AI SUSTAINABILITY
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“Additionally, AI can automate carbon footprint calculations and Scope 3 reporting, making ESG compliance more efficient and cost-effective. This approach ensures that while AI consumes energy, it also drives measurable sustainability improvements. “AI is already being used to replace guesswork with hard data, enabling businesses to identify energy-saving opportunities. Examples include optimising cooling systems in data centres and analysing supply chain data to select partners that meet sustainability standards. AI-driven dashboards can track performance against sustainability criteria, helping organisations reduce waste and energy use across the value chain.” Simon agrees that AI can create more efficient business processes that could in theory, offset the impact of increased AI data use. “AI has the potential to create better logistics, run machinery more efficiently and avoid unnecessary waste in manufacturing,” he adds. “Depending on the business, it could be game-changing to overall energy use.” Jason notes that in industrial settings, AI enables predictive maintenance, reducing downtime and waste. “In commercial buildings, AI can optimise HVAC and lighting systems in real time, driving down emissions,” he adds. “It can automate resource planning, identify energy wastage and improve operational efficiency across the business. By using flexible consumption models like Storage- as-a-Service, enterprises can align costs with actual usage, and this can ultimately cut total cost of ownership while also improving performance.
Adam adds that AI is particularly effective at identifying inefficiencies that humans might miss. “Whether that’s optimising workflows, reducing duplication, or improving timing and resource allocation,” he says. “In data-driven marketing, for example, smarter analysis can reduce wasted activity, unnecessary communications and redundant processing. Efficiency gains may not always be visible at an individual campaign level, but at scale they can meaningfully reduce energy and resource consumption.” Rupert Bull, CEO of www. thedisruptionhouse.com, says AI can help companies become more sustainable in many ways. “Examples include integrating fragmented logistics and supplier data to estimate emissions, filling gaps and acting as a radar of where issues may exist across complex supply chains,” he says. “It can speed up compliance and reporting – systems trained on the major frameworks and standards can map raw data to disclosure requirements and draft reports compliant with them to save hours of human effort.” Reseller role Resellers must be mindful of the implications – good and bad – of AI for sustainability, when selling tools to customers. Jad Jebara, CEO of Hyperview, says resellers have a role as connectors and trusted advisors for the demand and supply sides of the industry. “By acting in that fashion, the reseller channel can build a win-win-win situation,” he says. “Resellers can connect buyers with suppliers by understanding their requirements and the characteristics of the supply (deployed infrastructure) or by sourcing infrastructure and acting as trusted advisors and connectors. “Suppliers can connect with buyers through trusted resellers and distribution networks built in local markets. These networks utilise local experts who have an
Contributors
Simon Evans
wearepie.com
Rupert Bull
thedisruptionhouse.com
Jad Jebara
“Think of AI as a consumer and enabler, optimising supply chains,
hyperviewhq.com
energy management and sustainability reporting. But it starts with good data and better infrastructure; if the infrastructure and data pipelines are designed with sustainability in mind, AI can become a net positive for ESG performance.”
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