DISRUPTIVE TECH sophisticated . According to the latest Cisco AI Readiness Index , only 30 % of companies globally have the capabilities to tackle these threats . As cybercriminals adapt their tactics , networks will become essential as the first and last line of defence . Integrating AI will enhance human capabilities , strengthening network security and policy enforcement .

The environmental impact of AI is the elephant in a lot of rooms . AI requires high energy consumption levels that impact carbon emissions across the board .

The energy used by AI-dedicated data centres is expected to match the amount consumed by a country the size of the Netherlands in one year .

Over 40 % of network outages are directly caused by misconfigurations , and can cost businesses 9 % of their total annual revenue . AI has the potential to virtually eliminate these manual misconfiguration mishaps .

Sustainability frequently arises in discussions with customers , who increasingly seek partners that can help them achieve net-zero commitments and sustainability goals .

Intelligent , automated tools can execute workflows throughout the network lifecycle and provide traceability for every action . AI-driven tools will revolutionise network management , learning from each configuration to reduce errors and ensure uninterrupted operations . As AI adoption increases , we expect to see a rapid decline in misconfigurations and network downtime caused by human error approach zero .

Successful businesses will prioritise energyefficient products and circular business models . AI technology will be pivotal in enhancing energy efficiencies , ushering in an era of energy networking that combines software-defined networking with direct current , DC microgrids for improved visibility into emissions and optimisation of power usage , distribution , and storage .

Dr Stefan Leichenauer , VP Engineering , SandboxAQ

This agentic approach represents a new frontier , enabling systems that adapt to dynamic conditions , a significant advantage in sectors where conventional data analysis may fall short . positioning the region as a leader in next-generation AI applications .

Verticals such as agriculture , construction , manufacturing , and supply chain management have historically been slower to embrace AI . But , as AI technologies advance , these industries are on the brink of transformative change . LQMs , with their robust capabilities , offer tailored solutions that surpass language-based limitations by analysing quantitative data specific to each sector ’ s challenges .

Large Quantitative Models , LQMs are instrumental in this evolution , leveraging extensive quantitative data combined with physics-aware architectures .

Unlike language models , which predominantly process text , LQMs are designed to address complex , dataintensive challenges in fields such as drug discovery , materials science , healthcare diagnostics , financial modelling , and industrial optimisation .

The precision and data-driven insights provided by LQMs far exceed what typical AI tools can achieve . For regional companies operating in these sectors , adopting LQMs could facilitate breakthroughs ,

As AI becomes more prominent in these industries , the demand for new roles that blend AI expertise with sector-specific knowledge will grow . Professionals skilled in both AI and industry nuances will be essential to maximise the impact of these technologies .

As AI models scale up in size and complexity , dedicated infrastructure becomes crucial . The focus in AI is shifting from algorithmic advancements alone to the physical infrastructure that supports AI applications at scale .

Custom-built data centres and high-performance hardware , paired with optimised energy management

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