Course Overview
Dive into the dynamic world of Artificial Intelligence (AI) with our one-day course, "Implementing AI in Business: Stakeholder Strategies for the Modern Data Driven, Automated Enterprise." Designed to provide stakeholders with a panoramic yet nuanced view of AI's transformative capabilities, this course serves as both a primer and a catalyst for action. From understanding the pivotal role of data collection and storage, to deciphering the symbiosis between Big Data, Data Science, and AI, we've curated the course to offer you a blend of strategic knowledge and practical skills.
We're not just covering the essentials; this course also empowers you with the capability to make informed decisions on various AI applications. Learn to evaluate cloud-based versus on-premise solutions, understand the key factors in building effective data science and AI teams, and get familiar with essential AI frameworks and tools. Our expert facilitators will also give you an exclusive look into the future of business AI through bonus chapters on Generative AI and emerging trends in data analytics and machine learning. These forward-looking topics will equip you to anticipate and adapt to rapidly changing technologies.
Through engaging lectures, timely demonstrations, and interactive discussions, you'll walk away with a balanced understanding of AI's real-world applications and future potential. Whether you're aiming to enhance existing systems or pioneering new AI initiatives, this comprehensive course ensures that you leave with actionable insights and a roadmap for meaningful change.
Who should attend
This introductory-level course is geared for a mix of business stakeholders who are either directly involved in or impacted by AI-driven initiatives within their organization. This includes C-suite executives, business unit leaders, product managers, and innovation strategists eager to gain a working knowledge of AI's capabilities and applications in a business context. Professionals in roles such as data analysts, IT managers, and project managers who collaborate with data science teams will find this course instrumental in bridging gaps and facilitating effective communication across departments.
Prerequisites
Although there are no specific pre-requisites to attend this course, it would be helpful to have a basic understanding of business metrics and key performance indicators, elementary data literacy, basic understanding of project management, and general tech savviness.
Course Objectives
Working in an interactive learning environment, led by our engaging AI expert, you will:
- Learn which data is most useful to collect now and why it’s important to start collecting that data as soon as possible
- Understand the intersection between big data, data science and AI (Machine Learning / Deep Learning) and how they can help you reach your business goals and gain a competitive advantage.
- Understand the factors that go into choosing a data science and AI driven systems including whether to go with a cloud-based solution
- Explore common tools and technologies to aid in making informed decisions
Outline: Implementing AI in Business: Stakeholder Strategies for the Modern Data Driven, Automated Enterprise (TTAI2101)
1. Introduction to AI in Business
- Explore the relevance and potential impact of AI technologies in modern businesses.
- What is AI and Why Does It Matter
- The ROI of AI Adoption
- Case Studies: AI Transformations
- AI Ethics and Regulation
- AI's Role in Different Business Units
- Real-World AI Transformations: A quick overview of businesses successfully adopting AI
2. Data: The Bedrock of AI
- Identify the types of data most pertinent to AI applications in business.
- Types of Data: Structured vs Unstructured
- Importance of Data Collection
- Data Storage Solutions
- Data Security and Compliance
- Data Integration Strategies
- Database Choices: Evaluating databases suited for AI applications
3. Big Data, Data Science, and AI – The Power Trio
- Explore how Big Data, Data Science, and AI synergize for business advantage.
- The Big Data Ecosystem
- Data Science Methodologies
- Machine Learning vs Deep Learning
- Business Cases for Data Science and AI
- Tools for Analytics and Data Processing
4. Cloud-Based vs On-Premise Solutions
- Make informed decisions on cloud-based vs. on-premise AI solutions.
- Factors for Choosing a Platform
- Cloud-Based Pros and Cons
- On-Premise Pros and Cons
- Cost Implications
- Security Considerations
- Cloud and On-Premise Comparison: Brief on performance, costs, and scalability
5. Selecting Tools and Technologies
- Explore various tools and technologies to facilitate AI adoption.
- Popular AI Frameworks
- Visualization Tools
- Data Cleaning and Preprocessing Tools
- Analytical Platforms
- APIs and Libraries
- Tool Showcase: Quick tour through a suite of cutting-edge AI tools
6. Building Your Data Science and AI Teams
- Explore the key skills and roles to consider when forming a data science/AI team.
- Core Team Roles
- Required Skill Sets
- Training vs Hiring
- Collaboration Tools
- Team KPIs and Performance Metrics
- Virtual Team Management: Leveraging tech for remote collaboration
7. Generative AI: A New Paradigm for Business
- Understand how Generative AI can redefine content creation, design, and decision-making in business.
- What is Generative AI?
- Applications in Marketing and Design
- Generative AI in Product Development
- Ethical and Legal Concerns
- Generative AI in Business Strategy
- Auto-Generated Content: See how Generative AI can automatically produce marketing copy and design elements
8. Emerging Trends in Data & AI for Business
- Stay ahead of the curve by understanding upcoming trends that will shape the future of AI in business.
- Federated Learning and Edge AI
- AI for Sustainability and Social Good
- AI in Cybersecurity
- Reinforcement Learning in Business
- Explainable AI (XAI)
- Explore the impact of new trends on different business sectors
9. Next Steps and Action Plan
- Develop a roadmap to start or advance AI implementation in your organization.
- Setting Short-term and Long-term Goals
- Budget and Resource Allocation
- Scaling AI Projects
- Ongoing Training and Skills Update