Why AI for Sustainability?

Just as IT has been the lynchpin of digital transformation, technologists will also be equally critical leaders in sustainable transformation.

Organizations are looking to IT personnel and artificial intelligence (AI) solutions to help achieve two key outcomes:

• To reach tech zero, or diminish the carbon footprint of an organization’s IT function by optimizing operations to reduce waste and inefficiencies in existing processes.
• To become tech positive, meaning using technology as a lever for the whole organization to reach its net zero goals and to drive new value and differentiation for a positive overall impact.

Measuring Organizational Progress Toward Sustainability

Environmental, social, and governance (ESG) data can help enterprises operate responsibly and contribute to a more sustainable future. The IT team can help gather related data, which enables investors, stakeholders, and ESG rating agencies to assess an organization’s performance relative to responsible sustainability practices and long-term value creation.

The following are factors that contribute to the measurement of a business’s sustainability impacts:

• Environmental: Carbon emissions, energy consumption, water usage, waste management, and efforts to reduce environmental footprint.
• Social: Commitment to diversity, labor practices, employee turnover rates, and impact on local communities.
• Governance: Board diversity and executive compensation, risk profile, long-term viability, accountability for ESG business practices, and compliance with regulations.

One appealing benefit for ESG-savvy organizations is that, as a company’s ESG score improves, its capital costs reduce, which, in turn, improves the company’s valuation.

Responsible AI

As with most emergent technologies, there are challenges to implementing and using AI. Decision-makers should be aware of data security and privacy risks, computational resourcing and expense, and ethical and societal implications. As a foundation for all uses of AI for sustainability, there must be a commitment to using responsible AI for transparency, accountability, inclusivity, and governance to support societal trust and ensure that advances in and use of AI continue to uplift communities.

Benefits of AI for Sustainability

AI can be used to benefit the environment, community, workforce, and a business’s bottom line. AI solutions can help:

• Measure, predict, and optimize complex systems: AI can unlock new opportunities for greater efficiency across the enterprise by analyzing available resources, usage patterns, and processes and providing recommendations for optimizations and improvements to increase operational and resource efficiency. Additionally, AI can support transparency around ESG goals and progress to help meet disclosure requirements, increase data-driven decision-making, and improve carbon calculation across the value chain, which can, in turn, help identify opportunities for improvement and adaptability.
• Accelerate the development of sustainability solutions: Enterprises can leverage AI to help drive innovation and develop new environmentally conscious products and services. By gathering and analyzing data on customer behavior and forecasting future trends, organizations can use AI to improve the efficiency of the product development process. By accelerating research and development with new, environmentally friendly materials, enterprises can expedite the development of more sustainable products.
• Respond and adjust to climate impacts: AI offers an unprecedented opportunity to accelerate innovation toward greater sustainability and mitigate the impacts of climate change. For example, AI for sustainability can help:

• • Increase climate resiliency through early warning systems for extreme weather events.
  • Tailor energy profiles based on local conditions to better address renewable energy intermittency and reduce dependence on fossil fuels.
  • Pinpoint and mitigate risks posed by climate change by analyzing complex climate data and patterns, taking the first step to incorporate climate science into business operations.

AI for Sustainability Use Cases

No matter where an enterprise is on the AI adoption and maturity journey, opportunities abound. AI for sustainability projects can be relatively small, like starting to apply next-logical-step digitization to existing workstreams to accelerate decision-making, or they can be groundbreaking and ambitious projects that will revolutionize executing on a chosen use case. With AI, organizations can meet their goals for more sustainable operations by optimizing processes and aiming to reduce energy consumption.

