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IT Sustainability Think Tank: Rethinking tech management for the AI future
In today’s age, where digitalization, cost-efficiency, and environmental sustainability must be balanced, IT leaders are confronted with the challenge of keeping their organizations innovative while staying true to their sustainability goals.
The emergence of artificial intelligence (AI) technology brings both opportunities and challenges. AI can help monitor and reduce emissions, promote renewable energy, and improve waste recycling. However, the energy-intensive nature of AI systems can result in a significant carbon footprint.
Google’s recent announcement of a 50% increase in greenhouse gas emissions over five years due to datacenter expansion for AI products highlights the environmental impact of AI technology. With datacenter power demand expected to rise by 160% by the end of the decade, the sustainability implications of AI are becoming more apparent.
While manufacturers are working to reduce the environmental impact of AI-enabled hardware, the demand for rare raw materials for manufacturing poses challenges for the supply chain. Additionally, the financial cost of AI investments, forecasted to reach $200 billion globally by 2025, requires careful consideration.
Businesses must not only address the operational and financial implications of AI technology but also prioritize sustainability. It’s crucial to ask not just “what’s next?” but also “what next, sustainably?”
The AI revolution and hardware refresh
AI-powered computers are set to revolutionize business operations, with Canalys estimating 48 million AI-capable PCs shipping worldwide this year. Energy efficiency and embodied emissions are key considerations when upgrading to AI-capable hardware.
To ensure sustainability, IT leaders should prioritize environmentally-friendly hardware options and consider circular tech management models based on usage rather than ownership. These models offer financial flexibility, environmental benefits, and compliance advantages.
Adopting circular tech management allows businesses to scale AI capabilities based on demand, reduce financial burden, and maximize technology lifespan. It also ensures compliance with regulations on responsible tech resource management.
The data conundrum: you can only manage what you can measure
When selecting an AI strategy, IT leaders must evaluate the financial, operational, and sustainability benefits. Data security implications, especially with edge AI and on-premise models, require efficient tracking of data erasure to prevent breaches.
An end-to-end circular management model can provide a unified overview of IT assets throughout their lifecycle, ensuring efficient data erasure and compliance. This model minimizes the risk of lost devices and optimizes asset usage and refurbishing.
A smarter tomorrow
AI presents transformative opportunities for addressing global challenges, but concerns about privacy, energy consumption, algorithm bias, and job losses persist. While AI adoption may still be gradual, businesses must prepare to leverage its benefits while mitigating risks.
Partnering with technology service providers will be essential for maximizing AI benefits and minimizing risks. By focusing on core business objectives, tech leaders can navigate the complexities of AI integration and drive profitability and customer value.
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