The world is experiencing an unprecedented data explosion, fueled by innovations in AI, cloud computing, and the proliferation of connected devices. As organizations race to harness this data for competitive advantage, a new and urgent challenge is emerging – how to store, manage, and analyze massive data volumes in a way that is both economically viable and environmentally responsible.
We recently released our “Decarbonizing Data: Achieving Sustainability in the Age of AI” report, exploring how enterprises are navigating the dual priorities of cost efficiency and carbon reduction. AI is having a transformative impact on data infrastructure, and businesses need solutions that don’t sacrifice sustainability at the cost of innovation.
To thrive in the AI era, organizations must therefore balance the need for rapid data-driven innovation with the necessity to decarbonize their operations. As well as being an environmental issue, it is a business imperative that will shape competitive dynamics for years to come.
Senior Vice-President for Business and Markets at Seagate Technology.
The opportunities and challenges with the data explosion
The digitization of nearly every aspect of our lives has led to a data explosion. IDC predicts that the Global Datasphere will grow from 45 Zettabytes (ZB) in 2019 to 175ZB by 2025. This is an astronomical figure that reflects the sheer scale of today’s digital world. This data is the lifeblood of AI, powering everything from predictive analytics in healthcare to real-time fraud detection in financial services.
But as the datasphere expands, so does its environmental footprint. Data centers, which store and process this information, already account for about 1% of global electricity consumption. Without intervention, this figure could rise dramatically as more organizations deploy AI tools that are particularly data- and compute-intensive.
What’s more, the environmental costs are not limited to energy consumption alone. The manufacture, transportation, and eventual disposal of storage devices all contribute to an organization’s carbon footprint. E-waste is a growing concern worldwide, as vast quantities of outdated hardware are discarded each year.
Addressing the full lifecycle of IT infrastructure, from responsible sourcing of materials to ethical recycling, will be essential for organizations seeking to minimize their environmental impact while still supporting the growth and innovation that AI demands.
Nearly 95% of respondents are concerned about environmental impact, but only 3.3% prioritize it in purchasing decisions. This presents a complex challenge – how can organizations meet their growing appetite for data, while also achieving ambitious sustainability goals?
Balancing innovation with responsibility
The rapid pace of AI-driven innovation presents both tremendous opportunities and significant risks. On one hand, AI has the potential to unlock new business models, accelerate scientific discovery, and solve some of society’s most pressing challenges, from climate change to healthcare. On the other hand, if not managed responsibly, the infrastructure demands of AI could exacerbate global carbon emissions and undermine collective efforts to achieve net-zero goals.
Business leaders must recognize that sustainability is no longer a peripheral concern, it is central to long-term value creation. Customers, investors, and regulators are increasingly scrutinizing the environmental credentials of technology providers. Organizations that fail to decarbonize their data infrastructure risk falling behind in an era where “green” is the new gold standard.
Driving innovation and infrastructure transformation with AI
AI is both a driver of data growth and a catalyst for infrastructure transformation. As AI models become larger and more sophisticated, they require exponentially more data to train and operate effectively. According to our recent industry survey, 74% of IT leaders believe that AI will fundamentally change their infrastructure requirements within the next two years.
AI workloads are unique in their demands. They require not only vast amounts of raw storage, but also rapid data access and high-throughput processing. Traditional data center architectures (designed primarily for general-purpose computing) are often ill-suited to these needs. As a result, organizations are rethinking everything from storage media selection to data center design.
However, this shift is not without its challenges. The increased power and cooling requirements of AI infrastructure can lead to higher operational costs and greater carbon emissions. According to the International Energy Agency (IEA), training a single large AI model can consume as much electricity as 100 American homes use in a year. As AI adoption accelerates, the imperative to dissociate data innovation from carbon emissions becomes ever more pressing.
The cost-carbon conundrum
For decades, the primary driver of infrastructure investment was total cost of ownership (TCO). Enterprises sought to maximize performance while minimizing capital and operational expenses. Today, a new metric is rising in importance – total carbon ownership (TCO2). Increasingly, organizations are being asked not only to account for the financial cost of their infrastructure, but also its environmental impact.