Industry-Specific Optimization

Near-real-time monitoring by AI can support reducing the carbon footprint in countless ways, including:

• Supply chain optimization: Apply automatic prediction and decision-making to optimize efficiency through demand forecasting, avoid inventory waste and unnecessary transportation or storage, and optimize container logistics—including last-mile delivery—to maximize goods transferred with minimal fuel expenditures.
• Product development and production optimization: Use physical and virtual automation to accelerate continuous process improvements across the organization using AI-enabled digital twins; assess, test, and optimize energy, materials, and design variables; and streamline manufacturing for minimal materials and resources waste.
• Transportation and logistics optimization: Improve transportation-related workstreams by optimizing routes, timing, and en route logistics with smart traffic management; support fuel supply and electric vehicle (EV) charging needs using predictive AI to enhance fuel efficiency and reduce environmental impact.
• Agriculture optimization: Use AI for environmental forecasting to enable precision agriculture, animal monitoring for the most effective use of resources for health and safety, and intelligent crop monitoring—combining data from satellite imagery, sensors, and weather data—to support informed decisions in agriculture.
• Energy grid optimization: AI in energy can help match system load and supply in near-real time to make power grids smarter, more efficient, and more stable; improve grid resilience by predicting outages and dispatching repair crews; and monitor for potential environmental hazards and predictive maintenance needs.
• Smart building optimization: AI-enabled systems can monitor energy usage in offices and warehouses and make adjustments for optimal energy efficiency; automate recycling; track equipment life cycles and predict maintenance needs to reduce waste; monitor air quality and other environmental factors and alert on levels that exceed limits; track regulatory changes and requirements that may affect operations, notify respective teams and departments, and provide recommended next steps.

Data Center Optimization

Data centers consume 10 to 50 times the energy per floor space of a typical commercial office building, which, collectively, means data centers account for approximately two percent of the total U.S. electricity use.¹

AI can help data centers increase energy efficiency and improve waste management by adopting best practices like liquid cooling, using renewable energy sources, reusing waste heat, and monitoring hardware for life cycle maintenance.

To maximize the impact of the resources spent on data center AI initiatives and to reduce the carbon footprint of compute infrastructure, a proactive approach to project design and IT management is critical.

AI Optimization

Unchecked, AI training and inference can be extremely carbon intensive, requiring large amounts of power, water, fossil fuels, and other resources. Developing and deploying AI models more sustainably means enterprises can reduce environmental impact by focusing on core actions like:

• Model optimization: optimizing models by using more efficient algorithms and making them lighter and faster reduces needed computational power, lowers training time, and extends the model’s lifespan, collectively reducing the energy and resources needed to run them.
• Software optimization: AI software can be used to increase AI model and system performance, helping to reduce overall computing power needs and energy consumption.
• Carbon-aware software: Software that enables workloads to run at times and in regions of lower carbon intensity or during off-hours can significantly reduce carbon emissions.

These strategies are core to reducing energy consumption with lower carbon intensity for AI workloads both adding up to significant financial savings for the businesses and contributing to sustainability goal commitments.

Business Device Sustainability

There are a range of general IT strategies for improving sustainability across the entire PC life cycle from procurement through device end of life.

New AI PCs, capable of running AI workloads locally, take sustainability into account by design, building in energy efficiency optimizations at the system and application layers. This gives IT and businesses another tool to accelerate innovation while reducing environmental impact.

The Future of AI for Sustainability

The landscape of sustainable AI adoption possibilities is exponential. In fact, PricewaterhouseCoopers estimates that the use of AI can reduce worldwide greenhouse gas (GHG) emissions by four percent by 2030.²

• AI is already being used to further all 17 UN Sustainable Development Goals (SDGs), including the goal of establishing sustainable cities and communities.³
• Chief Technology Officers (CTOs) are playing a greater role in helping their organizations meet increasing compute power demands while simultaneously helping support net zero goals.
• AI-powered predictive analytics can surface patterns and trends that may elude traditional analysis, which means ESG reporting can become more accurate over time. This data can help accelerate enterprises in planning future IT investments in alignment with their environmental responsibility commitments, as well as create new business value.

Where AI meets sustainability lies an opportunity to leverage technology to reduce waste, optimize resources, increase efficiency, and improve decision-making through near-real-time data analysis.