Businesses heavily rely on data sourced from various channels like news articles, financial reports, and consumer reviews to drive their operations, enabling informed decision-making and identifying opportunities. However, traditional manual methods for data extraction are often time-consuming and resource-intensive, prompting the adoption of digital transformation initiatives to enhance efficiency. Yet, concerns persist regarding the sustainability of such initiatives and their alignment with the United Nations (UN)'s Sustainable Development Goals (SDGs). This research aims to explore the integration of Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) as a sustainable solution for Information Extraction (IE) and processing. The research methodology involves reviewing existing solutions for business decision-making, noting that many systems require training new machine learning models, which are resource-intensive and have significant environmental impacts. Instead, we propose a sustainable business solution using pre-existing LLMs that can work with diverse datasets. We link domain-specific datasets to tailor LLMs to company needs and employ a Multi-Agent architecture to divide tasks such as information retrieval, enrichment, and classification among specialized agents. This approach optimizes the extraction process and improves overall efficiency. Through the utilization of these technologies, businesses can optimize resource utilization, improve decision-making processes, and contribute to sustainable development goals, thereby fostering environmental responsibility within the corporate sector.

Region's first live, interactive networking business event of H2 2020 On 16 July, Dubai World Trade Centre hosted the Ai Everything x Restart Dubai Summer Conference, the MEASA region's first live, in-person business event to be hosted in H2 2020, driving forward the resumption of the global events sector. Showcasing the profound effect of artificial intelligence on the UAE's pandemic recovery, the event also exemplified how participating entities, technologies and prototypes can revolutionise the country's private sector and its growth prospects in the coming years. Dubai World Trade Centre implemented comprehensive regulatory protective measures at the venue, including temperature screening, social distancing, contactless transactions and hygiene protocols, with all upcoming events set to be conducted similarly in a highly controlled manner to ensure adherence to the strictest health and safety protocols for public wellbeing. His Excellency Helal Saeed Almarri, Director General, Dubai Tourism (DTCM) and Dubai World Trade Centre Authority (DWTCA) said: "Artificial intelligence will transform every industry in the UAE and will provide wide-reaching economic benefits, while the MICE sector remains essential to both the UAE's economic diversification agenda and Dubai's GDP, as well as being a crucial driver behind the development of a knowledge-based economy and a self-sustaining entrepreneurial ecosystem. Visitor safety and assuring a seamless experience for every participant remains our number one priority, and the Ai Everything x Restart Dubai Summer Conference provides a platform for us to implement best-in-class protocols and standards for the resumption of MICE activity, reinforcing Dubai's status a world-class MICE destination and serving as an international model for MICE resumption."

The Stock Sonar (TSS) is a stock sentiment analysis application based on a novel hybrid approach. While previous work focused on document level sentiment classification, or extracted only generic sentiment at the phrase level, TSS integrates sentiment dictionaries, phrase-level compositional patterns, and predicate-level semantic events. TSS generates precise in text sentiment tagging as well as sentiment-oriented event summaries for a given stock, which are also aggregated into sentiment scores. Hence, TSS allows investors to get the essence of thousands of articles every day and may help them to make timely, informed trading decisions. The extracted sentiment is also shown to improve the accuracy of an existing document-level sentiment classifier.