Our research shows that 66% of IT decision-makers now consider carbon footprint as a critical factor in their infrastructure buying decisions. This shift is being driven by a combination of regulatory pressure, investor expectations, and customer demand for sustainable products and services. Governments across the globe are enacting stricter emissions standards and sustainability requirements for technology providers, while investors and clients are demanding greater transparency and accountability throughout the value chain.
As these pressures intensify, organizations that have already integrated sustainability into their core strategy will be better positioned to meet evolving requirements, avoid compliance risks, and maintain the trust of their stakeholders. Anticipating these changes and proactively investing in sustainable practices today can yield significant competitive advantages tomorrow.
Yet, achieving the right balance between cost and carbon is no simple task. There are often trade-offs between the two, especially as organizations seek to deploy the latest AI technologies. High-performance infrastructure can deliver faster insights and greater business value but may consume more energy and generate higher emissions if not carefully managed.
Decarbonizing data – A roadmap for sustainable innovation
The good news is that solutions are emerging to address this challenge. One powerful method is embracing circularity within storage solutions. By extending the life of storage devices, reusing components, and recycling valuable materials, companies can significantly reduce carbon emissions associated with manufacturing and disposal.
Selecting energy-efficient technologies also plays a crucial role in this roadmap. Not all storage media are created equal; modern hard disk drives and solid-state drives have achieved notable improvements in energy efficiency. By carefully choosing the optimal mix of storage technologies based on specific workload requirements, organizations can achieve substantial reductions in energy consumption without sacrificing performance.
Another critical strategy involves intelligent data management and tiering. Not all data needs to reside in high-performance, energy-intensive environments. By implementing effective data management policies, businesses can move less-frequently accessed data to lower-cost, lower-carbon storage tiers. This conserves resources and ensures mission-critical workloads receive the performance and capacity they require.
The shift to renewable energy and innovative cooling
The shift to renewable energy and innovative cooling solutions further supports decarbonization. Powering data centers with renewable energy sources and adopting advanced cooling technologies, such as liquid cooling and heat reuse, can dramatically reduce operational emissions. Some of the major hyperscale operators have already set ambitious targets for sourcing 100% of their electricity from renewables, signaling the direction the industry must follow.
Robust measurement and optimization are foundational to any successful sustainability effort. Organizations need to invest in tools and processes that enable real-time monitoring of their data infrastructure’s carbon footprint. Armed with accurate and actionable data, they can pursue continuous improvement, driving their operations toward ever-greater efficiency and lower emissions.
Finally, no single organization can decarbonize the data ecosystem alone. Achieving meaningful progress will require unprecedented collaboration across the technology value chain, from component manufacturers to cloud service providers to end users. Working together, businesses and wider society can build a digital future that is prosperous, resilient, and sustainable for all.
Taken together, these strategies provide a roadmap for organizations to decarbonize their data infrastructure. By embedding sustainability at every level of data operations, businesses can foster innovation, reduce costs, and accelerate progress toward a greener, more resilient digital future.
Building the sustainable data infrastructure of the future
The way forward is clear. To realize the full potential of AI and digital transformation, organizations must build data infrastructure that is not only cost effective and high performing, but also sustainable. This requires a fundamental shift in mindset, from viewing sustainability as a compliance obligation, to embracing it as a source of innovation and competitive advantage.
The era of AI-driven data innovation is here, and it is transforming the way we live, work, and interact with the world. But with great power comes great responsibility. As stewards of the digital future, we must ensure that our pursuit of progress does not come at the expense of our planet.
Balancing cost and carbon is not a zero-sum game. With the right strategies, technologies, and partnerships, organizations can achieve both economic and environmental gains. The decarbonization of data infrastructure is not just the right thing to do, it’s the smart thing to do.
The choices we make today will determine the legacy we leave for future generations. Building data infrastructure that enables AI innovation, drives business value, and safeguards the planet we all share is imperative.
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