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What is Agentic AI?

When people ask, “What is Agentic AI?”, the answer lies in its autonomy. Unlike rule-based systems or generative AI (which creates content), Agentic AI analyzes data, learns dynamically, and makes decisions without human intervention. For instance, while AI in automation might streamline workflows, Agentic AI goes further—predicting risks, personalizing customer interactions, and executing actions in real time.

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What is Generative AI in Data Analytics?

Generative AI refers to artificial intelligence systems that create new and original data by analyzing and learning patterns from vast datasets. Unlike traditional AI, which primarily processes and classifies information, generative AI can synthesize new insights, automate report generation, and provide contextual data-driven recommendations. Using deep learning models like transformers and GANs, it identifies patterns, detects anomalies, and generates real-time insights. Businesses can use AI for data analytics to automate report generation, refine data modeling, and run advanced scenario simulations. By integrating natural language processing (NLP), it simplifies data interpretation and makes analytics more accessible. With its ability to streamline workflows and enhance business intelligence, Generative AI in data analytics is reshaping the way organizations analyze and act on data.
How Does Generative AI Work?
Generative AI models are built on sophisticated machine learning techniques, such as:
Generative Adversarial Networks (GANs): Used for synthetic data generation and anomaly detection in datasets.
Transformer Models: Power natural language processing (NLP) tools that enable conversational AI for querying databases.
Multimodal Models: Process multiple data types, allowing enhanced AI-driven insights from structured and unstructured data.
The training of these models requires massive datasets and computational power, often utilizing cloud-based infrastructure to enhance scalability and efficiency.
The training of these models requires massive datasets and computational power, often utilizing cloud-based infrastructure to enhance scalability and efficiency. For example, OpenAI’s GPT-4 was trained on 45 terabytes of text data (Source).
Real-World Applications of Generative AI
Using Gen AI for data analysis businesses can automate the creation of analytical reports by summarizing trends, anomalies, and patterns in data, saving time and reducing manual effort.
AI for Predictive Analytics
AI for Predictive Analytics enhances forecasting accuracy for business intelligence, identifying trends and patterns that impact decision-making.
AI in Business Intelligence
Generative AI in data analytics improves data storytelling by transforming raw data into easy-to-understand narratives, enabling organizations to gain deeper insights.
AI in Customer Insights and Segmentation
AI and data analytics enables personalized customer experiences by analyzing vast datasets to uncover behavioral patterns and preferences.
AI in Fraud Detection
Financial institutions use AI for data analytics to detect fraudulent transactions by identifying anomalies in real-time data streams.

What is Agentic AI Architecture

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Agentic AI architecture allows AI systems to set objectives, make choices, and take action without constant guidance, instead of being told what to do every step of the way.
In agentic AI architecture, the system perceives its environment, plans its next steps, remembers past experiences, and executes tasks, all with minimal human input. This makes the AI more goal-driven and adaptable.

For example, rather than just generating a report when asked, an agentic AI might identify a business objective, gather the right data, analyze it, and deliver useful insights without being told exactly how.
This architecture also scales to multi-agent systems, where several AI agents work together to solve more complex problems.

Importance of Data Analytics

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The importance of data analytics in today’s business world cannot be overstated. Every second, companies generate vast amounts of data, but without proper analysis, it remains just raw information. By leveraging data analytics, businesses can uncover valuable insights that drive growth, efficiency, and customer satisfaction.

Oracle Financial Analytics: Unveiling General Ledger KPIs and Dashboards for Actionable Insights from a Functional Perspective - Part 1

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Discover how Oracle Financial Analytics revamp Business finance with real time insights Learn key features and benefits from our expert guide

Introduction
In today’s data-driven landscape, leveraging Oracle Financial Analytics stands as a cornerstone for informed decision-making and strategic planning. As part of our initiative, we’ve implemented robust financial analytics on Google Cloud Platform (GCP / Looker ), harnessing data from Oracle EBS (E-Business Suite) to empower businesses with action