Course Overview

Enterprise AI: Designing, Deploying, and Scaling AI Solutions for Business Transformation

Lead Instructor: Professor Dr. Emdad Khan (CEO, InternetSpeech Inc. USA)

Co-Instructor: Md. Ridoy Sarkar (Lecturer, Daffodil International University)

⏱ Duration: 10 Weeks | 48 Hours (Total)

Course Description

This course is designed to bridge the critical gap between technical AI skills and business value creation. Moving beyond experimental models, this program equips professionals to architect, govern, and scale AI solutions across large organizations. It focuses on integrating AI with legacy systems (ERP, CRM), ensuring compliance, and optimizing ROI for sustainable business transformation.

Evidence of Demand

With the rapid rise of AI and Digital Transformation, organizations are moving beyond experimental AI models to scalable, governed enterprise solutions. There is a critical gap for professionals who can bridge technical AI skills (Data Science, LLMs) with business value creation (ROI, Compliance, Integration). Industry demands architects who can deploy AI across supply chains, HR, and finance at scale.

Purpose and Objectives

The purpose of this course is to equip students with the skills to architect, govern, and scale AI solutions in large organizations. Objectives:

• Understand Enterprise AI architecture & Digital Twins.
• Learn to integrate AI with legacy systems (ERP, CRM).
• Master AI Governance, Security, and Compliance.
• Develop strategies for ROI optimization and scaling.

Course Content & Class Plan (Modules)

Tentative Syllabus:

• Module 1: Intro to Enterprise AI (Context & Maturity Models)
• Module 2: Generative AI & LLMs (RAG, Knowledge Assistants)
• Module 3: Enterprise Data Foundations (Data Lakes, Governance)
• Module 4: AI Solution Architecture (Cloud vs. On-Prem, Microservices)
• Module 5: Enterprise AI Use Cases & Case Studies (Finance, Health, Mfg)
• Module 6: AI & Agentic Workflow Development (Lifecycle, Feature Eng.)
• Module 7: MLOps & Model Lifecycle Management (CI/CD, Monitoring)
• Module 8: AI Integration with Enterprise Systems (ERP/CRM APIs)
• Module 9: AI Governance, Security & Compliance (Privacy, Ethics, Transparency)
• Module 10: Decision Intelligence & Analytics (Predictive vs. Prescriptive)
• Module 11: Scaling & Performance Optimization (Cost/Reliability)
• Module 12: Capstone Project (Design & Implementation)

Practical & Field Work

Note: Practical assessments constitute approximately 70% of the course grade.

• Labs & System Design: Hands-on tasks involving MLOps tools (Docker, MLflow), visualization (PowerBI/Tableau), and Python environments.
• Capstone Project: Students will design and implement a complete Enterprise AI solution, including defining the business problem, creating the architecture, and providing a live demonstration.
• Examples include: AI-Powered Academic Administration Systems, Enterprise Knowledge Assistants (RAG-based), and Predictive Analytics Dashboards.

Learning Outcomes

Upon completion, students will be able to:

• Strategize: Identify AI opportunities for business optimization.
• Architect: Design scalable Cloud/Hybrid AI systems.
• Integrate: Connect AI with enterprise apps via APIs.
• Govern: Implement security and compliance frameworks.
• Evaluate: Measure AI success via ROI and impact.
• Scale: Deploy sustainable systems using MLOps.

Target Audience

• CSE/SWE/CIS/ITM Students (3rd/4th Year)
• Data Scientists & ML Engineers
• Solution Architects & Project Managers
• Industry Professionals seeking AI leadership roles

Entry Requirements

• Minimum Age: 20 Years
• Basic Programming: Python recommended
• Foundational Knowledge: Introduction to AI/Machine Learning and fundamentals of Databases & Information Systems
• Resource Requirements: Internet/Cloud Access

Career Pathways

• Enterprise AI Architect
• AI Product Manager
• MLOps Engineer
• AI Transformation & Digital Transformation Consultant
• Chief AI Officer (CAIO) track

Tools & Resources

• Programming: Python Environment (Jupyter/Colab)
• MLOps Tools: Docker, Kubernetes, MLflow, Jenkins/GitHub Actions
• Cloud Platforms: AWS / Azure / Google Cloud (Conceptual & Hands-on)
• ML Frameworks: Scikit-learn, TensorFlow, PyTorch
• Visualization: PowerBI / Tableau

Assessment Criteria

• Case studies & assignments: 30%
• Labs & system design tasks: 30%
• Final project: 40%

Standard Course Fee: Tk 15,000/-

The following discounts currently apply:

• Daffodil Students: Tk 5,000/- (67% Discount Promotional Offer)
• Non-Daffodil Students: Tk 7,500/-