Adopting AI in MENA: The Future of Economic Development and Growth
According to the Stanford University AI Index, its 2021 Global Vibrancy Ranking Weighted Index Scores in Research and Development and Economy ranks Israel in the top ten globally. However, the latest McKinsey report (2023) confirms that the AI deployment in the rest of MENA remains very low (with the exception of a few countries in the GCC). This volume analyzes the obstacles to AI adoption by companies in the region, and addresses the following questions specifically for MENA:
What is the potential for AI to contribute to global economic activity?
How might the adoption of AI widen gaps among countries, companies, and workers?
What are the challenges of adopting automation technology?
How might AI increase productivity growth?
What are the potential labor market impacts of AI?
What policies can help boost productivity growth while mitigating any labor market?
How might AI technologies be adopted and absorbed by companies?
What are the likely disruptions that countries, companies, and workers might experience as they transition to AI?
How might economic gains and losses be distributed among firms, employees, and countries?
How might this distribution potentially hamper the capture of AI benefits?
What are the dynamics of AI for a wide range of countries with similar characteristics?
How might AI adoption and full absorption vary across different industries and sectors?
By 2022, there were already 32 industry-produced AI models in the developed economies - compared to just three produced by academia. Since then, the economic deployment of AI has significantly advanced in these countries. AI models - such as ChatGPT, Stable Diffusion, Whisper, and DALL-E 2, just to give a few examples - are capable of an increasingly broad range of tasks across various domains:
What is the potential for AI to contribute to global economic activity?
How might the adoption of AI widen gaps among countries, companies, and workers?
What are the challenges of adopting automation technology?
How might AI increase productivity growth?
What are the potential labor market impacts of AI?
What policies can help boost productivity growth while mitigating any labor market?
How might AI technologies be adopted and absorbed by companies?
What are the likely disruptions that countries, companies, and workers might experience as they transition to AI?
How might economic gains and losses be distributed among firms, employees, and countries?
How might this distribution potentially hamper the capture of AI benefits?
What are the dynamics of AI for a wide range of countries with similar characteristics?
How might AI adoption and full absorption vary across different industries and sectors?
By 2022, there were already 32 industry-produced AI models in the developed economies - compared to just three produced by academia. Since then, the economic deployment of AI has significantly advanced in these countries. AI models - such as ChatGPT, Stable Diffusion, Whisper, and DALL-E 2, just to give a few examples - are capable of an increasingly broad range of tasks across various domains:
1. Natural Language Processing (NLP)
AI can understand and generate human language, allowing it to perform tasks such as language translation, sentiment analysis, chatbot interactions, and text summarization.
2. Image and Video Recognition
AI can analyze and interpret visual data, enabling tasks such as object recognition, facial recognition, image classification, and video content analysis.
3. Speech Recognition and Synthesis
AI can convert spoken language into written text and vice versa. This technology is used in applications like virtual assistants, transcription services, and voice-controlled systems.
4. Recommendation Systems
AI can analyze user preferences and behavior to provide personalized recommendations in various domains, including e-commerce, streaming services, and content platforms.
5. Autonomous Vehicles
AI is used in self-driving cars to perceive the environment, make decisions, and control the vehicle. It involves computer vision, sensor fusion, and real- time decision-making algorithms.
6. Medical Diagnosis and Treatment
AI can analyze medical data, including medical images and patient records, to assist in diagnosing diseases, recommending treatment plans, and predicting patient outcomes.
7. Fraud Detection
AI can analyze large volumes of data to identify patterns and anomalies that may indicate fraudulent activities in financial transactions, insurance claims, or online transactions.
8. Gaming
AI algorithms can learn and play games at a high level of proficiency. This includes traditional board games, video games, and competitive eSports.
9. Data Analysis and Predictive Modeling
AI techniques, such as machine learning and deep learning, can process large datasets to extract insights, make predictions, and support decision-making in fields like finance, marketing, and logistics.
10. Virtual Assistants and Chatbots
AI-powered virtual assistants like Siri, Google Assistant, and Alexa can understand and respond to user queries, perform tasks like setting reminders, providing weather updates, and controlling smart home devices.
Other volumes in CEESMENA's Research Series