In today’s world, where digital transformation, cost-effectiveness, and environmental responsibility must work in tandem, IT leaders are tasked with the crucial responsibility of keeping their organizations at the forefront of innovation while adhering to sustainability goals.
In this context, the rapid rise of artificial intelligence (AI) technology presents both opportunities and challenges. On the positive side, AI can drive significant environmental advancements by aiding in emissions monitoring, promoting renewable energy initiatives, and enhancing recycling efforts. However, AI systems also come with a considerable carbon footprint due to the substantial energy required for their training and operation. For instance, Google recently reported a nearly 50% increase in greenhouse gas emissions over the past five years, largely attributed to the growth of its data centers that support AI services. Projections from Goldman Sachs suggest that the power demand for data centers could rise by 160% by the end of the decade.
While hardware manufacturers are actively working to minimize the environmental impact of their products, new AI-enabled devices will still necessitate rare and essential raw materials for production, further straining an already stressed supply chain. This situation is complicated by geopolitical factors, as a single company in one country currently produces 90% of the world’s most advanced processors.
Moreover, the financial implications of the AI surge are significant and require careful consideration. Goldman Sachs estimates that global investments in AI could reach nearly $200 billion by 2025, likely occurring before the expected productivity gains from adoption and efficiency improvements materialize. Therefore, organizations must not only consider the operational effects of the AI shift but also its environmental and financial impact, prompting them to ask not just “what’s next?” but “what’s next in a sustainable manner?”
The AI Revolution and Hardware Upgrade
Despite currently modest demand, the emergence of AI-enabled computers is set to revolutionize business operations in the coming years, prompting many organizations to upgrade in order to stay competitive. Canalys projects that 48 million AI-capable PCs will be shipped globally this year, with Gartner forecasting they will represent 22% of total PC shipments by 2024.
Given that embodied emissions—those produced during the creation of technology—can account for up to 50% of a device’s overall carbon footprint, it is essential for IT leaders to prioritize sustainable practices when upgrading to new AI-capable hardware. Companies should seek IT sourcing solutions that allow for flexible supplier selection, ensuring that their hardware investments align with organizational goals and environmental, social, and governance (ESG) objectives from the outset.
Upgrading to new devices often entails a significant cost, and the rapid evolution of AI technology can lead to quicker obsolescence of current assets, resulting in accelerated depreciation and diminished return on investment. To address these challenges, businesses might consider a circular tech management model that emphasizes use over ownership, providing several key advantages.
Financially, this approach allows organizations to spread the cost of AI-capable hardware over time through subscription payments, enhancing cash flow management and alleviating the financial strain of large capital expenses. Furthermore, circular models often account for the residual value of technology during procurement, leading to cost savings.
From an environmental standpoint, these models align well with emerging regulations aimed at responsible technology management. Providers can reclaim assets at the end of their lifecycle, ensuring that technology is either refurbished for secondary use or recycled responsibly.
These models also offer scalability in AI capabilities according to demand, allowing organizations to respond swiftly to market fluctuations and technological developments without being tied to outdated, rapidly depreciating hardware. By defining contract lengths upfront, companies can bring predictability to their digital investments.
The Data Dilemma: Managing What You Can Measure
Assessing the financial, operational, and sustainability benefits of different IT solutions is crucial when developing an effective AI strategy. However, CIOs must also consider the data security implications associated with hardware.
For example, edge AI and on-premise models involve local data processing rather than relying on the cloud. Research indicates that while only 7% of security decision-makers express concern over lost or stolen assets potentially leading to breaches, such incidents make up 17% of all breaches. Therefore, effective tracking of data erasure during the device lifecycle is a vital compliance measure, especially as data volumes grow.
An end-to-end circular management model that oversees the entire device lifecycle enables organizations to maintain a comprehensive view of their IT assets from procurement to decommissioning and recycling. Monitoring devices from sourcing through usage minimizes loss risks and facilitates efficient data erasure at the end of their initial lifecycle. This model also provides valuable operational insights that can help ensure assets are utilized efficiently and refurbished at the optimal time.
A Smarter Future
While it’s impossible to foresee all the ways AI will reshape our world, it undeniably presents a transformative opportunity to tackle numerous challenges faced by businesses, governments, and society. Yet, this potential comes with complications, including data privacy concerns, energy demands, algorithmic bias, and potential job displacement, all of which will remain focal points of discussion for years ahead.
Widespread adoption of AI-powered PCs and smartphones may take time, as Gartner notes that the tangible benefits of these devices are not yet evident to many buyers. However, this cautious outlook shouldn’t be misinterpreted as disinterest. Forward-thinking businesses recognize that while integrating AI is complex, it is also unavoidable, given the substantial advantages it presents.
In this evolving landscape, it is crucial for organizations to be prepared to leverage AI’s opportunities when the moment arises, while proactively addressing the challenges this digital transformation poses. Collaborating with the right technology service providers is more important than ever, as it allows tech leaders to focus on core business objectives and maximize the benefits of AI for profitability and customer engagement, all while minimizing associated risks.