Can UK businesses balance AI ambitions with sustainability obligations?

Date:
Thu, 05 Jun 2025 14:05:33 +0000

Description:
AI initiatives must be built on a foundation of energy efficiency and sustainability.

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As the various deadlines for net-zero approach, sustainability initiatives
are climbing the boardroom agenda. In fact, two-thirds of businesses are expected to boost sustainability spending in 2025.

Cutting emissions will require a concerted effort from every department, and IT is an increasingly important voice in this conversation. Data storage , in particular, is emerging as a central element of an organization's efforts to decrease their carbon footprint. This is because a huge amount of data currently stored by UK businesses counts as single-use data. These are files, email conversations and other data that is generated but never used again despite being stored where it continues to use energy and budget.

Recent NetApp research found that the overwhelming majority of IT leaders (92%) are aware of the environmental and financial impact of single-use data. The challenge is that AI famously data and energy hungry is a competitive advantage that businesses are unwilling to leave on the table.

As a result, organizations up and down the nation are facing a conundrum between the commercial and productivity benefits of AI, and staying on track with their sustainability progress to net-zero. So, how can businesses solve this challenge? The invisible problem of data

When it comes to pollution, the environmental impact is often tangible and plain to see. Think of overflowing landfills and dirty rivers. But when the landfill resides in cyberspace, its mostly invisible and therefore easy to miss.

Single-use data might seem harmless, but the carbon footprint of unused data is a challenge that must be addressed; especially in our burgeoning AI age when 38% of data stored in the UK never gets accessed again after creation. This remains a substantial challenge for several reasons.

Firstly, around a third of IT leaders struggle to identify which data is single-use and which may be useful for future projects or training AI. On top of this, many are also feeling overwhelmed by the amount of accumulated data already, as sifting through stored archives requires more resource and budget than IT departments have available.

But challenging as this exercise may be, it is an important one. Not only because it will help cut emissions and save costs by reducing how much data businesses store, but an audit like this can also be massively beneficial in helping organizations get their house in order for AI. Managing AIs data appetite

It was, and still is, inevitable that AI will prompt an exponential rise in the data generated and stored. It is estimated that businesses will see their data estates grow by an average of 41% percent due to AI projects. For businesses that are already storing decades of data, this growth will be
huge.

Its easy to imagine a scenario of businesses hoarding data and plugging it
all into AI tools as part of their implementation projects. Single-use data could become rife, costing businesses thousands as well as resulting in needless carbon emissions. Data management will be instrumental in avoiding this scenario. After all, the quality of the data used for AI is perhaps the most important factor in determining the success of AI initiatives.

Businesses must therefore ensure that their data is high quality, relevant, and useful before they begin their AI journey. Proper data infrastructure not only sets up AI projects for success, but it can help businesses stay on top of their emissions by enabling a strategic, deliberate and carefully considered approach to data growth. Making storage more sustainable for AI

There are several approaches businesses can take to make their data storage more sustainable, even as AI implementation accelerates. Data lifecycle management policies are a fantastic place to start, which can include automated tiering and archiving of less frequently accessed datasets to more energy-efficient storage solutions.

This significantly reduces energy consumption. Elsewhere, data compression algorithms and deduplication efforts can help too. By reducing the physical storage footprint, businesses can reduce emissions by lowering the associated energy demands for both storage and retrieval.

Companies can reduce emissions by reforming their physical infrastructure
too. For example, they can opt for energy-efficient data centers, choosing those with the latest cooling technologies that minimize power consumption
and use renewable energy sources. Optimizing data center locations to take advantage of cooler climates or readily available green energy can also significantly reduce the carbon footprint.

Finally, businesses should not overlook the importance of responsible
hardware lifecycle management. The reuse, refurbishment, and proper recycling of storage components is crucial to minimize electronic waste, further contributing to sustainability objectives. Finding balance in a complex landscape

Ultimately, the convergence of net-zero targets and the promise of AI puts businesses at a critical juncture. The unchecked accumulation of data for AIs sake risks undermining hard-won sustainability progress.

To navigate this challenge successfully, UK businesses need to find their own balance between innovation and responsibility. This begins with a commitment to data hygiene regularly reviewing what is stored and why it is being kept all while exploring various infrastructure options that align with
sustainable practices.

Different organizations will require different approaches depending on their sector, size, and AI goals. What is clear is that the foundation upon which
AI initiatives are built must incorporate energy efficiency and
sustainability from the beginning. By taking a thoughtful, measured approach, businesses can harness the transformative power of AI while contributing towards a sustainable digital future.

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