AI is an accelerator for sustainability — but it is not a silver bullet

The global community stands at a critical juncture. With 2023 recording the hottest temperatures to date, we are on the verge of surpassing the critical 1.5°C threshold above pre-industrial levels. The climate crisis is intensifying, and without swift, decisive action, we risk hitting irreversible tipping points.

Businesses must step up as key drivers of change. The convergence of Artificial Intelligence (AI) and sustainability presents a unique opportunity to accelerate climate action.

However, while AI is often hailed as a “silver bullet” for addressing climate change, it is important to understand its true potential – and its limitations. AI can help scale and expedite sustainability efforts, but like kryptonite, its energy demands could undermine its benefits if not managed carefully.

So, can AI propel us forward, or will its costs weigh us down?

While AI alone won't solve the climate crisis, it has the potential to dramatically expedite and scale sustainability efforts. Its strength lies in navigating and managing the complexities of systems — from global supply chains to power grids to the climate. These systems are deeply interconnected, and minor changes in one area can have widespread effects. With AI’s ability to analyze vast amounts of data from various fields, it can uncover hidden patterns, connections and inefficiencies that traditional automation might miss.

AI's advanced capabilities allow businesses to track changes, predict outcomes and enhance system behaviors in previously unthinkable ways. Consider AI trained on extensive datasets of material properties. These systems can swiftly analyze billions of possibilities to identify the best materials for creating sustainable products. This process is significantly faster than traditional methods. Similarly, AI-driven precision agriculture analyzes data from satellite imagery, weather forecasts and soil sensors to help farmers optimize the use of water, fertilizers and pesticides. This increases crop yields and reduces environmental impact by minimizing resource waste and overuse of chemicals.

In the energy sector, AI is revolutionizing decentralized energy systems through innovations in sustainability like virtual power plants, where home-installed batteries autonomously return power to the grid. AI optimizes this process, reducing strain on traditional power plants and improving energy efficiency. SAP’s AI-assisted solutions, for instance, have reduced fuel consumption and emissions in logistics by identifying the most efficient routes and schedules, minimizing both time on the road and fuel usage.

AI is not a free pass to sustainability, however. AI systems and the data centres that power them consume significant amounts of energy — more than traditional IT and cloud solutions. This paradox prompts a familiar conversation around return on investment (ROI). Imagine a machine that costs 30% more than its older counterpart. From a cost perspective alone, it seems like a poor investment. But if this new machine increases production efficiency by 45%, the ROI justifies the initial cost.

Similarly, although AI has a higher carbon footprint, its sustainability applications — like optimizing supply chains, managing energy consumption and enabling energy storage — can significantly reduce overall emissions. The key question is whether AI’s sustainability benefits outweigh its own energy demands. A significant part of integrating renewable energy into the grid depends on AI’s ability to optimize energy storage and facilitate energy transitions, ensuring we can decarbonize faster.

Moreover, AI is increasingly optimizing its own energy consumption. Advances in data centre energy efficiency and cloud service optimization are helping mitigate AI’s carbon footprint.

As industries rely more on AI to process large datasets and automate decisions, improving the efficiency of these systems will be crucial for balancing the equation.

SAP customers generate approximately 84% of total global commerce. Here are two key case studies from among them, demonstrating how to implement AI to accelerate sustainability impact and ensuring that the benefits outweigh the costs.

Ambipar, a global leader in environmental solutions operating in 40 countries, is setting a new standard for end-to-end carbon management and environmental stewardship. The company uses ERP-centric, AI-powered sustainability solutions to monitor and establish a common foundation of data across its global operations, while preparing for future growth opportunities. The integration of finance and sustainability data through advanced cloud solutions offers a holistic view of how environmental initiatives can either pose risks or add value to the business. AI plays a pivotal role in organizing and analyzing environmental impact data at Ambipar, enabling more targeted and effective sustainability initiatives. By training AI models with collected data and cross-referencing this with other environmental metrics, Ambipar anticipates sustainable outcomes that will resonate across multiple sectors, setting a benchmark for others in the industry.

msg global has implemented sustainability management technology to transition from an ad hoc to a structured sustainability approach that includes clear reporting and goal setting. With improved visibility into dependable data embedded across core business processes, the company can monitor progress of its sustainability strategy and make informed decisions in a role-based manner with real-time insights. The data transparency and actionable insights help msg global steer holistically toward its sustainability goals while building trust with stakeholders.

AI won’t single-handedly solve the climate crisis, but it offers a powerful tool to scale and expedite sustainability efforts, like in the case of Ambipar, msg global and many others. From managing complex supply chains to optimizing energy grids, AI enables businesses to measure, predict and optimize environmental impact in ways that traditional methods can't match.

However, maximizing AI’s potential and limiting its drawbacks requires collaboration across governments, industries and civil society. We need investments in AI research and data sharing to fuel innovation in sustainability and deliver real-world impact. At the same time, we must address the energy consumption challenges that come with AI. By focusing on optimizing data centers and improving AI efficiency, we can ensure that the sustainability benefits of AI outweigh its footprint.

AI offers the means to accelerate progress toward halving global emissions by 2030. The task is daunting but achievable; with AI as a catalyst for scalable, meaningful change, businesses can align economic growth with environmental stewardship.

Now is the time to act, leveraging AI’s power to create a future where sustainability and profitability go hand in hand.