Agentic AI vs. Generative AI

Introduction

• Recent years have seen a rise in terms such as generative AI (gen AI) and agentic AI.
• Traditional AI focused on pattern recognition and data analysis.
• Gen AI creates new content like text, images, audio, etc.

Generative AI

• Definition: AI that generates original content in response to user prompts.
• Technologies: Utilizes deep learning models, machine learning, and robotic process automation.

Key Features

• Content Creation: Able to generate coherent essays, solve complex problems, and aid in software development.
• Data Analysis: Analyzes vast data to discover patterns and streamline workflows.
• Adaptability: Adjusts output based on user feedback.
• Personalization: Offers personalized recommendations, especially beneficial in retail.

Use Cases

• SEO Content Creation: Generates SEO-optimized content for driving traffic.
• Marketing and Sales: Assists in lead generation and administrative tasks.
• Product Design: Speeds up product development cycles.
• Customer Support: Automates responses for common inquiries.

Agentic AI

• Definition: AI systems designed to autonomously make decisions and act with limited supervision.
• Technologies: Combines large language models, NLP, machine learning, reinforcement learning.

Key Features

• Decision-Making: Assesses situations without/minimal human input.
• Problem-Solving: Follows a four-step approach of perceiving, reasoning, acting, and learning.
• Autonomy: Operates independently to perform complex tasks.
• Interactivity: Adjusts in real-time, as seen in self-driving vehicles.
• Planning: Executes multi-step strategies for specific goals.

Use Cases

• Customer Service: Enhances customer interaction and automates data management.
• Healthcare: Used in diagnostics and patient care devices like smart inhalers.
• Workflow Management: Automates supply chain operations and logistics.
• Financial Risk Management: Analyzes market trends for investment decisions.

Trends

Generative AI Trends

• Augmented Applications: Integration into software to enhance user experience.
• Synthetic Data: Used for training models where real data is scarce.
• Deepfake Technology: Raises ethical concerns despite its realistic outputs.
• Content Personalization: Allows targeted marketing based on data analytics.

Agentic AI Trends

• Financial Services: Revolutionizes trading with real-time data analysis.
• Robotics: Used in automation in places like Amazon warehouses.
• City Planning: Facilitates data-driven urban planning decisions.
• Human Resources: Provides autonomous decision-making in employee management.

Future Outlook

• Agentic AI is identified as a top strategic technology trend for 2025.
• Offers potential opportunities and risks for IT leaders to prepare for future AI innovations.