollout, teams discover that energy and water use is significantly higher than expected

In this scenario, AI adoption aligns with a growth-oriented strategy; however, resource implications emerge later, leading to internal tension or reputational risk.

Scenario Two: The Reflective Starter

Context: This organization is intrigued by AI’s potential but commits to deliberate inquiry. Adoption is thoughtful, cautious, and phased.

Cultural markers:

• Early questions are asked about energy, water, and supplier practices
• AI adoption requires a cross-functional review
• Teams are aware of national efforts such as the Canada AI Safety Institute and proposed investments in green data infrastructure.

Outcomes:

• Trade-offs are surfaced early and documented transparently
• Environmental criteria are integrated into infrastructure and vendor selection
• Slower timelines lead to missed early efficiencies and reduced speed to competitive advantage

In this scenario, the organization moves forward with AI in a way that reflects its values and manages risk effectively, but the cautious pace creates opportunity costs, such as slower innovation, delayed efficiency gains, or falling behind more aggressive competitors.

What This Means for You

Neither approach is right nor wrong. But the decisions made at the outset shape the outcome. 

Choosing to adopt AI is a strategic decision. How that decision plays out, how fast it moves, what trade-offs are considered, and who is involved, is deeply influenced by culture.

When strategy and culture are aligned, organizations are better equipped to weigh AI’s potential against its risks and to move forward with clarity. When misaligned, even well-intentioned efforts can become fragmented, reactive, or difficult to sustain.

Five Ways to Align Your AI Strategy with Organizational Culture and Sustainability Goals

1. Ask early, clarifying questions.
   Identify who is responsible for tracking energy and resource use, and what expectations exist for vendors.
2. Create cross-functional checkpoints
   Involve voices from IT, sustainability, operations, and procurement before finalizing AI adoption decisions.
3. Pilot with transparency
   Document assumptions and trade-offs early, and share updates as the implementation progresses.
4. Stay connected to policy developments.
   Monitor public-sector signals such as Canada’s responsible AI strategy and proposed funding for green infrastructure.
5. Frame AI adoption through a values lens
   Ensure the conversation is not just about performance or productivity, but about what matters most to your organization.

AI will continue to offer powerful tools for progress, but its environmental implications are real and growing. As organizations explore AI as part of their long-term strategy, alignment with culture becomes critical to making responsible, values-aligned decisions that hold up over time.

At MacPhie, we help organizations align culture, values, and strategy during moments of change, including the introduction of emerging technologies like AI. If your team is navigating how to integrate AI in a way that reflects your strategic priorities and how you work, we would be glad to support the conversation.