AI Future Unlocked: Exploring Actionable Gen AI Model in the Middle East
By gathering feedback on best practices, user experiences, and the specific needs of the Middle East market, the white paper, Leveraging Actionable Gen AI In The Middle East, in partnership with Astra Tech, provides a localized perspective addressing cultural sensitivities and compliance.
Topics
MIT SMR CONNECTIONS
News
- More than 80% of Saudi CEOs adopted an AI-first approach in 2024, study finds
- UiPath Test Cloud Brings AI-Driven Automation to Software Testing
- Oracle Launches AI Agent Studio for Customizable Enterprise Automation
- VAST Data and NVIDIA Launch Secure, Scalable AI Stack for Enterprises
- How Machine Identity Risks Are Escalating in AI-Powered Enterprises
- How Agentic AI Is Reshaping Healthcare
[Image source: Krishna Prasad/MITSMR Middle East]
Businesses are on the brink of a major transformation, with language models (LMs) providing automation and enhancement capabilities. This is especially crucial for industries that heavily depend on data processing, customer interaction, and content generation, where LMs can boost efficiency and cost-effectiveness.
In the white paper, Leveraging Actionable Gen AI In The Middle East, MIT SMR Middle East, in collaboration with Astra Tech, explores the transformative opportunities presented by Large Language Models/Small Language Models (LLMs/SLMs) and Large Action Models (LAMs) and examines how businesses in the Middle East are leveraging these AI models to gain a competitive edge.
Free Download: Leveraging Actionable Gen AI in the Middle East
Understanding the Powerhouse: LLMs and SLMs
LLMs, trained on massive datasets of text and code, excel at complex tasks like generating creative content, translating languages, and answering intricate questions. SLMs, their leaner counterparts, specialize in specific areas like sentiment analysis and chatbot interactions. Both leverage statistical analysis, contextual understanding, and entity recognition to decipher customer queries.
LAMs: From Understanding to Action
LAMs bridge the gap between language comprehension and real-world action. They are trained on data that includes text, sensor readings, robot movements, and other forms of interaction with the environment. They can process information from various sensors like cameras and LiDAR to understand their surroundings and use this understanding to plan and execute actions, such as controlling a robot arm or navigating a self-driving car.
The “Ultra” AI Advantage: Collaboration at its Finest
The true magic unfolds when LLMs and LAMs join forces within an AI system. LLMs take center stage, interpreting user queries and their intent. The LLM translates the user’s request into a form the LAM can understand, which could involve controlling a device, retrieving information from a database, or manipulating objects in the physical world.
AI Adoption and LLM/SLM integration
The Middle East presents a fascinating picture of AI adoption. While a significant portion of companies (27.63%) are still in the exploration phase, a strong showing (36.84%) has begun initial adoption. This indicates a growing awareness of AI’s potential and a willingness to experiment with its capabilities. Interestingly, a smaller but noteworthy group (13.16%) already achieved deep integration, suggesting a forward-thinking approach in specific sectors.
Application across industries
In the Middle East, businesses across industries are keen to unlock AI’s power. AI models can be an immensely powerful tool for businesses, from operational efficiencies and cost savings to addressing ethical and accountability concerns. There are instances of retail firms integrating LLMs, SLMs, and LAMs to deliver a personalized shopping experience at scale, recommending products based on individual customer purchase history and browsing behavior. This deep integration increases customer satisfaction and a more efficient shopping journey.
Since security, privacy, and other concerns remain high in the region, to get value from AI models, some businesses are developing in-house language models trained with all relevant compliance data. This model functions as a conversational bot, aiding compliance officers by answering specific regulatory questions, thereby enhancing efficiency and productivity.
Free Download: Leveraging Actionable Gen AI in the Middle East
User Interaction with Gen AI Systems
Understanding user satisfaction and addressing challenges are pivotal for improving technology adoption and user experience.
The survey data provides valuable insights into user interactions with AI-powered features, highlighting their satisfaction levels, challenges faced, and potential avenues for improvement.
According to the survey, the predominant interactions with AI-powered features include:
- Information Retrieval: 46.67% of users engage AI to retrieve information, showcasing its role as a primary utility for knowledge dissemination.
- Customer Service/Support: 33.33% rely on AI for customer service interactions, highlighting its integration into daily business and consumer interactions.
The survey addressed two key aspects to comprehensively understand AI adoption: representation across various organizational levels and participation from a diverse range of industries. By including senior executives, heads of departments, directors, chief officers, managers, and vice presidents, the survey captured insights from both strategic decision-makers and those responsible for ground-level implementation. Additionally, participation from a mix of industries, including technology, finance, government, healthcare, retail, transportation, media, and more, ensures the survey results apply to a broad spectrum of sectors. The geographic spread of respondents, with a significant portion from the Middle East and other regions, further strengthens the findings.
Talking about the white paper, Hassan Alnoon, Senior Vice President Tech, Astra Tech said, “The findings from this whitepaper highlight the immense potential AI holds for transforming industries in the Middle East. By gathering feedback on best practices, user experiences, and the specific needs of the Middle East market, this whitepaper provides a localized perspective that addresses cultural sensitivities and compliance, ensuring that our AI initiatives are both impactful and sustainable. The importance of localization cannot be overstated, as it allows us to tailor our solutions to the unique cultural and regulatory environment of the region, maximizing their effectiveness and acceptance.”
Elaborating on the survey findings, MIT SMR Middle East Publisher, Ravi Raman added, “The Middle East is experiencing a period of rapid technological advancement, and AI is at the forefront of this transformation. As AI applications become more sophisticated and integrated across industries, there is a growing need for insightful reporting and ongoing conversations to navigate this evolving landscape.”
