Environmental sustainability is becoming a key focus for CEOs, boards and business leadership across functions. To deliver sustainability outcomes, executive leaders need sustainable technologies. Artificial intelligence (AI) is one such key technology. It can improve business outcomes and generate value for society, but it can also inhibit sustainability progress. The tradeoffs and opportunities of AI can be understood by combining two concepts: AI for sustainability and sustainability for AI.
AI for sustainability is defined as the capability of AI to reduce the overall operations impact as well as to improve business outcomes, including financial outcomes such as revenue growth and cost optimization, toward social, environmental and business goals.
Sustainability of AI entails balancing short-medium and long-term actions across the AI system’s life cycle to use AI responsibly and ultimately to minimize its impact on the environment while also addressing social and governance risks. The focus is to improve governance and reduce the environmental and/or social equity footprint of applying AI technologies in operations and business. Sustainable AI derives from the broader responsible AI framework. It is strictly bound to the idea that AI-enabled solutions should be designed and implemented in a human-centric and socially beneficial way.
By 2025, 50 per cent of CIOs will have performance metrics tied to the sustainability of the IT organization, according to Gartner. AI models and techniques can help drive a range of environmental goals such as:
- Monitoring and predicting climate and weather-change trends such as global warming
- Managing waste and optimizing recycling processes and operations
- Making transportation, mobility and routes more efficient to enhance fuel efficiencies and reduce carbon footprints
Executive leaders must consider how AI can be used responsibly, in a sustainable fashion, and still generate business value. Here are five ways to develop more sustainable AI:
No. 1: Make AI as efficient as the human brain
- Consider adopting so-called composite AI, which uses network structures to organize and learn similarly to the efficient human brain.
- Composite AI uses knowledge graphs, causal networks and other “symbolic” representations to solve a wider range of business problems in a more effective manner.
No. 2: Put your AI on a health regimen
- Monitor energy consumption during machine learning, and stop training AI as soon as improvements flatten out and no longer justify the costs of continuing.
- Keep data for model training local, but share improvements at a central level. This type of “federated machine learning” reduces electricity consumption and bolsters data privacy.
- Reuse models that have already been trained, and contextualize them, if necessary.
- Use more energy-efficient hardware and networking equipment.
No. 3: Run AI in the right place and at the right time
- Manage when and where the AI workload happens. The carbon intensity of local energy supplies varies by country, generating authority, time of day, weather conditions, transfer agreements, fuel supply and other factors.
- Balance follow-the-sun data center workloads, which are better for clean energy production, with unfollow-the-sun measures, which are better for water efficiency.
- Use energy-aware job scheduling, along with carbon tracking and forecasting services to reduce related emissions.
No. 4: Buy new clean power where you plan to consume it
- Procure power purchase agreements (PPAs) when possible, or source renewable energy certificates (RECs) that reduce or offset greenhouse gas emissions and add new renewable energy to the grid where your organization will consume electricity.
- Prepare for future protocols. PPAs and RECs aren’t perfect or always available, so start building a detailed plan of clean power by location, time of day or both. This type of analysis can help you build a clean-power strategy, which regulators may require going forward.
No. 5: Make environmental impact a key factor in considering AI use cases
- Model environmental impacts, as well as business benefits, as you build AI strategy, and move forward with use cases that create more value than they destroy.
- Reduce the risk and energy of existing AI initiatives before proceeding. Improve their energy efficiency and lessen intellectual property and proprietary data risk.
- Do not invest in AI use cases that could damage business value or the environment.
CIOs and other executive leaders seeking to make their AI initiative more environmentally sustainable while still maintaining business value must evaluate both the benefits and drawbacks of AI. Understand that the impact AI technologies have on human life and the planet is becoming increasingly critical. Adopt a sustainable AI-approach to allow leaders to minimize the negative consequences of AI as this will help reduce environmental and social risks as well as leverage AI to improve business outcomes and generate value based on sustainable principles.
Kristin Moyer is a Distinguished VP Analyst at Gartner where she advises CEOs and digital business leaders on sustainability and digital business transformation. Gartner analysts will provide additional analysis on sustainability and digital transformation at Gartner IT Symposium/Xpo, taking place this week, in Orlando, FL.