AI is becoming increasingly used by many businesses, but it is power-hungry so how do businesses square that with their ESG goals – and how can resellers help with this?
The rapid adoption of AI by many businesses across the spectrum to carry out a range of tasks and functions is anticipated to continue this year. While AI can bring benefits to those that use it, AI can also bring challenges – not least to sustainability targets.
Jason Beckett, head of technical sales EMEA at Hitachi Vantara, says the demands of AI are rapidly outpacing traditional IT infrastructure. “AI workloads, particularly those running on GPU-intensive platforms or training large language models, require substantial computing power,” he adds. “This often translates into higher energy usage, increased cooling needs, and a greater carbon footprint especially when deployed without optimising for energy efficiency.
“These pressures are already under scrutiny from regulators, investors and sustainability-conscious customers. This focus will only intensify. Many legacy systems weren’t built to handle AI at scale, particularly the associated cooling demands.
“If left unaddressed, this creates real risk of grid overload and unsustainable operational costs. To stay ahead of these challenges, businesses must rethink their infrastructure strategies now. Innovation depends on agility and scalability: if your infrastructure can’t adapt quickly, every new initiative becomes a bottleneck. By designing for simplicity, security, and sustainability, organisations can pivot to new technologies like AI without major rework.”
For many businesses, the challenge isn’t just the initial training of models, but the ongoing cost of inference – systems running continuously in the background, adds Adam Herbert, Go Live data CEO. “What’s often underestimated is how quickly these demands scale when AI is deployed without clear constraints or purpose,” he says.
Richard Eglon, CMO Nebula Global Services, agrees that AI is inherently power-hungry. “This creates an ironic challenge: businesses are using energy-intensive technology to meet environmental goals,” he says. “Data centres, for example, need to manage cooling systems and server configurations carefully because every operational change impacts energy use and carbon output.”
Simon Evans, lead consultant and sustainability director at PIE Factory, notes that the carbon produced by processing AI data requests falls under scope 3 emissions. “So, businesses have a responsibility to manage them,” he adds. “This was always true of businesses using cloud software, but AI has dramatically increased the energy use involved compared with standard cloud computing. For businesses, this creates risk and risk that is difficult to predict with any level of accuracy.”
Squaring with ESG goals
With AI use increasing, and the carbon produced by it falling under scope 3 emissions, this poses a potential problem for businesses that want to use AI but are also mindful of their sustainability targets.
Simon says businesses square AI use with their ESG goals with great difficulty. “This is a nascent technology, so businesses can only make educated guesses about future energy demands for AI data processing,” he says. “ESG goals set before these assumptions are made are likely to need to be significantly adjusted. Water use is also a large-scale impact area that responsible businesses should be considering in their (excuse the pun) upstream processes.”
AI should be treated like any other capital asset, Jason adds. “Make sure you set usage budgets and get on top of how you track compute emissions: deploy AI only where there’s a proven productivity or efficiency return,” he says.
Businesses must take a long-term, architecture-first approach, he adds. “The most essential consideration is matching innovation with environmental management and long-term planning,”
he says.
“Businesses can reduce AI’s impact through energy-optimised infrastructure, such as platforms that avoid unnecessary data duplication and support modular scaling of compute and storage. Green software practices, zero-copy architectures and flexible scaling help reduce waste across the AI pipeline.
“Traditional scaling often means adding more hardware, more power and more cost. Sustainable scalability is about growing intelligently using energy-efficient systems, optimising resource utilisation, and supporting future workloads without constant forklift upgrades. It’s about aligning IT growth with environmental and financial responsibility.”
Adam says the starting point must be intentionality. “ESG and AI don’t have to be in conflict, but AI cannot be deployed indiscriminately,” he warns. “Businesses need to be clear on where AI genuinely adds value and where it simply adds complexity. From an ESG perspective, that means favouring targeted, use-case-driven AI over broad, experimental deployments, and being transparent about energy usage, data sources and outcomes. Responsible governance matters as much as the technology itself.”
Improving efficiency
Richard says businesses can help to reconcile ESG worries by using AI to optimise their own operations for sustainability. “For instance, AI can model the energy impact of different configurations in data centres, helping firms make informed decisions that reduce carbon output,” he says.
“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.
“Think of AI as a consumer and enabler, optimising supply chains, 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.”
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 established relationship with the buyers.”
Adam notes that resellers have a responsibility to move the conversation beyond ‘what’s possible’ to ‘what’s appropriate’. “That means helping customers understand the real cost – environmental as well as financial – of AI adoption,” he says. “Advice should focus on proportionality: choosing tools that match the problem, setting clear success criteria, and avoiding AI for AI’s sake. Resellers should also be asking harder questions around data quality, governance and long-term sustainability, not just performance gains.”
Jason says that to support ESG outcomes, resellers need to go beyond basic provisioning and lead the conversation around sustainable AI readiness. “This means educating clients on how to select scalable, modular infrastructure, reduce cooling inefficiencies, and build toward compliance with future regulation,” he says. “Above all, resellers must discourage retrofitting new AI workloads onto legacy systems. The cost may be invisible at first, but it grows quickly in energy waste, carbon emissions, and operational inefficiency. As ESG becomes a board-level priority, resellers that can link infrastructure strategy with sustainability outcomes will gain a competitive edge.”
Richard agrees that resellers can help to educate customers on AI tools for emissions tracking and Scope 3 reporting. “They can also promote collaboration between vendors for supply chain transparency,” he adds.
Resellers should also highlight ROI, he adds. “Show how AI can deliver predictable costs and help smaller businesses compete for ESG-compliant RFPs.”
Growing issue
Adam says that as AI becomes more embedded in everyday business operations, scrutiny will increase from regulators, investors, customers and employees alike. “ESG considerations won’t sit alongside AI strategy; they’ll become part of how AI decisions are evaluated in the first place,” he says. “Businesses that treat sustainability as a core design principle, rather than an afterthought, will be better placed to scale AI responsibly and retain trust over the long-term.
“Ultimately, the question isn’t whether businesses should use AI, but how thoughtfully they deploy it. The same principles that underpin responsible ESG strategies, encompassing restraint, transparency and long-term thinking, must also guide AI adoption.
“In data-driven sectors like marketing, this means using intelligence to reduce waste, instead of creating more; to respect audiences, instead of making them feel overwhelmed, and to design systems that are efficient. As AI continues to mature, the organisations that succeed will be those that treat sustainability and ethics as a stride towards innovation, as the forming framework that makes it sustainable from the offset.”
Simon adds that we are at the start of a revolution in businesses taking accountability for AI’s energy use. “Increased mandatory reporting requirements on scope 3 emissions will bring the large scale energy use of AI into the spotlight,” he says. “Those benefiting from potentially increased profits aided by AI technology will ultimately have to be accountable for the environmental impacts caused.”
Richard says that balancing AI’s energy demands with ESG commitments will become a central conversation across the tech channel. “The industry is optimistic but realistic – AI and ESG integration is still in early stages, but the potential for transformative impact is clear,” he says. “Over time, this will evolve from a niche discussion to a mainstream priority for vendors, partners, resellers and end clients alike.”
