Setting up an AI Office for Adapting and Scaling AI

Upcoming Schedules:

• 26th August 2024 - Monday
• 23rd September 2024 - Monday

Course Overview:

This course provides a comprehensive guide to establishing an AI office within an organization. It covers the strategic, technical, and operational aspects necessary for adapting and scaling AI solutions effectively.

Participants will learn how to build a robust AI infrastructure, manage AI projects, and foster a culture of innovation.

Target Audience:

This course is suitable for Senior Leaders who want to leverage AI services to facilitate and deploy intelligent solutions.

Prerequisites:

Prerequisites include a basic understanding of AI concepts, and leadership experience managing teams or projects. Participants should be comfortable with technology, possess data literacy, and have strategic thinking and problem-solving skills.

Open-mindedness and adaptability to new technologies are crucial, with pre-course reading and optional workshops recommended for additional preparation.

What's Included :

• 8 weeks (2 sessions per week, 2 hours per session)
• Workshop mode - instructor-led training 
• Recommended study guides & class notes
• Leading Industry CXO practitioner delivering the class
• Case studies 
• Groups projects & exercises 
• Q&A sessions and final project presentation

Module 1: Introduction to AI and Its Business Impact

Session 1:

Overview of AI Technologies
Definition and types of AI
Current trends and future outlook

Session 2: 

Business Applications of AI
Case studies of successful AI implementations
Benefits and challenges of AI adoption

Module 2: Strategic Planning for AI Implementation

Session 3:

Developing an AI Strategy
Aligning AI with business goals
Identifying AI opportunities and use cases

Session 4: 

Building an AI Roadmap
Phases of AI implementation
Setting milestones and KPIs

Module 3: Setting Up the AI Office

Session 5:

Organizational Structure and Roles
Key roles in an AI team (Data Scientists, AI Engineers, etc.)
Defining responsibilities and workflows

Session 6:

Infrastructure and Tools
Hardware and software requirements
Selecting AI platforms and tools

Module 4: Data Management and Governance

Session 7:

Data Collection and Preparation
Data sources and acquisition methods
Data cleaning and preprocessing techniques

Session 8: 

Data Governance and Ethics
Ensuring data privacy and security
Ethical considerations in AI

Module 5: AI Development and Deployment

Session 9:

AI Model Development
Machine learning and deep learning techniques
Model training and evaluation

Session 10:

Deployment and Integration
Deploying AI models in production
Integrating AI with existing systems

Module 6:  Scaling AI Solutions

Session 11:

Scaling AI Projects
Strategies for scaling AI solutions
Managing AI project lifecycle

Session 12:

Monitoring and Maintenance
Continuous monitoring and performance tuning
Handling model drift and updates

Module 7:  Fostering an AI Culture

Session 13:

Building an AI-Driven Culture
Promoting AI literacy and education
Encouraging innovation and experimentation

Session 14:

Change Management
Managing organizational change
Overcoming resistance to AI adoption

Module 8: Case Studies and Capstone Project

Session 15:

Case Studies of AI Implementation
In-depth analysis of real-world AI projects
Lessons learned and best practices

Session 16:

Capstone Project Presentation
Participants present their AI implementation plans
Peer review and feedback