In a rapidly evolving digital landscape, Artificial Intelligence is no longer a futuristic concept; it is a strategic driver shaping how industries and governments operate. As organizations seek smarter, faster, and more efficient decision-making, AI has become a critical enabler of transformation across sectors. In a fireside chat hosted by the ESG & Data for Good Center of Excellence, Mohamed Mysara, an AI industry expert at SAS, shared valuable insights on the evolution of Artificial Intelligence, from Generative AI to Agentic AI, and how data-driven intelligence is redefining public services, business operations, and everyday life.
AI Landscape
Mohamed Mysara began by defining Artificial Intelligence (AI) as a technology that has been quietly evolving for years, long before tools like ChatGPT captured the public’s attention. “AI did not suddenly appear,” he explained. “It has been embedded in systems we use daily, often without even realizing it. What has changed recently is not the technology itself, but our awareness of it and our ability to interact with it directly.”
At its core, AI functions like a digital brain, designed to simulate aspects of human intelligence. Just as humans rely on vision, hearing, reasoning, and creativity, AI systems are built from specialized components that replicate similar capabilities. Understanding these capabilities is crucial to seeing AI as more than a buzzword.
Understanding AI Beyond the Buzzwords
Many people now associate AI mainly with Generative AI, the technology behind text, image, video, and audio generation. While Generative AI is an important leap forward, it is only one part of a much larger ecosystem.
For example:
- Computer Vision enables machines to analyze images and videos, recognize faces, detect objects, and understand patterns.
- Audio Processing and Speech Recognition allow systems to convert speech into text, recognize voices, and even generate realistic audio.
- Machine Learning models learn from historical data, much like humans learn from experience, enabling prediction, classification, and decision support.
Generative AI represents the creative layer: the ability to produce new content based on prior learning. However, creativity alone is not enough to solve real-world problems.
The Shift toward Agentic AI
One of the most important emerging concepts today is Agentic AI. Unlike traditional AI systems that simply respond to prompts, Agentic AI is designed to take action.
Instead of only generating content, an AI agent can:
- Understand objectives and constraints
- Analyze data and context
- Make decisions
- Execute tasks autonomously
Examples include virtual agents that schedule meetings, plan travel within a given budget, manage customer complaints in call centers, or trigger operational actions based on detected issues. This shift—from “answering” to “acting”—marks a major evolution in how AI delivers value.
AI Has Been Part of Our Lives for Years
Maysara mentioned that AI is not new to critical industries. Financial institutions, for example, have relied on anomaly detection models for years to identify fraudulent transactions. When a bank flags or blocks a suspicious payment, that decision is often driven by AI analyzing behavioral patterns in real time.
Similarly, recommendation engines on e-commerce platforms, music streaming services, and digital assistants like Siri or Alexa are powered by AI. What has changed today is accessibility: modern AI tools are easier to use, more conversational, and commercially scalable.
Why Some AI Models Perform Better Than Others
Not all AI models, however, are created equal. Maysara emphasized that a model’s performance depends on multiple factors:
- The type and quality of data used during training.
- Specialization (language, vision, audio, code, or domain-specific data).
- Fine-tuning and reinforcement learning to improve consistency and accuracy.
- Retrieval-Augmented Generation (RAG), which allows models to retrieve answers from enterprise-specific datasets instead of producing generic responses.
For example, models trained extensively on Arabic language data will naturally perform better in Arabic contexts, while others may excel in Chinese or English environments. Specialization matters, especially in enterprise and government use cases.
Transforming Industries and Governments
AI is now delivering measurable impact across multiple sectors:
- Healthcare: AI-driven models are improving treatment recommendations, enabling personalized care plans, and significantly increasing patient survival rates in areas such as cancer treatment.
- Law Enforcement and Public Safety: AI helps detect patterns related to fraud, human trafficking, financial crime, and cyber threats—allowing faster and more informed decisions.
- Anti-Money Laundering: Governments and financial authorities use AI to uncover new laundering patterns, saving billions by preventing illegal financial flows.
- Education: AI analyzes student learning journeys, helping institutions improve curricula, monitor progress, and make data-driven decisions on resource allocation.
- Transportation and Smart Cities: AI optimizes traffic signals, public transportation routes, infrastructure planning, and urban mobility strategies to improve efficiency and citizen experience.
“These applications go far beyond simple automation,” Maysara emphasized. “AI enhances decision quality, reduces costs, and improves societal outcomes across multiple sectors.”
A Message for the Future
Maysara delivered a clear message: do not fear AI. While ethical considerations, data privacy, and governance are essential, avoiding AI entirely is far riskier than learning to use it responsibly. Individuals and organizations that experiment, adapt, and build AI capabilities will be better positioned for the future than those who resist change.
“The pace of AI innovation will only accelerate,” Maysara said. “New tools, models, and concepts will continue to emerge. Success will belong to those who have the courage to explore, learn, and evolve alongside these technologies.