FUTURE TECHNOLOGY
service AI that predicts user needs before a query is made , or a network management AI that identifies potential issues and resolves them autonomously , ensuring uninterrupted service .
In response to the rise of Agentic AI , we will see organisations implementing mandatory ethical guidelines to ensure fairness and transparency in algorithmic decisions and protecting intellectual property .
The interconnectedness of digital ecosystems complicates security , making attacks more 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 .
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 .
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 .
Arrival of Large Quantitative Models and agentic AI
Dr Stefan Leichenauer , VP Engineering , SandboxAQ
As organisations navigate the evolving AI environment , they must adopt future-oriented strategies to stay competitive . The future of AI extends beyond traditional language-based models , LLMs and is now evolving toward autonomous agents capable of decision-making through environmental interactions .
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 .
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 , data-intensive 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 , positioning
The precision and datadriven insights provided by LQMs far exceed what typical AI tools can achieve .
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 .
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 highperformance hardware , paired with optimised energy management systems , are necessary to handle the computational demands of massive models .
AI ’ s value in the workplace extends far beyond simple automation . As companies strive to improve productivity and streamline operations , AI tools are becoming integral to daily workflows ,
INTELLIGENT TECH CHANNELS 37