t cht lk
t cht lk
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 .
Sustainability frequently arises in discussions with customers , who increasingly seek partners that can help them achieve net-zero commitments and sustainability goals .
Successful businesses will prioritise energy-efficient 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 . language-based limitations by analysing quantitative data specific to each sector ’ s challenges .
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 systems , are necessary to handle the computational demands of massive models .
Dr Stefan Leichenauer , VP Engineering , SandboxAQ
Arrival of Large Quantitative Models and agentic AI
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 decisionmaking 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 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
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 , empowering employees to make smarter and faster decisions .
By prioritising AI training and developing accessible AI-driven tools , organisations can enable employees at all levels to effectively leverage AI .
This approach not only boosts productivity but also democratises access to AI across the organisation . Collaborating with AI specialists to design user-friendly tools ensures that employees in diverse roles , from marketing and finance to HR and operations , can use AI to drive data-informed decisions and foster innovation within their departments .
Developing an AI-literate workforce will be essential for Middle Eastern companies to remain competitive and establish a culture of continuous improvement .
While AI is advancing rapidly , the cost-efficiency of AI solutions remains a critical challenge . Many AI systems operate on tight margins , with significant investment required for data , energy , and computational resources in the pursuit of selfsustaining AI models .
As the industry matures , companies may increasingly adopt smaller , task-focused models to reduce costs and improve returns on investment . Specialised models , in many cases , outperform broad , general-purpose models , offering efficient and targeted solutions .
As AI becomes embedded in more sensitive aspects of business operations , data security will become
66 INTELLIGENTCIO AFRICA www . intelligentcio . com