Implementing AI in Business: Strategies for a Data-Driven Enterprise

Course Duration

1 Day

Audience

Employees of federal, state and local governments; and businesses working with the government.

Prerequisites

No prerequisites required.

Course Description

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.

Learning Objectives

• 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
• Gain the skills required to build your data science, AI and machine learning teams

Course Outline

• 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
• Quick overview of businesses successfully adopting 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
• Evaluating databases suited for AI applications
• 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
• 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
• Explore various tools and technologies to facilitate AI adoption.
• Popular AI Frameworks
• Visualization Tools
• Data Cleaning and Preprocessing Tools