AI


actionable insights from ever-growing datasets—all without significant disruption. Tis forward-thinking approach empowers enterprises to optimise their AI investments, driving both efficiency and competitive advantage in an increasingly data- driven world.


Balancing AI and regulation As AI plays an increasing role in daily life, regulatory frameworks must keep pace. Te EU AI Act is one of the most comprehensive frameworks globally, and businesses outside the EU will be closely watching how EU firms adapt. Companies will need to take responsibility for the ethical use


of AI tools, especially in high-risk areas such as healthcare and employment. Innovation, while exciting, cannot go unchecked. AI will face increasing scrutiny – not only from regulators, who may struggle to legislate quickly enough, but also from customers and the public. Te penalties for non-compliance with the EU AI Act are


substantial (€30 million or 6% of annual global turnover, whichever is higher). However, the potential loss of customer trust could prove even more costly. To deploy AI ethically, businesses should build internal


governance frameworks that anticipate external regulations. Whether developing models in-house or collaborating with technology partners, they must avoid bias, ensure transparency in decision-making, and demonstrate accountability for AI outcomes.


since 1950. Multiply that by 200, and you’ll approximate the 300 petabytes of data large organisations are expected to manage by the end of 2026. Tat’s an enormous volume of data – and a significant source of potential risk if not managed properly, especially in AI applications. Te Hitachi Vantara State of Data Infrastructure Survey


revealed that nearly half of UK companies identified data quality as their primary concern when implementing AI projects. Yet, many IT leaders are falling short in addressing this issue, jeopardising AI success. A significant challenge lies in the management of ‘dark data’ – data collected but not actively used for analysis or decision-making. Alarmingly, 56% of UK IT leaders reported that more than half of their data falls into this category. As data volumes continue to grow, the proportion of dark data risks expanding exponentially, leaving vast insights untapped. Globally, only 38% of organisations are actively enhancing the quality of training data to improve AI outputs, while 20% fail to review datasets for accuracy, and 37% neglect to tag data for visualisation, highlighting critical gaps in AI readiness. To address this gap, IT leaders should review their data


management strategies and invest in modern data infrastructure to reduce risks and enhance AI outcomes. Advanced systems not only streamline data management but also ensure that AI strategies align seamlessly with business objectives. By leveraging scalable, flexible, and intelligent infrastructures, organisations can accelerate innovation while maintaining operational stability. Such frameworks allow businesses to test AI models with precision, adapt quickly to evolving demands, and extract


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Counting the environmental cost of AI Sustainability is another critical consideration. Infrastructure developed without sustainability in mind may require costly reconstruction to comply with future regulations. Organisations building infrastructure for current data needs should look ahead to avoid reinvestment down the line. Modern infrastructure offers a solution. Designed to be energy


efficient, it enables organisations to improve performance while reducing their carbon footprint. For example, advanced systems combine higher processing power with improved performance per watt, enabling businesses to meet growing demands with fewer units. Tis results in 30-40% less electricity usage compared to older models and requires less physical space, further reducing operational costs and environmental impact. Over the next year, businesses will likely become more


transparent about the environmental costs of AI due to increasing regulatory demands and stakeholder expectations. While AI can increase energy consumption—particularly during the training of large models—modernised infrastructure and advancements in energy-efficient data centers help offset these impacts. Moreover, AI-driven innovations, such as optimising supply chains or improving energy management in other operations, can deliver net environmental benefits, showcasing how businesses can balance growth with sustainability. Moving forward, IT leaders must embrace AI strategically,


ensuring that infrastructure, ethics, and sustainability initiatives align with long-term goals. While AI can deliver quick wins, its transformative potential demands careful planning.


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