In an era when digitalisation, cost-efficiency and environmental sustainability must go hand in hand, IT leaders face the critical challenge of ensuring their organisations can remain at the forefront of innovation without compromising their corporate sustainability agendas.
In this landscape, the advent of artificial intelligence (AI) technology represents a double-edged sword.
On the one hand, AI can undoubtedly unlock important environmental opportunities by helping monitor and reduce emissions, advance renewable energy, and even recycle more waste.
On the other, however, AI systems can have a significant carbon footprint due to the amount of energy required to train and operate them.
Google recently announced that its greenhouse gas emissions (GHG) had surged by almost 50% in the past five years due to the expansion of its datacentres, which underpin its AI products.
And we are just at the start of the journey. According to Goldman Sachs, datacentre power demand is poised to grow 160% by the end of the decade.
On the hardware front, even though many manufacturers are actively working to reduce their products’ environmental footprint, new AI-enabled PCs and laptops will still need rare and critical raw materials to be manufactured, putting additional pressure on an already overburdened supply chain. There are also political implications linked to this, as “90% of the world’s most advanced processors can only be made by one company in one country”.
To complicate matters even further, the AI revolution comes at a financial cost that needs to be carefully balanced: Goldman Sachs forecasts that AI investments will approach $200 billion globally by 2025 and “they will probably happen before adoption and efficiency gains start driving major gains in productivity.”
It’s therefore clear that businesses must start addressing not just the operational impact of the AI technology shift but also its impending environmental and financial ramifications, and ask themselves not just “what’s next?” but also “what next, sustainably?”
The AI revolution and hardware refresh
Despite relatively slow demand for the time being, the rise of AI-powered computers is poised to transform business operations over the next few years, driving many organisations to upgrade to these advanced devices to maintain a competitive edge.
Canalys estimates that 48 million AI-capable PCs will ship worldwide this year, and Gartner predicts they will make 22% of total PC shipments in 2024 alone.
With embodied emissions (the ones generated during the production of technology assets) potentially accounting for up to 50% of a device’s carbon footprint and energy efficiency being one of the main concerns related to AI processing, it’s important for IT leaders to consider the sustainability credentials of new AI-capable hardware as they upgrade.
To take advantage of the more environmentally-friendly options available in the market, businesses should prioritise IT sourcing solutions that provide complete flexibility in supplier selection. This can ensure their hardware choices align with their broader organisational requirements and ESG objectives from the moment of procurement.
Upgrading devices can also come with a hefty cost premium. These costs are also compounded by the rapid pace of advancements in AI: as new, more powerful AI hardware emerges, current assets will quickly become obsolete, leading to accelerated depreciation and reduced return on investment.
To navigate these challenges, businesses should also consider adopting a circular tech management model based on use rather than ownership, which offers several key advantages.
From a financial perspective, instead of a substantial upfront investment in AI-capable hardware, businesses can spread their expenditures over time through subscription fees. This allows for better cash flow management and reduces the financial burden associated with large capital expenditures. In these circular models, moreover, the residual value of the technology is often factored in at the procurement stage, resulting in significant cost savings.
Circular tech management models are also powerful from an environmental point of view and can help with compliance as regulations come into force mandating businesses to ensure responsible management of all their tech resources. That’s because these models are based on the ability of the provider to take back the assets at the end of the usage cycle, and they offer businesses the reassurance that their technology’s full potential lifespan will be maximised with a secondary user or that their assets will be responsibly recycled.
Finally, another critical advantage of these models is that they provide businesses with the flexibility to scale their AI capabilities based on demand. This ensures that companies can respond quickly to market changes and technological advancements without being locked into outdated, quickly depreciating hardware. And by agreeing upfront on the length of the contract, organisations can bring predictability into their digital investments.
The data conundrum: you can only manage what you can measure
Evaluating the financial, operational and sustainability benefits of any IT solution will undoubtedly be essential when selecting the right AI strategy.
However, CIOs will also need to consider hardware-related data security implications.
Edge AI and on-premise models, for instance, involve processing and storing data locally on devices, servers and datacentres rather than in the cloud. According to Forrester Research, only 7% of security decision-makers are concerned that a lost or stolen asset could cause a breach.
However, such incidents account for 17% of all breaches, making efficient and effective tracking of data erasure at the end of the device lifecycle a crucial compliance step, even more so as the amount of data on devices increases.
Here, again, an end-to-end circular management model that encompasses the entire device lifecycle and allows organisations to gain a single, unified overview of their IT asset from the moment they are procured to decommissioning and refurbishing (or recycling) ticks many boxes.
Following devices as they are sourced, assigned, and used minimises the risk of lost devices and ensures efficient data erasure at the end of the first lifecycle can be performed; it can furthermore provide operational data and insights that ensure assets are being used efficiently, renewed and refurbished at the optimal time.
A smarter tomorrow
It is not possible to predict all the ways that AI will change the world.
It certainly represents a transformative opportunity to address many of the challenges businesses, governments, and individuals face across the globe.
However, that’s only half the story.
Considerations related to privacy and misuse of data, energy requirements, algorithm bias, and potential job losses will undoubtedly continue to dominate the conversation for years to come.
AI-powered PCs and smartphone adoption are also still some time away, as “the real benefits of owning such a device are not yet clear to most buyers”, according to Gartner.
However, this cautious approach shouldn’t be mistaken for a lack of interest. The most advanced and customer-centric businesses know that AI integration will be complex, but it’s also inevitable, as the potential benefits of AI are simply too significant to ignore.
In this landscape, businesses must ensure that, when the right time comes, they are ready to take advantage of the opportunities offered by AI and prepared to mitigate some of the challenges that come with this digital revolution.
Partnering with the right technology service providers will become more important than ever before. This will allow tech leaders to focus on their core business and ensure the benefits of AI in terms of profitability and customer output can be maximised while minimising potential risks.