Overview
Explore a practical framework for evaluating AI and data analytics solutions for enterprise business problems, avoiding costly missteps and identifying suitable approaches.
Syllabus
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- Introduction to AI and Data Analytics
-- Overview of AI and data analytics in the enterprise
-- Key terms and concepts
- Understanding Business Problems
-- Identifying and defining business challenges
-- Assessing the impact and importance of problems
- AI Solutions Landscape
-- Types of AI technologies
-- Common data analytics methods
- Evaluating AI and Data Analytics Solutions
-- Criteria for evaluating AI feasibility
-- Decision frameworks for AI solution selection
- Cost-Benefit Analysis
-- Estimating costs vs. expected benefits
-- Economic models for AI implementation
- Risk Management
-- Identifying potential risks and pitfalls
-- Creating risk mitigation strategies
- Case Studies
-- Real-world examples of successful AI adoption
-- Lessons from AI project failures
- Implementation Strategy
-- Steps to integrate AI solutions into business processes
-- Change management and stakeholder buy-in
- Measuring Success
-- Defining key performance indicators (KPIs)
-- Continuous evaluation and improvement metrics
- Ethical and Regulatory Considerations
-- Understanding AI ethics and privacy concerns
-- Navigating regulations and compliance
- Conclusion and Next Steps
-- Tailoring AI strategies to specific business needs
-- Resources for further AI exploration and learning
Taught by
Tags
On-Demand
