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CETA Program

Course Overview

The Certified Enterprise Transformation Analyst (CETA) certification equips professionals with the strategic mindset and AI-driven execution frameworks to lead enterprise-wide transformation using Agentic AI, LLMs, and RAG.

🔹 Total Course Duration: 100 Hours (60 Hours Learning + 40 Hours Practical Internship)
🔹 Mode: Hybrid (Live + Hands-on Lab + Enterprise Case Studies)
🔹 Outcome: Develop AI-driven strategies, deploy LLMs, and execute transformation initiatives at scale.

Phase 1: Foundations of Enterprise Transformation (10 Hours)

 📌 Objective: Understand enterprise transformation principles, drivers, and barriers.

1.1 Understanding Enterprise Transformation

  • What is Enterprise Transformation?
  • The 3 Pillars: People, Processes, and Technology
  • Why most transformation initiatives fail

1.2 The Role of AI in Enterprise Transformation

  • How Agentic AI differs from traditional AI
  • The LLM Revolution and its impact on business strategy
  • RAG (Retrieval-Augmented Generation): Making AI decisions more accurate

1.3 The Future of Work and Digital Disruption

  • How automation, AI agents, and cognitive AI redefine enterprise workflows
  • The rise of AI-augmented decision-making
  • AI vs. Human Collaboration: Finding the balan

Phase 2: Strategy Development & AI-Driven Execution (20 Hours)

 📌 Objective: Learn how to craft AI-powered transformation strategies and execute them with agility.

2.1 Strategic Transformation Frameworks

  • OKRs, Balanced Scorecard, and AI-driven KPIs
  • How to align AI capabilities with business goals
  • Case Study: How top enterprises leverage AI to drive transformation

2.2 Enterprise Architecture for AI-Driven Transformation

  • The AI-Driven Enterprise Stack
  • Cloud vs. On-Prem AI Architectures: Trade-offs & cost-benefit analysis
  • Designing AI-ready organizational structures

2.3 Leveraging LLMs for Strategy Execution

  • How LLMs enhance business strategy formulation
  • Predictive vs. Prescriptive AI: Driving intelligent decision-making
  • Case Study: AI-powered financial forecasting and risk mitigation

Phase 3: Deploying Agentic AI and RAG for Enterprise Applications (20 Hours)

 📌 Objective: Implement AI-driven workflows using Agentic AI models, LLMs, and RAG systems.

3.1 Introduction to Agentic AI for Enterprise Operations

  • What are AI agents, and how do they enhance execution?
  • LLM-powered AI Agents: How they drive workflows autonomously
  • The Federated Enterprise AI Model: Securing AI in business operations

3.2 Retrieval-Augmented Generation (RAG) for Enterprise Intelligence

  • Why RAG? Solving the AI hallucination problem
  • How RAG-powered AI Agents improve enterprise knowledge management
  • Building AI-powered strategic dashboards

3.3 Designing AI-Augmented Decision Systems

  • The Decision Intelligence Layer: Combining RAG and structured databases
  • LLMs in Business Process Automation (BPA): Enhancing enterprise efficiency
  • Case Study: Deploying AI-driven decision support systems

3.4 App-in-Chat SDK: The New Interface Layer for Agentic Workflows

  • From Automation to App-in-Chat Ecosystems
  • Architecture and Functional Flow of App-in-Chat SDKs
  • Strategic Advantages: Unified experience, contextual memory, and composable workflows
  • Enterprise Use Cases: Procurement automation, decision rooms, incident management
  • The Future of Conversational Enterprise OS: From “Chat about work” → “Do work in chat”

Phase 4: Real-World Enterprise AI Applications (10 Hours)

 📌 Objective: Learn to apply AI to enterprise domains with hands-on case studies.

4.1 AI-Driven Customer Experience (CX) & Personalization

  • Conversational AI: Deploying LLM-powered chatbots
  • AI for hyper-personalized marketing & recommendations
  • Case Study: AI-powered customer retention strategies

4.2 AI in Finance, Compliance, and Risk Management

  • LLMs in Regulatory Compliance: Automating legal workflows
  • Risk Scoring Models: Using AI for fraud detection & credit risk assessment
  • Case Study: AI-powered fintech innovations

4.3 AI-Augmented Supply Chain & Operations

  • Predictive Analytics for Demand Forecasting
  • Agentic AI in Logistics Optimization
  • Case Study: Using AI to optimize enterprise supply chain networks

Phase 5: Practical Internship – Hands-On Enterprise AI Projects (40 Hours)

 📌 Objective: Work on real-world enterprise AI projects with industry mentors.

Internship Experience Includes:
Designing AI-powered strategic models for transformation
Developing a custom RAG-based knowledge management system
Implementing LLM-powered automation workflows
Optimizing AI-driven enterprise operations

🎯 Capstone Project:

  • Real-World Transformation Challenge: AI-powered business strategy implementation
  • Presentation to a panel of AI and business experts

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